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 production
Who This Book Is For
Software 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.
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on... Blockchain Basics: A Non-Technical Introduction in 25 Steps
In 25 concise steps, you will learn the basics of blockchain technology. No mathematical formulas, program code, or computer science jargon are used. No previous knowledge in computer science, mathematics, programming, or cryptography is required. Terminology is explained through pictures, analogies, and metaphors.
Pro Python Best Practices: Debugging, Testing and Maintenance
Learn software engineering and coding best practices to write Python code right and error free. In this book you’ll see how to properly debug, organize, test, and maintain your code, all of which leads to better, more efficient coding.
Software engineering is difficult. Programs of any substantial length are...
Everyday Data Structures
A practical guide to learning data structures simply and easily
About This Book
This book is a very practical, friendly, and useful guide that will help you analyze problems and choose the right data structures for your solution
Learn to recognize data patterns for determining which...
Daniel Arbuckle's Mastering Python
Covers the latest and advanced concepts of Python such as parallel processing with Python 3.6
Explore the Python language from its basic installation and setup to concepts such as reactive programming and microservices
Get introduced to the mechanism for rewriting code in a
Pro RESTful APIs: Design, Build and Integrate with REST, JSON, XML and JAX-RS
Discover the RESTful technologies, including REST, JSON, XML, JAX-RS web services, SOAP and more, for building today's microservices, big data applications, and web service applications. This book is based on a course the Oracle-based author is teaching for UC Santa Cruz Silicon Valley which covers architecture, design best...