Theory Part 1 - RNNs and LSTMs
  • Before we Start
  • Introduction to RNNs Part 1
  • Introduction to RNNs Part 2
  • Test Your Understanding
  • Playing with the Activations
  • LSTMs
  • LSTM Variants
  • LSTM Step-by-Step Example Walktrough
Theory Part 2 - Sequence Modeling
  • Sequence Modeling
  • Attention Mechanism in LSTMs
  • How Attention Mechanisms Work
Practical Part 1 - Introduction to PyTorch
  • Installing PyTorch and an Introduction
  • Torch Tensors Part 1
  • Torch Tensors Part 2
  • Numpy Bridge, Tensor Concatenation ad Adding Dimensions
Practical Part 2 - Processing the Dataset
  • The Dataset
  • Processing the Dataset Part 1
  • Processing the Data Part 2
  • Processing the Dataset Part 3
  • Processing the Dataset Part 4
  • Processing the Words
  • Processing the Text
  • Processing the Text Part 2
  • Filtering the Text
  • Getting Rid of Rare Words
Practical Part 3 - Data Preperation
  • Preparing the Data for Model Part 1
  • Understanding the zip function
  • Preparing the Data for Model Part 2
  • Preparing the Data for Model Part 3
  • Preparing the Data for Model Part 4
Practical Part 4 - Building the Model
  • Understanding the Encoder
  • Defining the Encoder
  • Understanding Pack Padded Sequence
  • Designing the Attention Model
  • Designing the Decoder Part 1
  • Designing the Decoder Part 2
Practical Part 5 - Training the Model
  • Creating the Loss Function
  • Teacher Forcing
  • Visualize Training Part 1
  • Visualize Training Part 2
  • Training
  • Proceeding
Deep Learning with Transformers
  • Transformers