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
  • Eugenio Culurciello
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