Oreilly – Up and Running with PyTorch 2025-3
Oreilly – Up and Running with PyTorch 2025-3 Downloadly IRSpace
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Up and Running with PyTorch course. This course teaches the basics of working with PyTorch, one of the powerful deep learning frameworks. It begins with an introduction to PyTorch and its role in deep learning research, then explains basic concepts such as tensors as building blocks of data and how to work with them. It also teaches the importance of computational graphs and the backpropagation mechanism using the torch.autograd module. One of the key sections is optimizing computations using GPUs and introducing learning algorithms such as Gradient Descent and Stochastic Gradient Descent (SGD). Next, the concepts are put into practice by implementing a simple linear regression model. Then, the structure of perceptrons and neurons as the basic units of neural networks is introduced, and a multilayer neural network (MLP) is created using the torch.nn module. The course concludes with a comprehensive review of the material presented, paving the way for learning more complex models.
What you will learn:
- Understand the basic concepts of PyTorch and deep learning frameworks
- Working with Tensors in PyTorch
- Understanding the concept of Computational Graphs and Backpropagation
- Using torch.autograd for automatic differentiation
- Working with GPUs in PyTorch
- Understanding the components of a learning algorithm
- Implementing a Linear Regression Model with PyTorch
- Understanding the concept of Perceptrons and Neurons
- Using layers and activation functions with torch.nn
- Building Multilayer Feedforward (MLP) Neural Networks
Who is this course suitable for?
- People who are just getting acquainted with deep learning and PyTorch.
- Developers who want to use PyTorch to build deep learning models.
- Students and researchers interested in learning the fundamental concepts of deep learning.
- Anyone who wants to gain a practical understanding of how neural networks work.
Up and Running with PyTorch course details
- Publisher: Oreilly
- Instructor: Jonathan Dinu
- Training level: Beginner to advanced
- Training duration: 2 hours and 55 minutes
Course headings
- Introduction
Up and Running with PyTorch: Introduction - Lesson 1: PyTorch for the Impatient
1.1 What Is PyTorch?
1.2 The PyTorch Layer Cake
1.3 The Deep Learning Software Trilemma
1.4 What Are Tensors, Really?
1.5 Tensors in PyTorch
1.6 Introduction to Computational Graphs
1.7 Backpropagation Is Just the Chain Rule
1.8 Effortless Backpropagation with torch.autograd
1.9 PyTorch’s Device Abstraction (ie, GPUs)
1.10 Working with Devices
1.11 Components of a Learning Algorithm
1.12 Introduction to Gradient Descent
1.13 Getting to Stochastic Gradient Descent (SGD)
1.14 Comparing Gradient Descent and SGD
1.15 Linear Regression with PyTorch
1.16 Perceptrons and Neurons
1.17 Layers and Activations with torch.nn
1.18 Multi-layer Feedforward Neural Networks (MLP) - Summary
Up and Running with PyTorch: Summary
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
680 MB
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