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

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Book Details
Language
English
Publishers
Packt Publishing; 2nd edition (31 May 2024)
Weight
0.94 KG
Publication Date
31/05/2024
ISBN-10
1801074305
Pages
558 pages
ISBN-13
9781801074308
Dimensions
2.9 x 19.05 x 23.5 cm
SKU
9781801074308
Author Name
Ashish Ranjan Jha (Author)
Ashish Ranjan Jha received his Bachelors degree in Electrical Engineering from IIT Roorkee (India), Masters degree in Computer Science from EPFL (Switzerland) and an MBA degree from Quantic School of Business (Washington). He has received distinction in all 3 of his degrees. He has worked for large technology companies like Oracle, Sony as well as the more recent tech unicorns such as Tractable and Revolut, mostly focussed around Artificial Intelligence. He currently works as Head of Machine Learning and Artificial Intelligence at XYZ Reality, a construction tech startup bringing AR/VR and AI into the world of construction. Ashish has 10+ years of working experience and specialisation in the field of Machine Learning, and Python is his go-to tool. He 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, data scientist, he also blogs frequently on his personal blog site (DataShines) about the latest research and engineering topics around Machine Learning.In his free time, Ashish likes to contribute to open source projects around python / ML, answering issues on stackoverflow, and if time permits - taking on a full blown kaggle competition. He also has a non-technical side in his musician avatar, a food-lover and a runner-for-life.Read more about this authorRead less about this author
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Master advanced techniques and algorithms for machine learning with PyTorch using real-world examplesUpdated for PyTorch 2. x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networksPurchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesUnderstand how to use PyTorch to build advanced neural network modelsGet the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and DockerUnlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworksBook DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications.

This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis.

As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training.

You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries.

These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What you will learnImplement text, vision, and music generation models using PyTorchBuild a deep Q-network (DQN) model in PyTorchDeploy PyTorch models on mobile devices (Android and iOS)Become well versed in rapid prototyping using PyTorch with fastaiPerform neural architecture search effectively using AutoMLEasily interpret machine learning models using CaptumDesign ResNets, LSTMs, and graph neural networks (GNNs)Create language and vision transformer models using Hugging FaceWho this book is forThis deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch.

This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Table of ContentsOverview of Deep Learning using PyTorchDeep CNN architecturesCombining CNNs and LSTMsDeep Recurrent Model ArchitecturesAdvanced Hybrid ModelsGraph Neural NetworksMusic and Text Generation with PyTorchNeural Style TransferDeep Convolutional GANsImage Generation Using DiffusionDeep Reinforcement LearningModel Training OptimizationsOperationalizing PyTorch Models into ProductionPyTorch on Mobile DevicesRapid Prototyping with PyTorchPyTorch and AutoMLPyTorch and Explainable AIRecommendation Systems with TorchRecPyTorch and Hugging Face. .

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