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Deep learning with Python : a hands-on introduction
\"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.\"--Back cover.
Book
Deep learning with Python : a hands-on introduction
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to productionWho This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
eBook
Stochastic Gradient Descent
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
Book Chapter
Introduction to Tensorflow
In this chapter we will cover Tensorflow which allows users to define mathematical functions via computational graphs and to compute their gradients. Tensorflow is conceptually similar to Theano, and Keras uses both of them as back ends.
Book Chapter
Introduction to Deep Learning
This chapter provides a broad overview and an historical context on the subject of deep learning. It also gives the reader a roadmap for navigating the book, its prerequisites, and further reading to dive deeper into the subject matter.
Book Chapter
Introduction to PyTorch
In this chapter, we will cover PyTorch which is a more recent addition to the ecosystem of the deep learning framework. PyTorch can be seen as a Python front end to the Torch engine (which initially only had Lua bindings) which at its heart provides the ability to define mathematical functions and compute their gradients. PyTorch has fairly good Graphical Processing Unit (GPU) support and is a fast-maturing framework.
Book Chapter
Introduction to Theano
In this chapter we introduce the reader to Theano, which is a Python library for defining mathematical functions (operating over vectors and matrices), and computing the gradients of these functions. Theano is the foundational layer on which many deep learning packages like Keras are based.
Book Chapter
Regularization Techniques
In this chapter we will cover three regularization techniques commonly used in deep learning, namely, early stopping, norm penalties, and dropout. The reader is advised to refer to Chapter 10.1007/978-1-4842-2766-4_2 introducing the basics of machine learning, specifically to revisit the notions of model capacity, overfitting, and underfitting.
Book Chapter
Machine Learning Fundamentals
Deep Learning is a branch of Machine Learning and in this chapter we will cover the fundamentals of Machine Learning. While machine learning as a subject is inherently mathematical in nature, we will keep mathematics to the basic minimum required to develop intuition about the subject.
Book Chapter
Feed Forward Neural Networks
In this chapter we will cover some key concepts around feedforward neural networks.
Book Chapter