Deep learning 1

Topics

• Artificial neurons and neural networks
• Deep learning with neural networks
• Teaching neural networks
• Use of trained neural networks
• Hyperparameters of neural networks
• Using the Pytorch environment
• CNN neural networks (Convolutional Neural Networks)
• RNN neural networks (Recurrent Neural Networks)
• Basics of natural language processing (NLP).

Exercises

• Part 1: Introduction to PyTorch and using tensors

• Part 2: Building fully-connected neural networks with PyTorch

• Part 3: How to train a fully-connected network with backpropagation on MNIST

• Part 4: Exercise - train a neural network on Fashion-MNIST

• Part 5: Using a trained network for making predictions and validating networks

• Part 6: How to save and load trained models

• Part 7: Load image data with torchvision, also data augmentation

• Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats

• Part 9: Comparison FC network vs. CNN vs. Pretrained network

• SpaCy 1: NLP basics with Spacy

• SpaCy 2: Word vectors, BLEU and similarity

• RNN 1: RNN basics with character level text classification task

• RNN 2: Character level text generation with RNN

• RNN 3: Encoder-decoder architecture and attention mechanism with RNNs