Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Index

A refresher on deep learning

Neural networks are a sub-type of machine learning methods that are inspired by the structure and function of the biological brain, such as the biological neuron shown in Figure 1.2. In neural networks, each computational unit, analogically called a neuron, is connected to other neurons in a layered fashion. When the number of such layers is more than two, the neural network thus formed is called a Deep Neural Network (DNN). Such models are generally called deep learning models.

Figure 1.2: Artificial neuron inspired by biological neuron. (Biological neuron image by: https://pixabay.com/users/clker-free-vector-images-3736)

Deep learning models have been proven superior to other classical machine learning models because of their ability to learn highly complex relationships between input data and the output (ground truth). In recent times, deep learning has gained a lot of attention, and rightly so, primarily because of the following two reasons...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime