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Foundations and Core Concepts of PyTorch

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Foundations and Core Concepts of PyTorch

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Set up and configure a PyTorch environment.

  • Understand fundamental AI and machine learning concepts.

  • Build, train, and evaluate neural networks from scratch, utilizing various optimization techniques

  • Apply PyTorch to real-world deep learning tasks.

Details to know

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Recently updated!

September 2024

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 7 modules in this course

In this module, we will introduce you to the course structure, covering the main topics and learning objectives. You'll learn how to set up your system, including installing necessary software and creating a conda environment. We'll also guide you on accessing course materials and provide tips for navigating the course efficiently.

What's included

6 videos2 readings

In this module, we will delve into the basics of machine learning. You will start with an introduction to artificial intelligence and its core concepts. The module will then explore the essentials of machine learning and provide an overview of different machine learning models, laying the groundwork for more advanced topics.

What's included

3 videos

In this module, we will explore the foundational concepts of deep learning. You will gain insights into deep learning models, their performance evaluation, and the evolution from perceptrons to neural networks. The module also covers various types of neural network layers, activation functions, loss functions, and optimization techniques, providing a robust understanding of deep learning frameworks.

What's included

9 videos1 assignment

In this module, we will focus on evaluating machine learning models. You will learn about underfitting and overfitting, and how to mitigate these issues. The module will also cover the train-test split method and its importance in model evaluation, along with various resampling techniques to manage imbalanced datasets effectively.

What's included

3 videos

In this module, we will guide you through the process of constructing a neural network from scratch. You will start with data preparation and model initialization and proceed to implement essential functions such as forward and backward propagation. The module also covers training and evaluation techniques to help you build and assess your neural network model effectively.

What's included

12 videos1 assignment

In this module, we will explore the concept of tensors and their significance in PyTorch. You will learn about the relationship between tensors and computational graphs and gain hands-on experience with tensor operations through coding exercises. This module aims to equip you with the skills to apply tensors in real-world machine learning scenarios.

What's included

3 videos

In this module, we will introduce you to PyTorch modeling. You will learn to build and train models from scratch, including linear regression. The module covers batch processing, datasets, and dataloaders to manage data effectively. You will also explore techniques for saving, loading, and optimizing models, including hyperparameter tuning, to enhance your machine learning workflow.

What's included

15 videos1 reading2 assignments

Instructor

Packt - Course Instructors
Packt
375 Courses14,912 learners

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