Introduction to PyTorch (crash course)
Learn PyTorch through practical implementation and gain a deep understanding of differentiable programming. Ideal for anyone interested in AI and machine learning.
What you’ll learn
- How PyTorch works – under the hood
- The integrated differentiation engine of PyTorch
- Learning PyTorch through practice (tensors, optimizers, schedulers, decorators, …)
- Differentiable programming
- Solving an optimization problem (“black-box”) with PyTorch
- Implementing neural networks with PyTorch
In this course, I will explain in a practical and intuitive way how PyTorch works. We will go beyond the use of the API which will allow you to continue your journey in machine learning and/or differentiable programming with more confidence.
This course is divided into three parts.
In the first part, we will implement (in Python, from scratch) our own differentiable programming framework, which will be very similar to PyTorch. This will allow you to understand how PyTorch, TensorFlow, JAX, etc. work. Then, we will focus on PyTorch and see the basic tensor operations, the calculation of gradients and the use of graphics cards (GPUs).
In the second part, we will focus on gradient descent algorithms (essential for training neural networks). We will implement the simulator of a ballistic problem and see how to use the power of PyTorch to solve an optimization problem (this pedagogical problem can be easily extended to real problems, such as fluid mechanics simulations, for those who wish). We will also see how to use optimizers and how to combine them with schedulers to make them even more efficient.
Finally, we will tackle neural networks. We will solve an image classification problem, first with an MLP, and then with a CNN.
If this program enchants you, don’t wait any longer!
Who this course is for:
- Anyone who would like to learn PyTorch through practise
- Anyone who would like to understand PyTorch in depth
- Anyone interested in differentiable programming
- Anyone interested in machine learning & artificial intelligence
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