Fundamentals of deep learning : designing next-generation artificial intelligence algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. 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.--Publisher website 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. The Neural Network -- Training Feed-forward Neural Networks -- Implementing Neural Networks In Tensorflow -- Beyond Gradient Descent -- Convolutional Neural Networks -- Embedding And Representation Learning -- Models For Sequence Analysis -- Memory Augmented Neural Networks -- Deep Reinforcement Learning. Nikhil Buduma ; With Contributions By Nicholas Locascio. Includes Bibliographical References And Index.