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APPLIED DEEP LEARNING WITH PYTORCH: DEMYSTIFY NEURAL By Hyatt Saleh *Excellent*
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eBay item number:335358540565
Item specifics
- Condition
- Very Good
- Seller Notes
- ISBN-10
- 1789804590
- Book Title
- Applied Deep Learning with PyTorch: Demystify neural networks
- Item Height
- 9.25 inches
- Item Weight
- 0.97 pounds
- ISBN
- 9781789804591
- Publication Year
- 2019
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Subject Area
- Computers
- Publication Name
- Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch
- Publisher
- Packt Publishing, The Limited
- Item Length
- 3.6 in
- Subject
- Intelligence (Ai) & Semantics, Neural Networks, Programming Languages / Python
- Item Width
- 3 in
- Number of Pages
- 254 Pages
About this product
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1789804590
ISBN-13
9781789804591
eBay Product ID (ePID)
10038401091
Product Key Features
Number of Pages
254 Pages
Publication Name
Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch
Language
English
Publication Year
2019
Subject
Intelligence (Ai) & Semantics, Neural Networks, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Length
3.6 in
Item Width
3 in
Additional Product Features
Intended Audience
Trade
Table Of Content
Table of Contents Introduction to Deep Learning and PyTorch Building Blocks of Neural Networks A Classification Problem Using DNNs Convolutional Neural Networks Style Transfer Analyzing the Sequence of Data with RNNs
Synopsis
Starting with the basics of deep learning and their various applications, Applied Deep Learning with PyTorch shows you how to solve trending tasks, such as image classification and natural language processing by understanding the different architectures of the neural networks., Implement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architectures Key Features Understand deep learning and how it can solve complex real-world problems Apply deep learning for image classification and text processing using neural networks Develop deep learning solutions for tasks such as basic classification and solving style transfer problems Book Description Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns, which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you'll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). By the end of this book, you'll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems. What you will learn Detect a variety of data problems to which you can apply deep learning solutions Learn the PyTorch syntax and build a single-layer neural network with it Build a deep neural network to solve a classification problem Develop a style transfer model Implement data augmentation and retrain your model Build a system for text processing using a recurrent neural network Who this book is for Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this book useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.
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