Welcome to the Neural Network Resources repository! This README file contains a collection of interesting and useful links related to neural networks. Feel free to explore and contribute additional resources.
- Introduction
- Online Tools
- Tutorials and Guides
- Research Papers
- Books
- Community and Forums
- Contributing
- License
This repository aims to be a comprehensive resource for anyone interested in neural networks, from beginners to advanced practitioners. Here, you'll find links to online tools, tutorials, research papers, and more.
- Setosa.io - Image Kernels: An interactive tool to visualize image kernels and their effects on images. It's a great way to understand how convolutional layers work in neural networks.
- 3D Visualization of a Convolutional Neural Network: Interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real time. Created by Adam W. Harley.
- CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization: An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs). For more information check the paper of the original authors.
- Probability Playground: Interactive Probability Distributions: Probability Playground is designed as an interactive tool for exploring the most common (and some not-so-common) probability distributions and their relationships to each other.
- ConvNetJS: Deep Learning in your browser: ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. It also has interesting demos and explanations.
- ML Cheatsheet: Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
- Kernel Functions-Introduction to SVM Kernel & Examples: Detailed description of SVM Kernel and Different Kernel Functions and its examples such as linear, nonlinear, polynomial, Gaussian kernel, Radial basis function (RBF), sigmoid etc.
- Introduction to Softmax for Neural Network: SoftMax is a popular activation function used for multiclass classification problems.
- Deep Learning Specialization by Andrew Ng: A comprehensive course on deep learning, covering neural networks, CNNs, RNNs, and more.
- PyTorch Tutorials: Official tutorials for learning and using PyTorch for deep learning projects.
- Attention Is All You Need: The paper that introduced the Transformer model, which has become foundational in many NLP tasks.
- ImageNet Classification with Deep Convolutional Neural Networks
- Deep Residual Learning for Image Recognition: The paper introducing ResNet, a very influential deep learning model for image recognition.
- mixup: BEYOND EMPIRICAL RISK MINIMIZATION: The paper that introduces mixup data augmentation technique to alleviate issues such as memorization and sensitivity.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: An essential book covering the fundamentals and advanced topics in deep learning.
- Neural Networks and Deep Learning by Michael Nielsen: An online book that provides an intuitive understanding of neural networks and deep learning.
- r/MachineLearning: A Reddit community for discussions, news, and articles on machine learning and deep learning.
- Stack Overflow: A Q&A platform where you can ask questions and find answers related to neural networks.
Contributions are welcome! If you have any interesting resources or links related to neural networks, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
Thank you for visiting and happy learning! 😁