Mastering PyTorch - Second Edition: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

Mastering PyTorch - Second Edition focuses on all the topics that a user requires to work on the latest version of PyTorch, namely PyTorch 2. x. Whether you want to learn image classifiers, sentiment analysis, or generative models for music and or text, this book provides it all. You will also know several backed libraries and frameworks such as Hugging Face, fast.ai, PyTorch Lightning, and PyTorch Geometric and deploy on multiple GPUs and inference services. 

Over the course of reading “Mastering PyTorch - Second Edition,” you’d explore the applicational aspects of deep learning ranging from creating convolutional and recurrent neural networks, transformers, and diffusion models. It will also cover self-reinforcement learning, training with a combination of different precisions, and model deployment for both Android and iOS. It also familiarises you with the rich surroundings of PyTorch to help you through the faster prototype, AutoML, and xAI tools so that you will be armed with tools for any deep learning problem. 

When you complete reading this book, “Mastering PyTorch - Second Edition” you will be ready to solve a variety of challenges in AI by using PyTorch. Practice heavily involves recommendation systems, interpreting machine learning models with Captum, and creating language and vision transformers with Hugging Face. It will be useful for readers who want to enhance their knowledge of machine learning and construct intelligent and effective AI models. 

Mastering PyTorch - Second Edition Table of Contents:

1. Overview of Deep Learning Using PyTorch
2. Deep CNN Architectures
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

Who is this course for?

1. Newcomers’ excitement to adopt complex deep learning algorithms into their data science works. 
2. This is for machine learning engineers who plan on working with PyTorch and wish to learn about improved ways of using the tool. 
3. Engineers, especially those in the field of learning machines, are searching for new approaches to advanced ML. 
4. Computational Deep Learning graduate students interested in optimizing PyTorch. 
5. It refers to professionals who use one machine learning framework known as TensorFlow to the other called PyTorch. 

Click on the links below to Download Mastering PyTorch - Second Edition!