Ai+ Training

Module 1: PyTorch: tensors, tensors, tensors (15 min)    

• Introducing a simple and familiar example: linear regression   

• Generating synthetic data    

• Tensors: what they are and how to create them

• CUDA: GPU vs CPU tensors    

• Parameters: tensors meet gradients 

Module 2: Gradient Descent in Five Easy Steps (15 min)    

• Step 0: initializing parameters    

• Step 1: making predictions in the forward pass

• Step 2: computing the loss, or “how bad is my model?”    

• Step 3: computing gradients, or “how to minimize the loss?”    

• Step 4: updating parameters    

• Bonus: learning rate, the most important hyper-parameter    

• Step 5: Rinse and repeat 

Module 3: Autograd, your companion for all your gradient needs! (15 min)    

• Computing gradients automatically with the backward method    

• Dynamic Computation Graph: what is that?   

• Optimizers: updating parameters, the PyTorch way    

• Loss functions in PyTorch 

Module 4: Building a Model in PyTorch (15 min)

• Your first custom model in PyTorch    

• Peeking inside a model with state dictionaries

• The importance of setting a model to training mode    

• Nested models, layers, and sequential models

• Organizing our code: the training step 

Module 5: Datasets and data loaders (20 min)   

• Your first custom dataset in PyTorch   

• Data loaders and mini-batches    

• Evaluation phase: setting up the stage   

• Organizing our code: the training loop   

• Putting it all together: data preparation, model configuration, and model training 

• Taking a break: saving and loading models