Deep Learning Course

🚀 Introduction:

Course info and phylosophy

📚 Lectures:

unit 0.1 - What is machine learning?
unit 0.2 - Introduction to PyTorch and Tensors
unit 0.3 - Tensors and real data
unit 0.4 - Types of learning
unit 0.5 - Approximating functions
unit 3.1 - Neurons
unit 1.1 - The simplest Neural Network
unit 1.2 - Binary net in PyTorch with manual weights
unit 1.3 - XNOR in neural nets
unit 1.4 - Back-propagation
unit 1.5 - Binary network PyTorch Training
unit 1.6 - Datasets
unit 1.7 - Professional training script
unit 1.8 - Learning curve fitting
unit 1.9 - Neural Networks: what is next in this course?
unit 2.0 - Learning sequences
unit 2.1 - Learning sequences with neural networks
unit 2.2 - Learning sequences with a CNN
unit 2.3 - Learning sequences with a tiny GPT
unit 3.0 - Transformers basics
unit 3.1 - Transformer Network
unit 3.2 - Transformer and LLM examples
unit 3.5 - Recurrent neural networks (RNN)
unit 3.6 - RNN example
unit 4.0 - Convolutional layers
unit 4.1 - Convolutional neural network example
unit 4.2 - Training a CNN on CIFAR
unit 4.3 - Data loaders
Issues
Notes
unit 4.4 - Fixing our first example
unit 4.5 - Labeling data
unit 5.0 - Tips and tricks for training neural nets
unit 5.1 - Neural Networks Building Blocks
unit 5.2 - Neural Network Architectures
unit 5.3 - Fine tuning a pre-trained Neural Network
unit 6.0 - Unsupervised and self-supervised learning
unit 6.1 - Generating images
unit 7.0 - Artificial brains
unit 8.0 - introduction to Reinforcement Learning (RL)
unit 8.1 - Reinforcement learning - Deep Q networks
unit 8.2 - Reinforcement learning - Policy Gradients

💓 Our instructor:

Eugenio Culurciello

Deep Learning Course

unit 3.6 - RNN example
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unit 3.6 - RNN example

For a well developed and commented RNN example implementation, see this repo.

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