PyTorch 101: building a model step-by-step

Abstract: 

Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.

The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and "torch" it! At the end of it, you should be able to understand PyTorch's key components and how to assemble them together into a working model.

Session Outline:
Module 1: PyTorch: tensors, tensors, tensors
• 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
• 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
• 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
• 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

Background Knowledge:
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming.

Bio: 

Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers. He writes regularly for Towards Data Science. His blog post "Understanding PyTorch with an example: a step-by-step tutorial" reached more than 220,000 views since it was published. The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics. Daniel is also the main contributor of two python packages: HandySpark and DeepReplay. His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.

Open Data Science

 

 

 

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