#matric part 1 math

[arfan039]

Urdu_10th Class Guess Paper

If you are looking for Urdu_10th Class Guess Paper, you are at right place. Here we have uploaded Urdu_10th Class Guess Paper 2023. We have uploaded Urdu Assessment papers for all important questions, chapters, etc. These questions most often come in BISE Exams. Urdu_10th Class Guess Paper, are equally useful for all boards of Punjab, including Faisalabad, Lahore, Gujranwala, Sahiwal, Rawalpindi,…

[shankhachil]

Countdown to JEE (Main): Week 1/33

Yep, I'm doing it. Forgive my attempt at aesthetics (but if you like it, then don't).

I've been a bit less productive than usual this week, mostly because I've been spending far too much time on Discord — should really cut down on that — and also staying up late, which has a terrible impact on my daily activity level.

But, well, it's going.

Targets: 3 topics of each subject covered per week; 400 questions solved per week, at least 80 of each subject

Topics covered:

Physics: Potential and Capacitance; Motion in One and Two Dimensions (2/3)

Chemistry: Halogen Derivatives; Alcohols, Phenols, and Ethers; Chemical Thermodynamics and Thermochemistry (3/3)

Mathematics: Definite Integrals; Area Under a Curve; Matrices (3/3)

Looks like I'll need to work on Physics, then. I'm a bit behind with respect to Allen, but my physics tuition is helping me along in that regard so no need to worry.

Questions solved:

Physics: - Allen Electrostatics module, JEE (Main) archives — 34 questions, 30 correct - Tuition Potential and Capacitance module — 192 questions, 172 correct - Tuition Vectors and Motion in Two Dimensions module — 108 questions, 102 correct Total: 334/80 questions, 304 correct

Chemistry: - Allen Thermodynamics (Part 2) module, O1, O2 and JEE (Main) archives — 82 questions, 76 correct - Himanshu Pandey, Alcohols, Phenols, and Ethers chapter — 110 questions, 93 correct Total: 192/80 questions, 169 correct

Mathematics: - Allen Definite Integrals module, O1, O2, O3 and O4 — 75 questions, 61 correct - Allen Area Under a Curve module, O1 — 30 questions, 23 correct Total: 105/80 questions, 84 correct

GRAND TOTAL: 631/400 questions, 557 correct

I've been having some trouble with calculus, as you can see. I'll need to work on that as we go. Nothing to do but practise.

Upcoming tests:

07/05/24 (Friday) — test at physics tuition center. Topics: Motion in One and Two Dimensions; Units and Dimensions; Potential and Capacitance; Chemical Thermodynamics and Thermochemistry; Alcohols, Phenols, and Ethers.

09/05/24 (Sunday) — Aryabhatta National Maths Competition

That's all for this week.

[effortentries]

Chicken tikka masala and lil bits about my life story so far.

The first recipe i have decided to attempt is Chicken tikka masala. I got the recipe from tiktok and i can say that it tastes extremely delicious. My dad even said that he would pay me to make it again for him.

For all of the recipes i’m going to add the recipe, the method i used to make the food and also a little bit about my life.

So let’s start this journey

Bismillah

Chicken tikka masala (tiktok version)

ingredients

2 Chicken breasts, cubed

Chicken marination:

1 Tbsp paprika

1 tsp cumin

1 tsp coriander

1 tsp garam masala

1 tsp red chili powder

1/2 tsp tumeric

1/2 tsp salt

2 large spoons yogurt

1/2 tsp garlic ginger paste

the juice of half a lemon

Set aside overnight or for a minimum of 4 hours.

Half an onion, finely chopped

Method

Cook chicken on medium heat in oil

Remove chicken from the pan and add it to a different bowl to rest while the sauce is being made.

Add onions to the pan

Add 3 large spoons of tomato paste

Add half a cup of water

Add one cup of cream

Stir the above ingredients together

Add the spices

1tsp of red chili

1 tbsp of paprika

1tsp of cumin

1 tsp of coriander

1 tsp of garam masala

1/2 tsp of black pepper

1/2 tsp tumeric

Cook all in medium heat until the mixture bubbles and the spices stop being raw.

Add the chicken back in and put the pan on low heat and incorporate everything together.

Garnish with dhanya and enjoy :)

And here begins my first story.

I was raised in a loving home. My most beautiful memories are with my mom, she’d do my hair into all different kinds of hairstyles when i was little, they always had to have cute little “goggles” as she’d call them at the ends of them. She’d plait my hair every day. And put little plastic flowers, or whatever pretty looking thing at the end of them.

I grew up Christian, my parents used to regularly attend an Afrikaans church close to tygerberg high school, and if they didn’t, then gospel songs would ring out every sunday from the TV and little me knew all the words.

Primary school, i kept the nice hairstyles until my mom said i was old enough to do my hair myself. Endless alice bands. I was obsessed with alice bands. I was extremely academically inclined, i was third in the grade in grade 5, grade 6 i was seventh in the grade and grade 7 i was fifth in the grade. I was a monitor for the primary school passages, patrolling all around to tell naughty little boys to tuck their shirts in and to walk in a single file line. I was in the representative council of learners where i’d run meetings, patrol every day during breaks around the school while hoping that all i was doing was enough for me to get into Rhenish girls high. It was.

I accidentally forgot a little part, well…not so little, In grade R i did ballet, then i decided to do the 360° switch to karate after i watched the karate kid for the first time. I carried on with karate throughout primary school.

Now, Rhenish girls high.

Driving to stellenbosch from brackenfell everyday was a challenge, tiring bus rides, but i knew that the education that i’d get at that school would be worth it all. And it was.

I got my black belt when i was in grade 9, i ended up quitting karate because balancing school, and it ended up being a challenge as my sensei wanted me to teach some classes to little children as well.

Grade 10 i dropped pure math, i begged my dad to do that, he looked like he wanted to cry but the decision was worth it in the end. Grade 10 was also when i went with raadiah to her Islamic society meetings, just as an observer.

Grade 11. A busy year, i was captain of 2nd team soccer. i was elected into the matric dance committee. That’s when my journey with graphic design began. I attended more islamic society meetings. I helped raadiah with hijab day, when i put the scarf on my head for the first time i somehow knew where my life would end up. End of grade 11 i was elected into the learner council.

Grade 12 i was…extremely busy? too busy? I was the school’s designated graphic designer, i converted to islam in january of that year so i was still learning a lot, I studied hard. I ended up with 6 distinctions Alhamdulilah. I had full academic colors, and full colors for culture since i did choir for all 5 years of high school

January of 2023 i was left to decide on UCT or Stellenbosch.

UCT offered me a scholarship for Bachelor of social sciences, i was also accepted for LLB but no scholarship for that.

Stellenbosch accepted me for BA law (my dream course), and seemingly didn’t give me a scholarship (they did, but their systems were so bad they only sent the money in may).

I picked stellenbosch, it was home.

Anyways, the food tasted amazing, my parents keep on asking it to make it again.

Maybe aunty can teach me how to make it taste better?

Next time, i’m going to speak about my revert story.

[faurdani]

Neeeerd time!!✨️ (wow it's my 10th months of grad study)

My mood is very good now since today's exam grade is better than I expected alhamdulillah.🤍 so i want to talk about... related to this course: Applied Math I. And other courses actually. I reaaally love that the grad courses connect each other very well!

Actually, this starts from an uneasy feeling I felt in my undergrad. There was a lot of things taught in my undergrad, but I don't understand most of them. And I am very serious. I felt my undergrad is very weird, at least for me. It was so difficult to understand things. I think I did my exams very bad. But the worse thing (to me), is, we did not get any feedback unless the semester is finished.

I hated it because I did not know whether my understanding is correct or not. I didn't know which part is true and which part is not. Whether I did good or bad in midterm, quiz, finals, or homework. I didn't really read the books. I only read Calculus, some Atomic Physics, Feynman for optics & electromagnetics, and books + papers for thesis. Oh, also some parts of book for Sensors, Actuators, Electrical Circuit. The rest.. I just read the prof's ppt/modules. Hahaha ok I admit that I wasn't that "good" in studying.

So I was so lost in Chemistry, Thermodynamics, Heat & Mass Transports, Process Techniques, & Fluid Mechanics. I also felt lost in Linear Algebra, Calculus, Differential Equation, Electronics, Signal Processing, Acoustics, System Dynamics, Controls... (really, I am actually bad... i don't understand how did they grade, why do i still get good grades when I actually don't understand things..)

I might be able to solve the problems from Linear Algebra, Differential Equations, and System Dynamic class back then.

But I didn't understand things. I just followed the pattern, nothing sticked, nothing feels connected.

That's why I was so struggled in the last years of my undergrad: when I faced Instrumentation System Design and Undergrad Thesis--which was about acoustics, mechanics, PDEs.

Many concepts were fixed and got better during the thesis, but I still felt something is missing.

When I first came here (my grad campus), we were tested on 3 subjects, undergrad level: (1) math, (2) physics, (3) chemistry.

Basically that. I passed physics & (surprisingly) chemistry, but failed math, so I should take undergrad level math in my first semester. But I am so happy!

Maybe it's also because I already finished undergrad (so I have some basics, even though it's not strong), maybe the prof is just very good at teaching (the attitude is very great. She is always punctual, effective, and willing to answer every questions. Very helpful). Maybe the ppt, the reference book, the homeworks & quiz are good. Maybe the syllabus, and the curriculum--because at the same time I need to practice a lot of calculus at Fluid Mechanics class. Maybe also the language? I am not so good in English, but now if I think about it... i feel that Indonesian math terms are not intuitive. :") Afterall, maybe it's the combination of that. I love it alhamdulillah. :')

Sooo. In my grad study, I've been taking:

- undergrad math: derivatives, integrals, ODE, series & sequence

- fluid mech: (1) a bit of surface tension, dimensionless analysis, Reynold Transport Theorem, Navier Stokes, vorticity, ideal flow, gravity waves, laminar flow; (2) boundary layer, instability, turbulence, compressible flow (shockwaves); (math) kronecker delta, levi-civita, a looot of PDE & calculus because Navier-Stokes, diff coordinates (cartesian, polar), slight complex for waves & instability discussion

- thermo: 1st, 2nd, 3rd law of thermo; (math) basically PDE *note: i'm still very bad at this*

- solid mech: statics, kinematics, constitutive equations (relating statics & kinematics--basically how to solve/model the solid mechanics phenomenon? --> including elasticity, optimization problem), crack propagation; (math) tensor calculus (dyadic notation, matrices, linear algebra), ODEs, Gauss theorem, diff coordinates (cartesian, cylindrical, polar)

- Applied Math II: complex analysis (complex algebra, derivative, integration, Laurent series) & linear algebra (vectors, dimensions, rank, linear dependency, eigenvalues, eigenvectors. Basically how to solve linear "discrete" system)

- Applied Math I: ODE, PDE. Including Green's Function (transfer function) & Sturm Liouville's problem (eigenvalues, eigenfunctions, linear dependency. How to solve linear "continuous" system)

*phew* that's a lot for one year.

But as you can see... every physics class has some math (generally: calculus, ODE, PDE, linear algebra). It helps me to understand math and math helps me to understand that!

If i remember again, my undergrad has a lot of math also (mainly solving ODE, PDE)..

So that now I'm taking Applied Math I, especially during the Green's function discussion, it blooows my mind!

I rmb very clearly that in undergrad we discussed transfer function *a lot*. It is output per input. But how do we find it?

I didn't understand the math.

In Controls, Acoustics, Instrumentation System Design classes, even for my undergrad thesis, we discussed about harmonic oscillator system.

But i didn't really understand what solution is. Why do we solve that way. Why the transfer function and the solution is written that way.

Now I want to share the main insight from current course. In short (I hope), when we have ODE:

Ly(x) = f(x); L is linear differential operator (e.g. D² + pD + q),

f is the input and y is the output

We can solve this, get a solution of

y(x) = int[ G(x,t)*f(t) ]dt

Such that G, the Green's function is equivalent to output over input; the transfer function. And we practice--G only depends on L and Boundary Conditions!

Moreover,

Ly(x) = f(x) is actually equivalent to

LG(x,t) = delta(x-t); delta(x-t) is impulse at x=t, means we'll get transfer function if we give impulse as the input! (I knew this "physically" but math understanding is ✨️amazing✨️)

And thenn I also just notice (or maybe remember, but now understand by heart, I hope) that impulse is a derivative of step, which is a derivative of smooth piecewise function.

And smooth piecewise function can actually be written as a series. Fourier series.

And Fourier series can also be seen as a linear combination of 3 orthogonal bases: 1, sin(n*pi*x/L), cos(n*pi*x/L). Note that we can imagine 3 orthogonal bases as x,y,z in Cartesian coordinates..

Honestly the concept of relating vectors (matrix, "discrete") with functions ("continuous") amazes me in the beginning of this course. 🥺🥺✨️

[rashlin3110]

The Journey to Understanding Data Science

Data science is a fascinating and ever-changing field that helps us make sense of data. In this blog, we'll break down the core areas of study in data science using easy-to-understand language, helping you grasp the basics of this captivating discipline.

1. Mathematics: At the heart of data science is mathematics. Data scientists study statistics, linear algebra, calculus, and probability theory. These math concepts help them find patterns in data, make predictions, and check if their findings are reliable.

Statistics: Data scientists use statistics to understand and analyze data. They look at things like averages, differences, and testing ideas to draw sensible conclusions from data.

Linear Algebra: Linear algebra helps data scientists work with data in an organized way. It uses matrices and vectors for jobs like simplifying data and creating machine learning models.

Calculus: Calculus helps data scientists understand how data changes over time. It's especially useful for making machine learning models and programs work better.

2. Programming: Data scientists study programming languages like Python and R and tools like SQL. These languages help them work efficiently with data.

Python: Python is one of the most common programming languages in data science. It's used to write code for data analysis, making graphs, and creating machine learning models.

SQL (Structured Query Language): Learning SQL is a must for getting data from big datasets. It's all about asking questions and getting answers from databases.

3. Data Cleaning and Preprocessing: A big part of a data scientist's job is making data clean and ready for use. This means fixing things like missing numbers, taking out weird stuff, and making sure the data makes sense.

4. Machine Learning and Data Mining: Data scientists study machine learning and data mining to make models that predict things and find patterns in data.

Supervised Learning: In supervised learning, data scientists use labeled data to make predictions. They can predict things like house prices, what customers will do, or if someone is sick.

Unsupervised Learning: Unsupervised learning helps data scientists find patterns in data without labels. This includes grouping similar things together and making data easier to understand.

5. Domain Knowledge: Domain knowledge means understanding the field you're working in, like healthcare, finance, or marketing. Data scientists need to know the details and problems of that area to get the most value from the data.

Data science is a mix of math, programming, data cleaning, machine learning, data visualization, and knowing the field you're working in. Data scientists use these skills to explore data, find useful insights, and make smart decisions using data.

If you're interested in learning more about data science course, I'd recommend checking out ACTE Technologies. They offer certifications and help you find job opportunities. Their experienced instructors can make learning easier for you, and you can choose to attend classes either online or in person. If data science piques your interest, you might want to consider taking a course.

I hope this information helps you. If you have any more questions, feel free to ask them in the comments. I'm here to learn and provide assistance. If you found this helpful, please show your support by following me and giving me a thumbs-up. Thanks for your time and support. Have a wonderful day!

[jayhorsestar]

to 'gigi, re.'paper and employees, and how MAROMA Ltd advertizing was handling certain billboards around city limits, A1. up until 2019, WHEN the moment LAW Working from home made available for 401(k) retirement estimations. and BOLT at Latvia put BOLT at Sibiu in charge of BOLT London UK, and Sibiu ran to Brasov and said 'You do it, and ..from home. ..and IT help desk TRELLO moved out of RO, all during August-September 2021. Chrome books (notebooks), Avaya emulated, and Billing remained on RO in view of UK. and BREXIT since 2020 January. B1. E.U. is thinking enlarging w. perhaps package of recently added into NATO, bordering Russia and UKR, such as saying Finland, etc. well by 2009, RO and BG into E.U. meant certain pre-requisites had had to been met. E.U. was regarding the GDP for it be calculus averages to lots of estimations, currency been minted upon. C1. making love to 'dove, am aware, yes but no, kids should not learn all of those habits. 'dove was playing protective mom, and she hated that. so she loved me but she hated me. and i loved her, and i ignored her altogether. D1. thank you 'Lily Collins and 'Maria Isabel and 'Gigi for the recent suites of pics, past 72hrs. E1. am sure Global citizen adventure is saying about some exotic POOLS the reading online PAX so eagerly voting up against or upon. smth they needed to feel. perhaps 'Allen Coliban the Mayor now (was Senator), is counting (re.Holograf 'Money talks and Kangaroo math events) sort of those pre-requisites 3% to a 30% alike averages needed by RO to enter the E.U. upon COVID19, when he was voted Mayor. indeed not cash he be counting. F1. miss 'Taylor Hill telling indeed electronic currency brought all NYC ladies to marry foreign citizen male husbands, to avoid entering the electronic matrices of 'good behavior associations (no Private North VPN app to install there, just yet to book in air fare flights from the cheapest, not the closest available dealer), seen at BOLT drivers London 2021 August-September. G1. pusi 'Sofia Carson, am proud of your R.Kelly Our hope (the 'we said hi you said what) Part 1, Hang on Part 2, Part [silent] to be strong, old Sessions AOL (him white, not by my right - see watch?v=gd6sx7WKjCQ loved all the 'Galitzine shaking dance moves, by Pau.. m

[realtalk-princeton]

what are the best online resources to self-study for orf309? are there any online courses/resources that are similar to the material that the course teaches? (also, is there somewhere i can find a syllabus for the course?) thanks!

Response from Heisenberg:

Harvard's Stat 110 taught by Joe Blitzstein is probably the best for self-study. Find the textbook for that course here (this book covers more than ORF 309 does). The accompanying lectures are on YouTube. There's also a website with lots of problems and solutions for you to practice with. If you ask around, you might be able to get Ramon's lecture notes (which he follows very closely in lectures). They are more concise (~ 202 pages) than Blitzstein's book but they're not that easy to self-study from in my opinion (for instance, the class uses a weird "element of" dx notation for integrals), so you're still totally fine with Blitzstein if you can't find the lecture notes.

Roughly speaking, ORF 309 will cover chapters 1-5, 7.1 through 7.3, 9, 10.1 through 10.3, 11, and 13 in Blitzstein's book. I might have missed some small subsections but this should keep you plenty busy for the summer. You can skip all parts related to R programming. You should probably read the math appendix (mainly the calculus and matrices section, getting familiar with set theory notation would be good) to familiarize yourself with the math since some of the computations and integrals in that class ARE NOT TRIVIAL.

I can't remember exactly what was on the syllabus since I think it was just written on the course's Canvas home page lol, but the grade distribution is on the registrar. Try to do well on midterm 1 (it's basically just basic probability concepts from AP Stats / ORF 245) since midterm 2 and the final are hard af. Midterm 1 covers up to chapter 5 in the Stat 110 book, midterm 2 covers the other half, and the final tests both halves.

Best of luck!

[klavierpanda]

On today's episode of "Wow maths is so cool!" we have groups. The fact that seemingly unrelated things can have the same underlying structure is so cool to me!*

A group, is a set, G, with an operation, •, that satisfies the following:

If a and b belong to G, then so does a•b

There exists and identity element, e, in G such that e•a=a•e=a

There exists and inverse in G such that ā•a=a•ā=e (note here the bar isn't standard notation, I just don't know how to get superscript on Tumblr mobile)

Associativity: (a•b)•c=a•(b•c)=a•b•c

Before learning about groups, I knew that multiplying by complex numbers can be a way to represent rotations in two dimensions, which means that complex numbers can also be represented by 2 by 2 matrices. What I didn't know was there's a deep structure there: you guessed it, groups! The link between the two is that you can find a bijective mapping between them: an isomorphism!

A more specific example is the 12th roots of 1 (with multiplication) and 2D rotations by multiples of 1/12th of 360° / 2π radians are isomorphic, they behave in the same way. Why did I pick 12? Well there's another group called the cyclic group, in this case the cyclic group of order 12, which is also isomorphic to these groups!The cyclic group of order n described what happens when you add numbers modulo n. This means when I divide a whole number by n, how does it's remainder change when I add other whole numbers to it. The reason I chose 12 is because addition modulo 12 is exactly what we do when we read a clock face! 1:00 and 13:00 both read as 1 on a clock face, likewise 2:00 and 14:00 read as 2, etc. This is because when divided by 12, 1 and 13 both have a remainder of one and so on and so for. You can find an isomorphism between either of the two previous groups and the cyclic group of order 12. All seemingly unconnected parts of maths connected by some deeper structure!

*Incidentally this is why I've really enjoyed my linear algebra module. I also got hints of this in my analysis module when we compared theorems about convergence of sequences of real numbers and those about convergence of sequences of functions and how the proofs look pretty much the same. My lecturer said it's to do with something called a metric space and we'll learn about it next year!

[ashishkumarletslearn]

Full Syllabus of Class 12 Maths 

Ch01. Relations and Functions (Part – 1) Ch01. Relations and Functions (Part – 2) Ch02. Inverse Trigonometric Functions Ch03. Matrices Class 12 Maths Ch03. Matrices Class 12 Assignments Ch04. Determinants Class 12 Maths Ch04. Determinants Class 12 Assignments Ch05. Continuity and Differentiability Class 12 Maths Ch05. Continuity and Differentiability (Derivative Assignments) Class 12 Assignments Ch05. Continuity and Differentiability (Differentiability Assignment – 3) Class 12 Assignments Ch06. Applications of Derivatives Ch06. Applications of Derivatives Class 12 Assignments Ch07. Integrals Ch07. Properties of Integrals Ch08. Limit of Sum & Application of Definite Integrals Ch09. Differential Equations Ch10. Vector Algebra Class 12 Maths Ch10. Vector Algebra Class 12 Assignments Ch11. Three Dimensional Geometry Class 12 Maths Ch12. Linear Programming Problems Ch13. Probability

[bloggulf220]

Mr. Mac's Challengesmr. Mac's 6th Grade

Mr. Mac's Challengesmr. Mac's 6th Grade Language Arts

Mr. Mac's Challengesmr. Mac's 6th Grader

These seem like obvious things but just trying to help people with the learning curve.

This feature is not available right now. Please try again later.

Anker Tests Reading / Language Arts Skills. More Reading Comprehension More Spelling (10 words each) More Homophones More Analogies. More 6th General Math. By Mr Mac. Now contains Australian/British English and US English spellings. A great visual prompt for students to implement the 'Super Six' comprehension strategies when reading or viewing texts. great poster display for classrooms - text and images.

Elementary Math Skills

Mrs. Victoria Rose

This class will address 3-6th grade mathematical skills in preparation for pre-algebra.

Pre-Algebra

Mr. Mac Ogilvie 1

STARS offers a Pre-Algebra course using the Saxon Math 8/7 book as a resource. This course provides an excellent summary of the basic skills required to move into Algebra. It can be considered to be a Middle School Math course that bridges elementary school to high school math. Public schools seem to be moving some students into Algebra as early as sixth grade depending largely on the skill level of the student. But the movement into Algebra at any grade level requires the exposure to a wide range of general math knowledge and the mastery of key skills essential to the ability to be successful in Algebra. This course provides that knowledge and the ability to master those critical skills. The course includes coverage of basic geometry and probability and statistics. The STARS instructor at this time has an extensive background in teaching both middle school math and Algebra both in public schools and here at STARS.

Need: Saxon 8/7 Homeschool Kit- http://www.rainbowresource.com/product/Saxon+Math+8-7+3ED+Homeschool+KIT/024434

Mr. Mac's Challengesmr. Mac's 6th Grade Language Arts

Algebra I

Mr. Mac Ogilvie

This course will use the Saxon book, Algebra 1, to provide a comprehensive teaching of the fundamental aspects of problem solving. It offers a substantial review of pre algebra fundamentals while also offering coverage of area, volume, and perimeter of geometric figures. Major topics include evaluation of algebraic equations, thorough coverage of exponents, polynomials, solving and graphing linear equations, complex fractions, solving systems of equations, radicals, word problems, solving and graphing quadratic equations, solving systems of equations, and solving equations by factoring.

Textbook: Saxon Algebra 1 Homeschool Kit http://www.rainbowresource.com/product/sku/000628

Algebra II

Mr. Mac Ogilvie

This course will use the Saxon book, Algebra 2, to provide a comprehensive teaching of the fundamental aspects of problem solving. It offers a substantial review of all topics in Algebra 1 and then moves on to cover these topics at an advanced level. Major topics include the solving and graphing of linear and quadratic equations, factoring, a variety of types of word problems, solving quadratic equations by completing the square, solving simultaneous equations with fractions and decimals, complex roots of quadratic equations, solving systems of nonlinear equations, graphing and solving a system of inequalities, exponential equations, and review of key geometry, probability and statistics topics.

Textbook: Saxon Algebra 2: Homeschool Kit Third Edition

Jacob’s Geometry

Ms. Enjoli Stith

3rd Edition. This is an excellent geometry course with clear explanations of geometric concepts, including plenty of practice with proofs (informal and paragraph). The second chapter (six lessons) is devoted to logic in preparation for constructing proofs. Topics build incrementally and each practice set assumes knowledge gained in previous lessons in order to construct proofs. The author has set his text up to include three sets of problems with each lesson so as to present the basic concepts in Set I exercises, applications in Set II exercises, and extension of concepts in Set III exercises. Finally, there are Algebra reviews located at the end of most chapters in the student textbook. An appendix contains all presented theorems and postulates. After a thorough study of Euclidean geometry, a single chapter of four lessons presents non-Euclidean geometries. SAT math problems have also been included in exercise sets. The teacher’s guide contains lesson plans, black line masters, and answers to all exercises. The test bank contains two tests for each chapter, a mid-term, a final, and solutions.

Textbook: Geometry: Seeing, Doing, Understanding, 3rd Edition

Advanced Math (Pre-Calculus and Trigonometry)

Ms. Enjoli Stith

Advanced Mathematics fully integrates topics for algebra, geometry, trigonometry, discrete mathematics, and mathematical analysis, to include trigonometric equations and inequalities, the unit circle and trigonometric identities, conic sections, logarithms and exponents, probability and statistics, complex numbers, functions and graphs, sequences and series, and matrices. Graphing calculator applications are developed to facilitate calculations and enhance in-depth understanding of concepts. Word problems are developed throughout the problem sets and become progressively more elaborate. With this practice, high-school level students will be able to solve challenging problems such as rate problems and work problems involving abstract quantities. Conceptually oriented problems that help prepare students for college entrance exams (such as the ACT and SAT) are included in the problem sets. Students complete 2-3 lessons per week.

Mr. Mac's Challengesmr. Mac's 6th Grader

Note: This is a two year class. Part 1 covers lessons 1-66, Part 2 covers lessons 67-125.

[lawyeruse30-blog]

7 Easy Ways To Be An Efficient Software Application Tester

7 Easy Ways To Be A Reliable Software Application Tester

#toc background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px; .toctitle font-weight: 700;text-align: center;

Content

ÄHnliche JobsuchezurüCkweiter.

Easy Ways To Be A Reliable Software Program Tester.

A Want To The Future Of Software Testing.

Interface Designer.

Top 10 Software Program Testing Courses.

In addition I think designers that unit test can much better comprehend what QA is discovering. CIOs are already exploring making use of no-code as well as reduced advancement tools to accelerate software program advancement. Consequently, the low-code/no-code motion presents additional subtleties when testing software application, with more examination cases and also broader protection to match this larger landscape.

Is software testing a dead end job?

You can join any 4 months duration Software Testing course or can do a diploma in Software Testing which is probably 6 months to 1 year. Keep the preparations going on during your course.

Handbook Testing is a process of discovering the flaws, bugs in a software application. A tester do end individual role and validates if all the functions are working appropriately or otherwise. Hands-on testing is the process of making use of the features of an application as an end-user. With manual testing, a tester manually conducts examinations on the software application.

Testing makes sure premier user experience by eliminating weak points in software growth. To be able to construct top notch scalable software program, one needs to believe like a software tester. Testing is a pretty important part of the software advancement life cycle. So you should not be thinking of testers as people who live to bother you with insects, but instead as a group friend that assists you deliver top quality software program.

What is the duration of software testing course?

As quoted by Michael Bolton, “Testing is a continuous learning process by exploring, discovering and investigating the information you have”. If you are passionate about software testing then you will be less likely to find this as a boring job. At some point, testing can be a monotonous work.

ÄHnliche JobsuchezurüCkweiter.

More software is being produced, with a reduced barrier to access, and faster time to market. So, fast responses, supported by clever test automation systems, is mosting likely to be vital.

Easy Ways To Be An Efficient Software Program Tester.

After these actions are completed, a designer refactors the code to pass all the tests. This post will discuss the very best methods of exactly how to improve software application testing process and for enhancing the quality of your software products. Software quality control is the component of high quality management that consists of a planned collection of organizational actions. The objective of these actions is to improve the software application growth process, presenting requirements of quality for protecting against mistakes and insects in the item. This will likewise call for testers who have excellent shows skills.

Handbook Testing is a fundamental kind of testing in the application under test. In one of the job meetings that occurred for the role of a software program tester, we discovered a prospect with previous experience in software application advancement just and no testing experience. The candidate was overconfident about fracturing the interview because of the assumption that testing is a second-tier work contrasted to growth. Nowadays a lot of the job openings need you to have some accreditations in testing.

End-user testing or user-acceptance testing traditionally comes with the final stages of software development.

And also if the programmers do not think testing is an actual task, or that it's easy, have them create the examination matrices, carry out thousands of test cases, document every single outcome and every solitary action.

At Cigniti, testers are our Stars and also a few of them are better paid than programmers in other firms.

Java tester opleiding are paid in addition to software program programmers.

An Aim To The Future Of Software Application Testing.

You will certainly learn exactly how to effectively intend, schedule, estimate as well as document a software application testing strategy. The lessons will additionally show you exactly how to examine metrics to enhance software program quality and software tests. This program likewise discusses software program quality efforts established by market specialists. Software application requires to be checked for insects and to make certain the item fulfills the needs and creates the wanted results.

User Interface Designer.

Cigniti likewise hires developers that have a passion for testing and also deploys them in locations that need a higher degree of programming abilities. Time stress-- Developers obtain postponed and also cut into the moment permitted testing. This puts pressure on testers given that they remain in the last phase of software advancement life process. Throughout java tester of the release, it is not uncommon for testers to place in additional hours as well as weekends trying to meet target dates. Taking care of temperamental programmers-- Considering that testers have the tough job of calling programmer's "developments" hideous, they need to discover to deal with the reaction when dealing with temperamental programmers.

With this software testing oriented web site, the individual community can aid you to stay up to day on the testing patterns, training, as well as conferences. And also if you are having issues with testing projects, the pro area of testers on the web site will certainly support you obtain the option in the Q&An area.

Additionally I believe too that system testing ought to be a needed component of software program growth different from QA. So as QA finds bugs, the system tests are altered to evaluate for that.

As a Software application Tester, you would certainly be investing most of the time trying to "break" the software program. You need to have superb aptitude & analytical skills paired with knowledge of testing methodologies as well as devices. The job interviewer will definitely ensure that you have all the standard needed knowledge of Software program Testing as well as have the necessary abilities to do the job. In this course, you will certainly learn more about the administration aspects of software application testing.

How do I train to be a software tester?

Many employers look for software tester candidates with a bachelor's degree in computer science, math or engineering, although it's not always required. If you've got a lot of experience, a stable work history and solid references or letters of recommendation, it's possible to land a job without a college degree.

While they are typically still the unrecognized heroes, the work that QA specialists do is significantly acknowledged for its payments to DevOps. At the same time, testing will continue to become endemic throughout other components of the software lifecycle process, with fast-evolving tools bringing tests within the reach of much more employee. You will certainly obtain advice on real-world software testing, QA, management, examination automation, and also advancement proficiency in a broad series of software application growth firms ranging from small to large range. Sticky Minds is in collaboration with the Techwell area.

What is manual testing example?

Watts S. Humphrey (July 4, 1927 – October 28, 2010) was an American pioneer in software engineering who was called the "father of software quality."

[easy-as-01-10-11]

Little things about Maths+CompSci people

0. 0 is part of N fight me

1. Indenting proofs

2. Small obvious steps, very precise

3. EDGE CASES

4. Recognisable variable names even if the proof ends up 4 pages long

5. Everything is Boolean Algebra

6. BINARY

7. Matrices everywhere

8. Brute force proof

Add more in the tags if you like

[realtalk-tj]

Could you please explain in more detail what each of the math post-APs are and how easy/hard they are and how much work? Thanks!!

Response from Al:

This can be added on to, but I can describe how Multivariable Calculus is. First off, I want to say not anyone’s opinion should affect how difficult or easy a class would be for YOU. Ultimately, do the classes you’re interested in. Personally, I thought Calculus was cool as subject, so that’s why I pursued Multi. Multi. builds off of BC Calculus, Geometry, and even some of the linear algebra you learned from middle school (not to be confused with the Linear Algebra you can take at TJ), so as long as you have a good foundation in those subjects, I’m sure you’ll do well in Multi. Depending on your teacher, assessments may or may not be more challenging, and that’s why I strongly emphasize take the class only if you’re genuinely into it. Don’t take it because of peer pressure / because you want to stand out in colleges. I’ll let anyone add below.

Response from Flitwick:

Disclaimer: I feel like I’m not the most unbiased perspective on the difficulty of these math classes, and I have my own mathematical strong/weak points that will bleed into these descriptions. Take all of this with a grain of salt, and go to the curriculum fair for the classes you’re interested in! I’ve tried to make this not just what’s in the catalog/what you’ll hear at the curriculum fair, so hopefully, you can get a more complete view of what you’re in for. 

Here’s my complete review of the post-AP math classes, and my experience while in the class/what I’ve heard from others who have taken the class. I’m not attaching a numerical scale for you to definitively rank these according to difficulty because that would be a drastic oversimplification of what the class is.

Multi: Your experience will vary based on the teacher, but you’ll experience the most natural continuation of calculus no matter who you get. In general, the material is mostly standardized (and you can find it online), but Osborne will do a bit more of a rigorous treatment and will present concepts in an order that “tells a more complete story,” so to speak. 

The class feels a decent amount like BC at first, but the difficulty ramps up over time and you might have an even rougher time if you haven’t had a physics course yet when it comes to understanding some of the later parts of the course (vector fields and flux and all).

I’d say some of the things you learn can be seen as more procedural, i.e. you’ll get lots of problems in the style of “find/compute blah,” and it’s really easy to just memorize steps for specific kinds of problems. However, I would highly recommend that you don’t fall into this sort of mindset and understand what you’re doing, why you’re doing it, and how that’ll yield what you want to compute, etc.

Homework isn’t really checked, but you just gotta do it – practice makes better in this class.

Linear: This class is called “Matrix Algebra” in the catalog, but I find that title sort of misleading. Again, your experience will depend on who you get (see above for notes on that), but generally, expect a class that is much more focused on understanding intuitive concepts that you might have learned in Math 4/prior to this course, but that can be applied in a much broader context. You’ll start with a fairly simple question (i.e. what does it mean for a system of linear equations to have a solution?) and extend this question to ask/answer questions about linear transformations, vectors and the spaces in which they reside, and matrices.

A lot of the concepts/abstractions are probably easier to grasp for people who didn’t do as well in multi, and this I think is a perfectly natural thing! Linear concepts also lend themselves pretty well to visualization which is great for us visual learners too :)) The difficulty can come in understanding what terms mean/imply and what they don’t mean/imply, which turns into a lot of true/false at some points, and in the naturally large amount of arithmetic that just comes with dealing with matrices and stuff. 

Same/similar notes on the homework situation as in Multi.

Concrete: Dr. White teaches this course, and it’s a great time! The course description in the catalog isn’t totally accurate - most of the focus of the first two main units are generally about counting things, and some of the stuff mentioned in the catalog (Catalan numbers, Stirling numbers) are presented as numbers that count stuff in different situations. The first unit focuses on a more constructive approach to counting, and it can be really hard to get used to that way of thinking - it’s sorta like math-competition problems, to a degree. The second unit does the same thing but from a more computational/analytic perspective. Towards the end, Mr. White will sort of cover whatever the class is interested in - we did a bit of group theory for counting at the end when I took it. 

The workload is fairly light - a couple problem sets here and there to do, and a few tests, but nothing super regular. Classes are sometimes proofs, sometimes working on a problem in groups to get a feel for the style of thinking necessary for the class. if you’re responsible for taking notes for the class, you get a little bonus, but of course, it’s more work to learn/write in LaTeX. Assessments are more application, I guess - problems designed to show you’ve understood how to think in a combinatorial way. 

Unfortunately, this course is not offered this year but hopefully it will be next year! 

Prob Theory: Dr. White teaches this course this year, and the course’s focus is sort of in the name. The course covers probability and random variables, different kinds of distributions, sampling, expected value, decision theory, and some of the underlying math that forms the basis for statistics. 

This course has much more structure, and they follow the textbook closely, supplemented by packets of problems. Like Concrete, lecture in class is more derivation/proof-based, and practice is done with the packets. Assessments are the same way as above. Personally, I feel this class is a bit more difficult/less intuitive compared to Concrete, but I haven’t taken it at the time of writing. 

Edit (Spr. 2020) - It’s maybe a little more computational in terms of how it’s more difficult? There’s a lot of practice with a smaller set of concepts, but with a lot of applications. 

AMT: Dr. Osborne teaches this course, and I think this course complements all the stuff you do math/physics-wise really well, even if you don’t take any of the above except multi. The class starts where BC ended (sequences + series), but it quickly transitions to using series to evaluate integrals. The second unit does a bit of the probability as well (and probability theory), but it’s quickly used as a gateway into thermodynamics, a physics topic not covered in any other class. The class ends with a very fast speed-run of the linear course (with one or two extra topics thrown in here and there). 

The difficulty of this course comes from pace. The problem sets can get pretty long (with one every 1-2 weeks), but if you work at it and ask questions in class/through email whenever you get confused, you’ll be able to keep up with the material. The expressions you’ll have to work with might be intimidating sometimes, but Osborne presents a particular way of thinking that helps you get over that fear - which is nice! All assessments are take-home (with rules), and are written in the same style as problem sets and problems you do in class. The course can be a lot to handle, but if you stick with it, you’ll end up learning a lot that you might not have learned otherwise, all wrapped up in one semester.  

Diffie: Dr. Osborne has historically taught this course, but this year’s been weird - Dr. J is teaching a section in the spring, while Dr. Osborne is teaching one in the fall. No idea if this trend will continue! Diffie is sort of what it says it is - it’s a class that focuses on solving differential equations with methods you can do by hand. Most of the class is “learning xx method to solve this kind of equation that comes up a lot,” and the things you have to solve get progressively more difficult/complex over the course of the semester, although the methods may vary in difficulty. 

I think this is a pretty cool class, but like multi, the course can be sort of procedural. In particular, it can be challenging because it often invokes linear concepts to explain why a particular method works it does, but those lines of argument are often the most elegant. This class can also get pretty heavy on the computational side, which can be an issue. 

Homework is mostly based in the textbook, and peter out in frequency as the semester progresses (although their length doesn’t really change/increases a little?). Overall, this is a “straightforward” course in the sense that there’s not as much nuance as some of these other classes, as the focus is generally on solving these problems/why they can be solved that way/when you can expect to find solutions, but that’s not to say it’s not hard. 

Complex: I get really excited when talking about this class, but this is a very difficult one. Dr. Osborne has historically taught this course in the fall. This class is focused on how functions in the complex numbers work, and extending the notions of real-line calculus to them. In particular, as a result of this exploration, you’ll end up with a lot of surprising results that can be applied in a variety of ways, including the evaluation of integrals and sums in unconventional ways. 

In some ways, this class can feel like multi/BC, but with a much higher focus on proofs and why things work the way they do because some of the biggest results you’ll get in the complex numbers will have no relation whatsoever to stuff in BC. Everything is built ground-up, and it can be really easy to be confused by the nuanced details. If you don’t remember anything about complex numbers, fear not! The class has an extra-long first unit for that very purpose, which is disproportionately long compared to the other units (especially the second, which takes twoish weeks, tops). Homework is mostly textbook-based, but there are a couple of worksheets in there (including the infamous Real Integral Sheet :o) 

This course is up there for one of the most rewarding classes I’ve taken at TJ, but it’s a wild ride and you really have to know what things mean and where the nuances are cold. 

[written-in-starlight]

So it’s finally the end of the quarter...

So you know what that means!

Time to collect all the weirdass shit my composites prof has said over the course of 10 weeks and share it with ya!

These two were basically back to back:

“If you’re wondering what the science behind this equation is, yeah, it’s pretty arbitrary. We just curve fit the data to get it. No science."

“So the units are something like MPa*sqrt(mm). Science!”

“This drove me crazy while I was learning it and it still kind of drives me crazy now, but I have a little bit of Stockholm Syndrome by now.”

“Now for another irritating thing. We have matrices S and C, which are the stiffness and compliance matrices. Guess which one is which!”

“Back, lost in the land of Oz.”

*Very sarcastically* “And here’s eta. Totally intuitive.” (it is truly a disgusting equation we never used lol)

“Does air weigh anything? No. Don’t tell a thermodynamicist I said that.”

“Tell me the volume of the fibers.” Some kid: “No!”

*Looks around with dead marker in hand* “There’s not even a trashcan here. *pause* Here’s a trashcan.” *tosses dead marker in corner. Continues to do this all quarter*

“What we’ll do tomorrow, *realizes tomorrow is Saturday* no, not tomorrow, Monday.” Later: “So that’s what we’ll be doing tomorrow *pause* wait.” End of class: “We’ll pick up on Monday! Got it right that time!” This also happened basically all quarter lol

Also related: “Where we left off yesterday. No, Friday!”

“I say linear algebra and see you all cringe”

“I can see by looking most people are like ‘yup, don’t care!’“

“So I’m dealing with carbon fibers, so I’m all covered up, but it’s a hot day out, so like a dumbass I roll up my sleeves.” 

For clarification, carbon fiber composites are real fuckin brittle, so when they snap (or get cut like my prof was doing), there’s gonna be splinters. Lil tiny splinters = bad rash if you touch them. 

“What’s cos(45)*sin(45)?” *crickets* “... It’s 1/2″

“What’s cos^2(45)-sin^2(45)?” *more crickets* “They’re the same thing...”

We go to a math and engineering school. I feel like my prof was just sad for us that day.

At some point he made a joke about math majors (that for the life of me I can’t remember and I couldn’t write it down like the rest of these quotes), and then went “It’s okay I can make fun of math majors because I was a math major.” 

*talking about the test* “At most I’d ask you to multiply a 3x3 matrix by a 3x1 vector... I’d hope you can do that by hand.”

“It’s really easy to just look at this like *cringes*”

“That’s a whole ‘nother can of worms. I got a PhD just on that.”

*projector turns off* “Don’t turn off, I’m still here!” 

“I’m stalling while we’re waiting for the projector to turn back on.”

“They end up being fun, sarcastic, equations.”

“For people who don’t know space, it’s hot as hell on the moon!”

“I won’t blame you if you don’t remember, since I forgot to mention it.”

*starts spinning around talking about theta*

“This sounds like spherical coordinates. Everybody loves spherical coordinates.” 

“So what now?”  Some kid: “Give up and go home?”

“When I asked for this data, the manufacturers basically sent back the shrug emoji.”

“It seems like I’m just rearranging deck chairs on the Titanic.”

“So I’m going to plug this fun equation into this summation of integrals of derivatives-” Entire class: “Oh no.”

“See, it’s like the calculus problem from Hell.” 

“Now we’re going to make 10 assumptions because we’re engineers.”

“It’s mechanics. We idealize everything!”

And finally: “I’ve been working with composites for 8 years now and I’d say I know about nothing.” 

Our class was also majorly cursed when it came to our projects. 

So the project was supposed to be relatively simple. Make a composite out of carbon fibers and some epoxy, wait for it to dry, get measurements n shit, prof will take the composites and cut them down to proper size, then we get to go break them on fancy machines.

But literally everything that could have gone wrong did go wrong.

Okay to be fair the actual making of the composites was fine. So was the measuring bit.

But then prof comes into class on Monday. He said he was gonna cut the composites over the weekend. And he says, “So, remember how I said you need a very specific bit to cut these composites? Yeah guess what just broke!”

So we wait while he gets a new one. And he comes in the Monday after getting the new one. “So. I got the new bit. And guess what also broke on me!”  Entire class: “Oh no.”  “But it’s fine because I was literally on the last cut when it broke, so I just kind of snapped off the rest. So we’re good to go on testing!”

So we go down to the lab and put a composite into the tester and how it’s supposed to work is that the grips eventually latch onto the sample and then pull it till it breaks or the tester reaches 10000 lbs of force. Well. The grips weren’t latching on. So the end of the period comes and our prof is just like “Welp. I’m gonna mess around with these for a bit and see if I can get them to work.” 

He sends us an email later that basically says, “I figured it out. Turns out the grips were failing, so I ordered a replacement part. But we’re gonna have to delay testing again.”

Eventually the new grips come in and he spends time down in the lab trying to fix the tester and talking to us about how it’s going in class, and finally he comes into class and says, “So, I figured out why I couldn’t get the grips to fit. We were sent the wrong ones!”

So by this point he’s ready to give up and move to the weaker tester (only goes up to 5000 lbs, probably wouldn’t break any of our samples (the 10k tester didn’t break mine so ye)), except. Apparently the lower level materials class had somehow broken that one too.

But my prof did some sort of black magic and got the 10k tester to work again, so we eventually got to go break our shit (except not for mine lol). At least I wasn’t the group that was still cursed because the grips couldn’t latch onto their sample lol

So yep. My composites class was weird and cursed as shit lol

[damdahdi-studies]

Random update

I havent been posting for a while, so here we go

why I havent been posting: I dont really have my shit together at the moment. to be frank I didnt want to post that shit you know, wanting to present a real nice version of yourself on social media

But thats not why I created this blog, I created this account to help me achieve more. And a part of that is being held accountable for my own mistakes, admitting them and trying to solve them. So what shit do i exactly dont have togehter right now?

physics - Im a bit behind in linear motion. i spent a lot of time doing it today, so estimate 2 hours needed to catch up completely. however if I really want to get more practise in, i need to do more practise problems from the textbooks other than the school one.

chemistry: i have a set of problems to finish that would take approximately 1 hour to do. there is also an assessment coming up this week that i have to research for. ive started, done 60% ish of it. estimated hours needed for the research part approx 2 hours

Literature - i have to start taking notes on my own on the whole book in general. also clean up notes taken in class. start preparing for that short and long essay coming up in 2 weeks. more on that later

mathematics - on both maths Im caught up & ahead. however i do want to take hand written notes on vector proofs etc for test next week. i also want to study ahead a little bit, especislly for calculus and matrices.

chinese - first i really need to study the vocabulary learnt in class and start writing practise with those. for the upcoming exam, i want to assign a week to all 4 topics learnt so far to revise. a test is coming up in 2 weeks that I need the vocab for.

so you see, im in bit of a mess. its 3am right now and i need to wake up early to start my chem research.

as of tomorrow, i need to prioritise chemistry, then physics as there are tests for that this week.

i kinda wound up in this shit state because i was really struggling during the term break to ... not suffer from anxiety and depression so I didnt end up doing much to prepare for the term.

i actually dont know at this point. ill try my best, and im very desperate, but it will be difficult. wish me luck

(sorry for shit posting its 3:05am fuckkkk)

[mahiworld-blog1]

Important libraries for data science and Machine learning.

Python has more than 137,000 libraries which is help in various ways.In the data age where data is looks like the oil or electricity .In coming days companies are requires more skilled full data scientist , Machine Learning engineer, deep learning engineer, to avail insights by processing massive data sets.

Python libraries for different data science task:

Python Libraries for Data Collection

Beautiful Soup

Scrapy

Selenium

Python Libraries for Data Cleaning and Manipulation

Pandas

PyOD

NumPy

Spacy

Python Libraries for Data Visualization

Matplotlib

Seaborn

Bokeh

Python Libraries for Modeling

Scikit-learn

TensorFlow

PyTorch

Python Libraries for Model Interpretability

Lime

H2O

Python Libraries for Audio Processing

Librosa

Madmom

pyAudioAnalysis

Python Libraries for Image Processing

OpenCV-Python

Scikit-image

Pillow

Python Libraries for Database

Psycopg

SQLAlchemy

Python Libraries for Deployment

Flask

Django

Best Framework for Machine Learning:

1. Tensorflow :

If you are working or interested about Machine Learning, then you might have heard about this famous Open Source library known as Tensorflow. It was developed at Google by Brain Team. Almost all Google’s Applications use Tensorflow for Machine Learning. If you are using Google photos or Google voice search then indirectly you are using the models built using Tensorflow.

Tensorflow is just a computational framework for expressing algorithms involving large number of Tensor operations, since Neural networks can be expressed as computational graphs they can be implemented using Tensorflow as a series of operations on Tensors. Tensors are N-dimensional matrices which represents our Data.



2. Keras :

Keras is one of the coolest Machine learning library. If you are a beginner in Machine Learning then I suggest you to use Keras. It provides a easier way to express Neural networks. It also provides some of the utilities for processing datasets, compiling models, evaluating results, visualization of graphs and many more.

Keras internally uses either Tensorflow or Theano as backend. Some other pouplar neural network frameworks like CNTK can also be used. If you are using Tensorflow as backend then you can refer to the Tensorflow architecture diagram shown in Tensorflow section of this article. Keras is slow when compared to other libraries because it constructs a computational graph using the backend infrastructure and then uses it to perform operations. Keras models are portable (HDF5 models) and Keras provides many preprocessed datasets and pretrained models like Inception, SqueezeNet, Mnist, VGG, ResNet etc

3.Theano :

Theano is a computational framework for computing multidimensional arrays. Theano is similar to Tensorflow , but Theano is not as efficient as Tensorflow because of it’s inability to suit into production environments. Theano can be used on a prallel or distributed environments just like Tensorflow.

4.APACHE SPARK:

Spark is an open source cluster-computing framework originally developed at Berkeley’s lab and was initially released on 26th of May 2014, It is majorly written in Scala, Java, Python and R. though produced in Berkery’s lab at University of California it was later donated to Apache Software Foundation.

Spark core is basically the foundation for this project, This is complicated too, but instead of worrying about Numpy arrays it lets you work with its own Spark RDD data structures, which anyone in knowledge with big data would understand its uses. As a user, we could also work with Spark SQL data frames. With all these features it creates dense and sparks feature label vectors for you thus carrying away much complexity to feed to ML algorithms.

5. CAFFE:

Caffe is an open source framework under a BSD license. CAFFE(Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which was developed by UC Berkeley, this framework is mainly written in CPP. It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It supports almost all major schemes and is fully connected neural network designs, it offers GPU as well as CPU based acceleration as well like TensorFlow.

CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.

6.PyTorch.

Torch is also a machine learning open source library, a proper scientific computing framework. Its makers brag it as easiest ML framework, though its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers(no int, short or double) in it which are not categorized further like in any other language. So its ease many operations and functions. Torch is used by Facebook AI Research Group, IBM, Yandex and the Idiap Research Institute, it has recently extended its use for Android and iOS.

7.Scikit-learn

Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy and matplotlib, it is also one of the most efficient tool for statistical modeling techniques namely classification, regression, clustering.

Scikit-Learn comes with features like supervised & unsupervised learning algorithms and even cross-validation. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM.

Below is a list of frameworks for machine learning engineers:

Apache Singa is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users.

Amazon Machine Learning  is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.  It connects to data stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on said data to create a model.

Azure ML Studio allows Microsoft Azure users to create and train models, then turn them into APIs that can be consumed by other services. Users get up to 10GB of storage per account for model data, although you can also connect your own Azure storage to the service for larger models. A wide range of algorithms are available, courtesy of both Microsoft and third parties. You don’t even need an account to try out the service; you can log in anonymously and use Azure ML Studio for up to eight hours.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.  Models and optimization are defined by configuration without hard-coding & user can switch between CPU and GPU. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.

H2O makes it possible for anyone to easily apply math and predictive analytics to solve today’s most challenging business problems. It intelligently combines unique features not currently found in other machine learning platforms including: Best of Breed Open Source Technology, Easy-to-use WebUI and Familiar Interfaces, Data Agnostic Support for all Common Database and File Types. With H2O, you can work with your existing languages and tools. Further, you can extend the platform seamlessly into your Hadoop environments.

Massive Online Analysis (MOA) is the most popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.

MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

mlpack, a C++-based machine learning library originally rolled out in 2011 and designed for “scalability, speed, and ease-of-use,” according to the library’s creators. Implementing mlpack can be done through a cache of command-line executables for quick-and-dirty, “black box” operations, or with a C++ API for more sophisticated work. Mlpack provides these algorithms as simple command-line programs and C++ classes which can then be integrated into larger-scale machine learning solutions.

Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and  visualization.

Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages — NumPy, SciPy, and matplotlib — for math and science work. The resulting libraries can be used either for interactive “workbench” applications or be embedded into other software and reused. The kit is available under a BSD license, so it’s fully open and reusable. Scikit-learn includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.

Shogun is among the oldest, most venerable of machine learning libraries, Shogun was created in 1999 and written in C++, but isn’t limited to working in C++. Thanks to the SWIG library, Shogun can be used transparently in such languages and environments: as Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, or explorative data analysis.

TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow implements what are called data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The movements of the data through the system are called “flows” — hence, the name. Graphs can be assembled with C++ or Python and can be processed on CPUs or GPUs.

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It was written at the LISA lab to support rapid development of efficient machine learning algorithms. Theano is named after the Greek mathematician, who may have been Pythagoras’ wife. Theano is released under a BSD license.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

Veles is a distributed platform for deep-learning applications, and it’s written in C++, although it uses Python to perform automation and coordination between nodes. Datasets can be analyzed and automatically normalized before being fed to the cluster, and a REST API allows the trained model to be used in production immediately. It focuses on performance and flexibility. It has little hard-coded entities and enables training of all the widely recognized topologies, such as fully connected nets, convolutional nets, recurent nets etc.