Deep Learning Course
Deep learning through examples - this is the phylosophy of this course. We introduce data science, machine learning and neural networks in applied tutorials. This course is based on python notebooks and learning by examples. Have fun learning - never stop learning.
People
- Instructor
Eugenio Culurciello (culurciello website)
- TAs
TBD (
TBD AT purdue DOT edu)
Useful links
tbd (used for tbd)
🚀 Introduction:
📚 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: