Librería Portfolio

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
Learn the fundamentals of reinforcement learning



Table of Contents
Preface
Chapter 1 The Neural Network
Chapter 2 Training Feed-Forward Neural Networks
Chapter 3 Implementing Neural Networks in TensorFlow
Chapter 4 Beyond Gradient Descent
Chapter 5 Convolutional Neural Networks:
Chapter 6 Embedding and Representation Learning
Chapter 7 Models for Sequence Analysis
Chapter 8 Deep Reinforcement Learning