Introduction to PyTorch (crash course)

Introduction to PyTorch (crash course), available at $44.99, has an average rating of 4.15, with 12 lectures, based on 12 reviews, and has 1052 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who would like to learn PyTorch through practise or Anyone who would like to understand PyTorch in depth or Anyone interested in differentiable programming or Anyone interested in machine learning & artificial intelligence It is particularly useful for Anyone who would like to learn PyTorch through practise or Anyone who would like to understand PyTorch in depth or Anyone interested in differentiable programming or Anyone interested in machine learning & artificial intelligence.

Enroll now: Introduction to PyTorch (crash course)

Summary

Title: Introduction to PyTorch (crash course)

Price: $44.99

Average Rating: 4.15

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Automatic differentiation: part1

Lecture 3: Automatic differentiation: part2

Chapter 2: PyTorch

Lecture 1: Tensors

Lecture 2: Computing gradients

Lecture 3: Optimizers

Lecture 4: Optimization: Ballistic problem

Lecture 5: Schedulers

Chapter 3: Neural networks

Lecture 1: Multilayer perceptron (MLP)

Lecture 2: GPU training

Lecture 3: Convolutional neural network (CNN)

Lecture 4: Conclusion

Instructors

Maxime Vandegar
Ingénieur de recherche

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 8 votes

Frequently Asked Questions

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