Deep Learning with Pytorch and Hugging Face

• You can access the slide deck that covers Pytorch Here
• You can access the slide deck that covers various concepts related to Transformers Here
• It is recommended to read the slide decks before using the following colab notebooks
• Once you get a good grip on the first four modules, you can easily walk through the documentation or other code to build an application. I will keep updating this repository.
• Recorded videos

Colab Notebooks

1. The Fuel: Tensors

   • Difficulty Level: Easy if you have prior experience using Numpy or TensorFlow
   • Understand the Pytorch architecture
   • Create Tensors of 0d,1d,2d,3d,... (a multidimensional array in numpy)
   • Understand the attributes: storage, stride, offset, device
   • Manipulate tensor dimensions
   • Operations on tensors

2. The Engine: Autograd

   • Difficulty Level: Hard, requires a good understanding of backprop algorithm. However, you can skip this and still follow the subsequent notebooks easily.
   • A few more attributes of tensor : requires_grad, grad, grad_fn, _saved_tensors, backward, retain_grad, zero_grad
   • Computation graph: Leaf node (parameters) vs non-leaf node (intermediate computation)
   • Accumulate gradient and update with context manager (torch.no_grad)
   • Implementing a neural network from scratch

3. The factory: nn.Module, Data Utils

   • Difficulty Level: Medium
   • Brief tour into the source code of nn.Module
   • Everything is a module (layer in other frameworks)
   • Stack modules by subclassing nn.Module and build any neural network
   • Managing data with dataset class and DataLoader class

4. Convolutional Neural Network Image Classification

   • Difficulty Level: Medium
   • Using torchvision for datasets
   • build CNN and move it to GPU
   • Train and test
   • Transfer learning
   • Image segmentation

5. Recurrent Neural Network Sequence classification

   • Difficulty Level: Hard for pre-processing part, Medium for model building part
   • torchdata
   • torchtext
   • Embedding for words
   • Build RNN
   • Train,test, infer

6. Using pre-trained models Notebook

   • Difficulty Level: Easy
   • AutoTokenizer
   • AutoModel

7. Fine-Tuning Pre-Trained Models Notebook

   • Difficulty Level: Medium
   • datasets
   • tokenizer
   • data collator with padding
   • Trainer

8. Loading Datasets Notebook

   • Difficulty Level: Easy
   • Dataset from local data files
   • Dataset from Hub
   • Preprocessing the dataset: Slice, Select, map, filter, flatten, interleave, concatenate
   • Loading from external links

9. Build a Custom Tokenizer for translation task Notebook

   • Difficulty Level: Medium
   • Translation dataset as running example
   • Building the tokenizer by encapsulating the Normalizer, pre-tokenizer and tokenization algorithm (BPE)
   • Locally Save and Load the tokenizer
   • Using it in the Transformer module
   • Exercise: Build a Tokenizer with shared vocabulary.

10. Training Custom Seq2Seq model using Vanilla Transformer Architecture Notebook

    • Difficulty Level: Medium, if you know how to build models in PyTorch.
    • Build Vanilla Transformer architecture in Pytorch
    • Create a configuration file for a model using PretrainedConfig class
    • Wrap it by HF PreTrainedModel class
    • Use the custom tokenizer built in the previous notebook
    • Use Trainer API to train the model

11. Gradient Accumulation - Continual Pre-training Notebook

    • Difficulty Level: Easy
    • Understand the memory requirement for training and inference
    • Understand how gradient accumulation overcomes the limited memory

12. Upload it to Hub [Notebook]

    • Under Preparation

UPDATE: Added Hugging Face Notebooks. If you plan to use Transformer Architecture for any task, HF is the way to go! The Transformer module is built on top of Pytorch (and Tensorflow) to do a lot of heavy lifting!