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.
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
