Grokking Deep Learning

Grokking Deep Learning - build neural network… | Awesome Repos

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.

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Open-source alternatives to Grokking Deep Learning

chenyuntc/pytorch-book

yunjey/pytorch-tutorial

mnielsen/neural-networks-and-deep-learning

ShusenTang/Dive-into-DL-PyTorch

Star history

Open-source alternatives to Grokking Deep Learning

chenyuntc/pytorch-book

yunjey/pytorch-tutorial

mnielsen/neural-networks-and-deep-learning

ShusenTang/Dive-into-DL-PyTorch