#randomsearch

[programmingandengineering]

VE477 Lab 7

Unless specified otherwise, all the programs are expected to be completed in Python or O’caml. In this lab we want to compare three search algorithms. Let A be an array of size n and k be a value to find in A. The first algorithm, RandomSearch, consists in randomly searching A for k. One simply selects a random index i and test if A[i ] = k. If true, then the algorithm returns i and otherwise…

[pandeypankaj]

Can somebody provide step by step to learn Python for data science?

Absolutely the right decision—to learn Python for data science. Segmenting it into something doable may be a good way to go about it honestly. Let the following guide you through a structured way.

1. Learning Basic Python

Syntax and semantics: Get introduced to the basics in syntax, variables, data types, operators, and some basic control flow.

Functions and modules: You will be learning how to define functions, call functions, utilize built-in functions, and import modules.

Data Structures: Comfortable with lists, tuples, dictionaries, and sets.

File I/O: Practice reading from and writing to files.

Resources: Automate the Boring Stuff with Python book.

2. Mastering Python for Data Science Libraries

NumPy: Learn to use NumPy for numerical operations and array manipulations.

Pandas: The course would revolve around data manipulation through the Pandas library, series, and data frames. Try out the cleaning, transformation, and analysis of data.

Familiarize yourself with data visualization libraries: Matplotlib/Seaborn. Learn to make plots, charts, and graphs.

Resources: 

NumPy: official NumPy documentation, DataCamp's NumPy Course

Pandas: pandas documentation, DataCamp's Pandas Course

Matplotlib/Seaborn: matplotlib documentation, seaborn documentation, Python Data Science Handbook" by Jake VanderPlas

3. Understand Data Analysis and Manipulation

Exploratory Data Analysis: Techniques to summarize and understand data distributions

Data Cleaning: missing values, outliers, data inconsistencies.

Feature Engineering: Discover how to create and select the features used in your machine learning models.

Resources: Kaggle's micro-courses, "Data Science Handbook" by Jake VanderPlas

4. Be able to apply Data Visualization Techniques

Basic Visualizations: Learn to create line plots, bar charts, histograms and scatter plots

Advanced Visualizations: Learn heatmaps, pair plots, and interactive visualizations using libraries like Plotly.

Communicate Your Findings Effectively: Discover how to communicate your findings in the clearest and most effective way.

Resource: " Storytelling with Data" – Cole Nussbaumer Knaflic.

5. Dive into Machine Learning

Scikitlearn: Using this package, the learning of concepts in supervised and unsupervised learning algorithms will be covered, such as regression and classification, clustering, and model evaluation.

Model Evaluation: It defines accuracy, precision, recall, F1 score, ROC-AUC, etc.

Hyperparameter Tuning: GridSearch, RandomSearch

For basic learning, Coursera's Machine Learning by Andrew Ng.

6. Real Projects

Kaggle Competitions: Practice what's learned by involving in Kaggle competitions and learn from others.

Personal Projects: Make projects on things that interest you—that is scraping, analyzing, and model building.

Collaboration: Work on a project with other students so as to get the feeling of working at a company.

Tools: Datasets, competitions, and the community provided in Kaggle, GitHub for project collaboration

7. Continue Learning

Advanced topics: Learn deep learning using TensorFlow or PyTorch, Natural Language Processing, and Big Data Technologies such as Spark.

Continual Learning: Next comes following blogs, research papers, and online courses that can help you track the most current trends and technologies in data science.

Resources: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Fast.ai for practical deep learning courses.

Additional Tips

Practice regularly: The more you code and solve real problems, the better you will be at it.

Join Communities: Join as many online forums as possible, attend meetups, and join data science communities to learn from peers.

In summary, take those steps and employ the outlined resources to grow in building a solid base in Python for data science and be well on your way to be proficient in the subject.

[phosphor-cat]

WHAT THE FUCK IS ON MY FYP

[myprogrammingsolver]

VE477 Lab 7

Unless specified otherwise, all the programs are expected to be completed in Python or O’caml. In this lab we want to compare three search algorithms. Let A be an array of size n and k be a value to find in A. The first algorithm, RandomSearch, consists in randomly searching A for k. One simply selects a random index i and test if A[i ] = k. If true, then the algorithm returns i and otherwise…

[trituenhantaoio]

Bạn hay dùng chiến lược nào? #trituenhantaoio #gridsearch #randomsearch #two #basic #strategy #hyper #parameter #tunning https://www.instagram.com/p/CNgK1Jtp1yO/?igshid=49krrpbqhw85

[rapidit]

From random searches to professional use, the internet has become a basic necessity to optimize our way of living. Therefore, as an online IT support service we know that whenever our clients contact us regarding a slow Internet connection, it becomes our highest priority to resolve it as quickly as possible.

[akenygren]

Öppnar en bok i Tornrummet: "Flyktighet" #randomsearch (på/i Sigtunastiftelsen)

[osgon-azure]

Day 12, wild. Random pinterest search for wild and this cool looking owl showed up. So I dream him. Quick morning sketch but still a nice sketch. #art #arts #artist #artistsoninstagram #artistofinstagram #draw #draws #drawing #sketch #traditionalart #pencil #pencildrawing #quicksketch #wild #owl #owlsketch #reference #pintrestreference #wildowl #wildowlart #growingartist #pencilsketch #sketchbook #morningsketch #randomsearch #nature #animals #birds #osgonazureart https://www.instagram.com/p/Bu7yovcnOzy/?utm_source=ig_tumblr_share&igshid=1n9deb50mbzdo

[actualtools]

Совет

А по поводу gridsearch и randomsearch там очень просто, даёшь ему на вход экземпляр модели, задаёшь параметры словарём типа параметр: range, говоришь ему на сколько частей делить выборку и он отдаёт датафрейм с параметрами и лучший вариант.

По модели в интернете есть общие рекомендации, я ими руководствуюсь, например лес спокойно ест выбросы, регрессия плохо с ними настраивается, а про обычное дерево вообще можно забыть, оно конечно легко интерпретируется, но в точности всегда лесу проигрывает.

[eurekakinginc]

"[D] Hyperparam optimisation using RandomSearch with argparse scripts?"- Detail: I tend to write complex model/training scripts in pure python using argparse to pass huge amounts of hyperparameters to the model, and then run these python scripts on multi-gpu EC2s.​I was wondering if anyone knows of any tools out there what would allow me to do hyperparam optimisation by passing different sets of hyperparams to these scripts via the argparse/commandline system? So imagine you have a process which generates hyperparam sets, then kicks off a subprocess which is the python training script with hyperparams passed in via commandline/argparse, then gets the metrics back, stores them, then kicks off the next set, etc, etc.​For basic grid search, you could easily accomplish this via a unix shell script, but for random search it's trickier. One possible solution would be to write a python script which uses sklearn's ParameterSampler and the subprocess module to accomplish all this, but I was curious if there is an already made solution out there which I could use? Would hate to reinvent the wheel if this particular wheel already exists out there somewhere.​Would greatly appreciate any help/tips.. Caption by trias10. Posted By: www.eurekaking.com

[radioplasma]

Random Searches Forum at Dean

https://soundcloud.com/radioplasma/random-searches-public-forum-dean

Produced in partnership with Holyoke Media.

On March 5th, 2020, Dr. Stephen Mahoney, Executive Principal at Holyoke High School, met with students, teachers, parents, school committee members, and community members, to listen to their feedback on the implementation of this policy.

This meeting continued showing the different…

[fullcrate]

We got pulled over by the cops. Haha #randomsearch

[radioplasma]

Random Searches Policy Public Forum

Random Searches Policy Public Forum

https://soundcloud.com/radioplasma/random-searches-public-forum

This is the recording of the public meeting, held at Holyoke High School, North Campus, regarding the random searches policy, on February 27th, 2020.

Dr. Stephen Mahoney, Executive Principal at Holyoke High School, met with students, teachers, parents, and community members, to listen to their feedback on the implementation of this…