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iamtrask/Grokking-Deep-Learning

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Grokking Deep Learning

Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of neural networks from scratch. It serves as a programming tutorial and implementation guide for understanding the internal mechanics of deep learning.

The project focuses on building various network architectures, including convolutional, recurrent, and long short-term memory networks. It provides step-by-step implementations of fundamental mechanisms such as forward propagation, backpropagation, and gradient descent.

The material covers a broad range of deep learning capabilities, from basic model architecture and training optimization to advanced techniques like automatic differentiation, word embeddings for natural language processing, and federated learning.

The content is delivered through interactive notebooks that interleave code and educational explanations.

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Features

  • Neural Network Implementation Guides - Implements the internal mechanics of backpropagation, gradient descent, and automatic differentiation through practical code examples.
  • Deep Learning Education - Serves as a comprehensive resource for learning neural network theory and practice by building components from scratch.
  • Automatic Differentiation Engines - Implements a system for automatic gradient computation using computational graphs.
  • Deep Learning Architectures - Focuses on the structural composition and design of multi-layered deep learning models.
  • Deep Learning Implementations - Provides code implementations of deep learning algorithms based on first principles.
  • Modular Layer Compositions - Implements a modular system for constructing neural networks by stacking independent layer objects.
  • Neural Network Implementations - Implements core neural network mechanics and training pipelines from scratch.
  • Machine Learning Tutorials - Provides a structured technical guide for implementing deep learning concepts through hands-on coding exercises.
  • Neural Network Implementations - Provides instructional guides for building various neural network architectures like CNNs and LSTMs from scratch.
  • Neural Network Training from Scratch - Implements the training process from scratch using manual forward and backward passes.
  • NumPy-Based Linear Algebra Implementations - Uses NumPy for the linear algebra and matrix multiplications essential for model training.
  • Natural Language Processing Implementations - Implements language understanding components such as word embeddings.
  • Specialized Network Architectures - Implements diverse network types including convolutional and recurrent architectures.
  • Training Optimization Techniques - Implements techniques for improving training convergence through regularization and activation functions.
  • Deep Learning Notebooks - Provides deep learning-specific educational notebooks combining theory and executable code.
  • Advanced AI Techniques - Covers advanced methodologies such as automatic differentiation and federated learning.
  • Advanced Learning Modules - Includes educational modules on complex concepts like word embeddings and federated learning.
  • Interactive Notebook Environments - Delivers educational content through interactive notebooks that interleave explanatory text and executable code.
  • Mathematical Derivative Demonstrations - Provides manual derivative calculations to demonstrate the mathematical flow of backpropagation.
  • Learning and Reference - Grokking Deep Learning book code.
  • Learning Resources - Intuitive guide to understanding deep learning from first principles.
7,707 stars·1,623 forks·Jupyter Notebook·22 views

Star history

Star history chart for iamtrask/grokking-deep-learningStar history chart for iamtrask/grokking-deep-learning

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does iamtrask/grokking-deep-learning do?

Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of neural networks from scratch. It serves as a programming tutorial and implementation guide for understanding the internal mechanics of deep learning.

What are the main features of iamtrask/grokking-deep-learning?

The main features of iamtrask/grokking-deep-learning are: Neural Network Implementation Guides, Deep Learning Education, Automatic Differentiation Engines, Deep Learning Architectures, Deep Learning Implementations, Modular Layer Compositions, Neural Network Implementations, Machine Learning Tutorials.

Which projects share features with iamtrask/grokking-deep-learning?

Projects with overlapping indexed features include: udacity/deep-learning — This project is a deep learning educational course and implementation guide designed for building and training neural… chenyuntc/pytorch-book — This project serves as a comprehensive educational resource and technical guide for mastering deep learning through… yunjey/pytorch-tutorial — This project is a collection of educational examples and code for implementing deep learning architectures using the… mnielsen/neural-networks-and-deep-learning — This project is a comprehensive educational resource and curriculum designed to teach the mathematical foundations and… hunkim/pytorchzerotoall — PyTorchZeroToAll is an educational resource and collection of tutorials focused on deep learning and the PyTorch… lisa-lab/deeplearningtutorials — This project is an educational resource and learning path for building and training neural network architectures. It…

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These projects share indexed features with Grokking Deep Learning. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    This project is a collection of educational examples and code for implementing deep learning architectures using the PyTorch framework. It serves as a tutorial and implementation guide for building various neural network architectures for machine learning tasks. The project provides practical implementations for computer vision, including image classification and neural style transfer, as well as natural language processing examples for building sequence models and language predictors. It also covers generative models using adversarial and variational networks to synthesize or transform visua

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  • mnielsen/neural-networks-and-deep-learningmnielsen avatar

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    This project is a comprehensive educational resource and curriculum designed to teach the mathematical foundations and practical implementation of neural networks. It provides a structured path for understanding how computers learn from data, covering core concepts such as gradient descent, backpropagation, and the biological inspiration behind artificial neurons. The platform distinguishes itself by combining theoretical proofs with hands-on implementation exercises. It demonstrates the universal approximation theorem through visual explanations and guides users in building various architect

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