#neural network python

[nikjag]

simulation of schizophrenia

so i built a simulation of schizophrenia using rust and python

basically you have two groups of simulated neurons, one inhibitory and one excitatory. the excitatory group is connected so they will settle on one specific pattern. the inhibitory group is connected to the excitatory group semi-randomly. the excitatory group releases glutamate while the inhibitory group releases gaba. glutamate will cause the neurons to increase in voltage (or depolarize), gaba will cause the neurons to decrease in voltage (hyperpolarize).

heres a quick visualization of the results in manim

the y axis represents the average firing rate of the excitatory group over time, decay refers to how quickly glutamate is cleared from the neuronal synapse. there are two versions of the simulation, one where the excitatory group is presented with a cue, and one where it is not presented with a cue. when the cue is present, the excitatory group remembers the pattern and settles on it, represented by an increased firing rate. however, not every trial in the simulation leads to a memory recall, if the glutamate clearance happens too quickly, the memory is not maintained. on the other hand, when no cue is presented if glutamate clearance is too low, spontaneous activity overcomes inhibition and activity persists despite there being no input, ie a hallucination.

the simulation demonstrates the failure to maintain the state of the network, either failing to maintain the prescence of a cue or failing to maintain the absence of a cue. this is thought to be one possible explaination of certain schizophrenic symptoms from a computational neuroscience perspective

[feitgemel]

Discover how to build a CNN model for skin melanoma classification using over 20,000 images of skin lesions

We'll begin by diving into data preparation, where we will organize, clean, and prepare the data form the classification model.

Next, we will walk you through the process of build and train convolutional neural network (CNN) model. We'll explain how to build the layers, and optimize the model.

Finally, we will test the model on a new fresh image and challenge our model.

Check out our tutorial here : https://youtu.be/RDgDVdLrmcs

Enjoy

Eran

#Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #SkinMelanoma #melonomaclassification

[edisonlb]

life, maisha, vie, bomoi

[dr-dipaksharan]

पीठ हमेशा मजबूत रखनी चाहिए क्यूंकि शाबासी और धोखा, दोनों पीछे से ही मिलते हैं.

[nucotdata]

DATA SCIENCE WITH PYTHON

Data science is a multidisciplinary field that uses scientific methods, algorithms, and statistical models to analyze structured and unstructured data, uncover patterns, make predictions, and support decision-making processes. Data science leverages various techniques and tools from mathematics, statistics, computer science, and domain-specific knowledge to extract value from data. These techniques include regression analysis, classification, clustering, natural language processing (NLP), deep learning, data visualization, and more. Data scientists often use programming languages like Python programming language and its associated libraries and tools for performing data analysis, extracting insights, and building predictive models. Python has gained significant popularity in the data science community due to its simplicity, versatility, and extensive ecosystem of libraries specifically designed for data manipulation, visualization, and machine learning and utilizes libraries and frameworks such as pandas, NumPy, sci-kit-learn, TensorFlow, or PyTorch to implement data science workflows effectively.

Data science finds applications in various industries, including finance, healthcare, marketing, e-commerce, social media, manufacturing, and more. It plays a crucial role in solving complex problems, driving innovation, making data-driven decisions, and improving business performance. Data science is already helping the airline industry, it optimizes plan routes and decides whether to schedule direct or connecting flights, forecast flight delays and offer promotional offers to individuals according to customers.

APPLICATIONS OF DATA SCIENCE

Product recommendations draw insights out of customers browsing history, and purchase history. Data Science predicts the data by forecasting, let's take weather forecasting as an example Data Science also aids in effective decision-making, Self-driving car is the best example Data science helps in fraud detection, lets take example of COVID-19 vaccination

Several Benefits Of Taking Data Science Training:

Skill Development: A data science course provides structured learning and hands-on experience in various data science techniques, tools, and methodologies. Career Opportunities: Data science is a rapidly growing field with a high demand for skilled professionals. By completing a data science course, you enhance your employability and open up a wide range of career opportunities. In-Demand Skills: Data science skills are in high demand across industries. By acquiring proficiency in data analysis, machine learning, and data visualization, you position yourself as a valuable asset to organizations seeking to extract insights from large volumes of data to make data-driven decisions. Problem-Solving Abilities: Data science courses equip you with a problem-solving mindset. You learn how to approach complex business problems, identify relevant data, analyze it, and derive actionable insights. Hands-on Experience: Many data science courses offer hands-on projects and case studies that allow you to apply the concepts you learn. Working on real-world datasets and solving practical problems helps you gain practical experience and build a portfolio, which can be valuable when applying for data science positions. Networking Opportunities: Joining a data science course gives you access to a community of like-minded individuals, including instructors, industry experts, and fellow students. Networking with professionals in the field can lead to valuable connections, mentorship, and potential job opportunities. Continuous Learning: Data science is a rapidly evolving field with new techniques, algorithms, and tools emerging regularly. Learn Data Science with Python to build a good and promising career with Nucot.

[lord-kyle-yann]

My thoughts on Animation Vs. Coding

Spoiler under the cut

I LOVE it !

I like how it shows the different uses of coding with the uses of different libraries. I also like how it's in Python, the most used programming language. And how it can be used to do incredibly complex things at the end of the episode.

However I do think this is the weakest of the Animation Vs Education episode we got so far.

The first reason for this is the time. It's only 8:28 minutes, it's litteraly shorter than Animation VS Geometry ! Now don't get me wrong I don't think the longer is the better, far from it. But with a topic as vast as coding is, I think it's kinda sad we didn't get more.

The second reason is because of the narrative structure of the episode : It's Animation Vs Math, but worse.

Let me explain : Act 1 Yellow/Orange explore their new environnement while learning the base knowledge they'll need in this new world. Act 2 an inhabitant of this world interrupts them and a fight ensue, while fighting they learn more and more complex knowledge but still quite simple. Act 3 They're now fighting with really complicated concepts and in their conflict, they'll end up destroying the world with a nuke/a big laser. And in act 4 they discover they've gone too far and become friends with the episodes antagonist.

Of course there's difference, but essentially it's basically the same

With near half the time AvMath had, this episode is really fast. There's no pause between the fight to let Yellow truly learn the intermediate knowledge. Why is Yellow capable of what they're doing in the end ? When did they learned how a function or a class worked ? The computer (we need a name for this lil guy) was the one coding all the time when Yellow was just Interrupting it or doing minor adjustements. How did Yellow went from playing with a print and two variables to doing a whole neural network ? ("but you see there's a time lapse between when they started and finished the neural network, they learned during that time" No they didn't, they immediatly started like they knew exactly what to do and not experimenting. Yellow didn't learn there, they already knew)

And with these two reasons combined, I think that's why Animation Vs Coding feels less mastered than the previous AvE episodes.

I still love this episode as someone who loves coding, but with previous episodes being such bangers it's normal that this one was gonna have some flaws. As I said, coding is a big of a topic to choose, so of course things would've been missing and all.

Plus the music is cool (It's from the same guy who made AvGeometry's. What a banger)

[nikitricky]

These are 50 triangles "learning" themselves to mimic this image of a hot dog.

If you clicked "Read more", then I assume you'd be interested to hear more about this. I'll try my best, sorry if it ends up a bit rambly. Here is how I did that.

Points in multiple dimensions and function optimization

This section roughly describes some stuff you need to know before all the other stuff.

Multiple dimensions - Wikipedia roughly defines dimensionality as "The minimum number of coordinates needed to specify any point within it", meaning that for a 2-dimensional space, you need 2 numbers to specify the coordinates (x and y), but in a 3-dimensional space you need 3 numbers (x, y, and z). There are an infinite amount of dimensions (yes, even one million dimensional space exists)

Function optimization - Optimization functions try and optimize the inputs of a function to get a given output (usually the minimum, maximum, or some specific value).

How to train your triangles

Representing triangles as points - First, we need to convert our triangles to points. Here are the values that I use. (every value is normalized between 0 and 1) • 4 values for color (r, g, b, a) • 6 values for the position of each point on the triangle (x, y pair multiplied by 3 vertices) Each triangle needs 10 values, so for 10 triangles we'd need 100 values, so any image containing 10 triangles can be represented as a point in 100-dimensional space

Preparing for the optimization function - Now that we can create images using points in space, we need to tell the optimization function what to optimize. In this case - minimize the difference between 2 images (the source and the triangles). I'll be using RMSE

Training - We finally have all the things to start training. Optimization functions are a very interesting and hard field of CS (its most prominent use is in neural networks), so instead of writing my own, I'll use something from people who actually know what they're doing. I'm writing all of this code in Python, using ZOOpt. The function that ZOOpt is trying to optimize goes like so: • Generate an image from triangles using the input • Compare that image to the image we're trying to get • Return the difference

That's it! We restrict how long it takes by setting a limit on how many times can the optimizer call the function and run.

Thanks for reading. Sorry if it's a bit bad, writing isn't my forte. This was inspired by this.

You can find my (bad) code here:

https://gist.github.com/NikiTricky2/6f6e8c7c28bd5393c1c605879e2de5ff

Here is one more image for you getting so far

[miscellaneousmathematics]

Gonna try and program a convolutional neural network with backpropagation in one night. I did the NN part with python and c++ over the summer but this time I think I'm gonna use Fortran because it's my favorite. We'll see if I get to implementing multi-processing across the computer network I built.

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[compneuropapers]

Interesting Papers for Week 42, 2024

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. Abatis, M., Perin, R., Niu, R., van den Burg, E., Hegoburu, C., Kim, R., … Stoop, R. (2024). Nature Neuroscience, 27(7), 1309–1317.

Jointly efficient encoding and decoding in neural populations. Blanco Malerba, S., Micheli, A., Woodford, M., & Azeredo da Silveira, R. (2024). PLOS Computational Biology, 20(7), e1012240.

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs. Driscoll, L. N., Shenoy, K., & Sussillo, D. (2024). Nature Neuroscience, 27(7), 1349–1363.

Kinetic features dictate sensorimotor alignment in the superior colliculus. González-Rueda, A., Jensen, K., Noormandipour, M., de Malmazet, D., Wilson, J., Ciabatti, E., … Tripodi, M. (2024). Nature, 631(8020), 378–385.

A recurrent network model of planning explains hippocampal replay and human behavior. Jensen, K. T., Hennequin, G., & Mattar, M. G. (2024). Nature Neuroscience, 27(7), 1340–1348.

Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy. Kaliuzhna, M., Carruzzo, F., Kuenzi, N., Tobler, P. N., Kirschner, M., Geffen, T., … Kaiser, S. (2024). Brain, 147(7), 2459–2470.

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties. Kessler, F., Frankenstein, J., & Rothkopf, C. A. (2024). Nature Communications, 15, 5677.

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input. Kristensen, S. S., & Jörntell, H. (2024). Scientific Reports, 14, 15243.

Factorized visual representations in the primate visual system and deep neural networks. Lindsey, J. W., & Issa, E. B. (2024). eLife, 13, e91685.3.

A mathematical theory of relational generalization in transitive inference. Lippl, S., Kay, K., Jensen, G., Ferrera, V. P., & Abbott, L. F. (2024). Proceedings of the National Academy of Sciences, 121(28), e2314511121.

Precise tactile localization on the human fingernail. Longo, M. R. (2024). Proceedings of the Royal Society B: Biological Sciences, 291(2026).

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory. Master, S. L., Li, S., & Curtis, C. E. (2024). Journal of Neuroscience, 44(28), e0060242024.

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques. Noel, J.-P., Balzani, E., Savin, C., & Angelaki, D. E. (2024). Nature Communications, 15, 5738.

Reward prediction error neurons implement an efficient code for reward. Schütt, H. H., Kim, D., & Ma, W. J. (2024). Nature Neuroscience, 27(7), 1333–1339.

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control. Schwöbel, S., Marković, D., Smolka, M. N., & Kiebel, S. (2024). PLOS Computational Biology, 20(7), e1012228.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs. Shahshahani, L., King, M., Nettekoven, C., Ivry, R. B., & Diedrichsen, J. (2024). eLife, 13, e96386.3.

A simple optical flow model explains why certain object viewpoints are special. Stewart, E. E. M., Fleming, R. W., & Schütz, A. C. (2024). Proceedings of the Royal Society B: Biological Sciences, 291(2026).

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Tang, D., Zylberberg, J., Jia, X., & Choi, H. (2024). Nature Communications, 15, 5753.

Control over self and others’ face: exploitation and exploration. Wen, W., Mei, J., Aktas, H., Chang, A. Y.-C., Suzuishi, Y., & Kasahara, S. (2024). Scientific Reports, 14, 15473.

BCI Toolbox: An open-source python package for the Bayesian causal inference model. Zhu, H., Beierholm, U., & Shams, L. (2024). PLOS Computational Biology, 20(7), e1011791.

[feitgemel]

🎣 Classify Fish Images Using MobileNetV2 & TensorFlow 🧠

In this hands-on video, I’ll show you how I built a deep learning model that can classify 9 different species of fish using MobileNetV2 and TensorFlow 2.10 — all trained on a real Kaggle dataset! From dataset splitting to live predictions with OpenCV, this tutorial covers the entire image classification pipeline step-by-step.

🚀 What you’ll learn:

How to preprocess & split image datasets

How to use ImageDataGenerator for clean input pipelines

How to customize MobileNetV2 for your own dataset

How to freeze layers, fine-tune, and save your model

How to run predictions with OpenCV overlays!

You can find link for the code in the blog: https://eranfeit.net/how-to-actually-fine-tune-mobilenetv2-classify-9-fish-species/

You can find more tutorials, and join my newsletter here : https://eranfeit.net/

👉 Watch the full tutorial here: https://youtu.be/9FMVlhOGDoo

Enjoy

Eran

#Python #ImageClassification #MobileNetV2

[emexotechnologies]

Best AI Training in Electronic City, Bangalore – Become an AI Expert & Launch a Future-Proof Career!

Artificial Intelligence (AI) is reshaping industries and driving the future of technology. Whether it's automating tasks, building intelligent systems, or analyzing big data, AI has become a key career path for tech professionals. At eMexo Technologies, we offer a job-oriented AI Certification Course in Electronic City, Bangalore tailored for both beginners and professionals aiming to break into or advance within the AI field.

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With a well-rounded curriculum and expert mentorship, our course serves learners across various backgrounds and experience levels.

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Our AI Certification Course in Electronic City, Bangalore covers the most in-demand tools and techniques. Key topics include:

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Deep Learning Tools: Master TensorFlow, Keras, OpenCV, and other industry-used libraries

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🔖 Popular Hashtags

[mitvpusolapur]

Top B.Tech Courses in Maharashtra – CSE, AI, IT, and ECE Compared

B.Tech courses continue to attract students across India, and Maharashtra remains one of the most preferred states for higher technical education. From metro cities to emerging academic hubs like Solapur, students get access to diverse courses and skilled faculty. Among all available options, four major branches stand out: Computer Science and Engineering (CSE), Artificial Intelligence (AI), Information Technology (IT), and Electronics and Communication Engineering (ECE).

Each of these streams offers a different learning path. B.Tech in Computer Science and Engineering focuses on coding, algorithms, and system design. Students learn Python, Java, data structures, software engineering, and database systems. These skills are relevant for software companies, startups, and IT consulting.

B.Tech in Artificial Intelligence covers deep learning, neural networks, data processing, and computer vision. Students work on real-world problems using AI models. They also learn about ethical AI practices and automation systems. Companies hiring AI talent are in healthcare, retail, fintech, and manufacturing.

B.Tech in IT trains students in systems administration, networking, cloud computing, and application services. Graduates often work in system support, IT infrastructure, and data management. IT blends technical and management skills for enterprise use.

B.Tech ECE is for students who enjoy working with circuits, embedded systems, mobile communication, robotics, and signal processing. This stream is useful for telecom companies, consumer electronics, and control systems in industries.

Key Differences Between These B.Tech Programs:

CSE is programming-intensive. IT includes applications and system-level operations.

AI goes deeper into data modeling and pattern recognition.

ECE focuses more on hardware, communication, and embedded tech.

AI and CSE overlap, but AI involves more research-based learning.

How to Choose the Right B.Tech Specialization:

Ask yourself what excites you: coding, logic, data, devices, or systems.

Look for colleges with labs, project-based learning, and internship support.

Talk to seniors or alumni to understand real-life learning and placements.

Explore industry demand and long-term growth in each field.

MIT Vishwaprayag University, Solapur, offers all four B.Tech programs with updated syllabi, modern infrastructure, and practical training. Students work on live projects, participate in competitions, and build career skills through soft skills training. The university also encourages innovation and startup thinking.

Choosing the right course depends on interest and learning style. CSE and AI suit tech lovers who like coding and research. ECE is great for those who enjoy building real-world devices. IT fits students who want to blend business with technology.

Take time to explore the subjects and talk to faculty before selecting a stream. Your B.Tech journey shapes your future, so make an informed choice.

[jessicaalltick]

Alltick API: Where Market Data Becomes a Sixth Sense

When trading algorithms dream, they dream in Alltick’s data streams.

The Invisible Edge

Imagine knowing the market’s next breath before it exhales. While others trade on yesterday’s shadows, Alltick’s data interface illuminates the present tense of global markets:

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The API That Thinks in Light-Years

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Secret Weapons of the Algorithmic Elite

Fed Whisperer Module: Decode central bank speech patterns 14ms before news wires explode

Meme Market Cortex: Track Reddit/Github/TikTok sentiment shifts through self-training NLP interfaces

Quantum Dust Explorer: Mine microsecond-level anomalies in options chains for statistical arbitrage gold

Build the Unthinkable

Your dev playground includes:

🧪 CRISPR Data Editor: Splice real-time ticks with alternative data genomes

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The Silent Revolution

Last month, three Alltick-powered systems achieved the impossible:

A crypto bot front-ran Elon’s tweet storm by analyzing Starlink latency fluctuations

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Warning: May cause side effects including disgust toward legacy APIs, uncontrollable urge to optimize everything, and permanent loss of "downtime"概念.

Alltick doesn’t predict the future—we deliver it 42 microseconds early.(Data streams may contain traces of singularity. Not suitable for analog traders.)