Applied Deep Learning with PyTorch
Demystify neural networks with PyTorch
Seiten
2019
Packt Publishing Limited (Verlag)
9781789804591 (ISBN)
Packt Publishing Limited (Verlag)
9781789804591 (ISBN)
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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 DescriptionMachine 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 forApplied 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.
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 DescriptionMachine 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 forApplied 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.
Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.
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
| Erscheinungsdatum | 02.05.2019 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-13 | 9781789804591 / 9781789804591 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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