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

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

by Ashish Ranjan Jha
Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond
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Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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

by Ashish Ranjan Jha

Product Details

ISBN-13: 9781801079969
Publisher: Packt Publishing
Publication date: 05/31/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 554
File size: 43 MB
Note: This product may take a few minutes to download.

About the Author

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

Table of Contents

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 TorchRec
  19. PyTorch and Hugging Face
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Computers
Science & Technology
Engineering
Technology
Artificial Intelligence (AI)
Robotics & Artificial Intelligence
Neural Networks

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