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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

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With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

  • Examine the foundations of machine learning and neural networks
  • Learn how to train feed-forward neural networks
  • Use TensorFlow to implement your first neural network
  • Manage problems that arise as you begin to make networks deeper
  • Build neural networks that analyze complex images
  • Perform effective dimensionality reduction using autoencoders
  • Dive deep into sequence analysis to examine language
  • Understand the fundamentals of reinforcement learning
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Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural...

Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube, GitHub, and more
Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube, GitHub, and more

Leverage the power of Python to collect, process, and mine deep insights from social media data

About This Book

  • Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more
  • Analyze and extract actionable insights from your social data using various...

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Building Machine Learning Systems with Python - Second Edition
Building Machine Learning Systems with Python - Second Edition

Get more from your data through creating practical machine learning systems with Python

About This Book

  • Build your own Python-based machine learning systems tailored to solve any problem
  • Discover how Python offers a multiple context solution for create machine learning systems
  • ...
Deep Learning with Python: A Hands-on Introduction
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...
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