Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features - Image 1

£47.99

Packt Publishing Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Name: Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features
Brand: Packt Publishing
Price: 47.99 GBP
Availability: InStock

AI & Machine Learning

This is the most expensive it has ever been. Walk away.

£48 today · previous high £48 · all-time low £22

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 75 days · 75 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 75 days • 4 price levels

Days at Price

Current Price

Price Analysis

Most common price: £45 (30 days, 40.0%)

Price range: £22 - £48

Price levels: 4 different prices over 75 days

Description

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book Description Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required. Table of Contents Overview of Deep Learning Using PyTorch Combining CNNs and LSTMs Deep CNN Architectures Deep Recurrent Model Architectures Hybrid Advanced Models Music and Text Generation with PyTorch Neural Style Transfer Deep Convolutional GANs Deep Reinforcement Learning Operationalizing Pytorch Models into Production Distributed Training PyTorch and AutoML PyTorch and Explainable AI Rapid Prototyping with PyTorch

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1789614384
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 12 February 2021
Listed Since: 20 November 2020

Barcode

No barcode data available

Packt Publishing Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation

Principles and Labs for Deep Learning

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Deep Learning at Scale: At the Intersection of Hardware, Software, and Data

Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Deep Learning with PyTorch, Second Edition: Training and Applying Deep Learning and Generative Ai Models

Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Machine Learning and Artificial Intelligence: Concepts, Algorithms and Models

State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)

Machine Learning with CUDA: Enhancing Neural Network Performance (GPU Mastery Series: Unlocking CUDA's Power using pyCUDA)

Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch

Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents

Springer Deep Learning: Concepts and Architectures 866 Book