From Logical Calculus to Artificial Intelligence
Author: Sandro Skansi
Publisher: Springer
ISBN: 3319730045
Category: Computers
Page: 191
View: 5564
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Deep Learning with Keras
Author: Antonio Gulli,Sujit Pal
Publisher: Packt Publishing Ltd
ISBN: 1787129039
Category: Computers
Page: 318
View: 5884
Python Machine Learning
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1783555149
Category: Computers
Page: 454
View: 7694
Make Your Own Neural Network
Author: Tariq Rashid
Publisher: Createspace Independent Publishing Platform
ISBN: 9781530826605
Category:
Page: 222
View: 8126
Learning TensorFlow
A Guide to Building Deep Learning Systems
Author: Tom Hope,Yehezkel S. Resheff,Itay Lieder
Publisher: "O'Reilly Media, Inc."
ISBN: 1491978481
Category: Computers
Page: 242
View: 9730
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
Author: Tom Hope,Yehezkel S. Resheff,Itay Lieder
Publisher: "O'Reilly Media, Inc."
ISBN: 1491978481
Category: Computers
Page: 242
View: 9730
Deep Learning with Python
Author: Francois Chollet
Publisher: Manning Publications
ISBN: 9781617294433
Category: Machine learning
Page: 384
View: 8026
Beginning Artificial Intelligence with the Raspberry Pi
Author: Donald J. Norris
Publisher: Apress
ISBN: 1484227433
Category: Computers
Page: 369
View: 4351
Artificial Intelligence for Humans, Volume 3
Deep Learning and Neural Networks
Author: Jeff Heaton
Publisher: Createspace Independent Publishing Platform
ISBN: 9781505714340
Category:
Page: 374
View: 7385
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
Author: Jeff Heaton
Publisher: Createspace Independent Publishing Platform
ISBN: 9781505714340
Category:
Page: 374
View: 7385
Machine Learning For Dummies
Author: John Paul Mueller,Luca Massaron
Publisher: John Wiley & Sons
ISBN: 111924577X
Category: Computers
Page: 432
View: 6605
Deep Learning for Natural Language Processing
Creating Neural Networks with Python
Author: Palash Goyal,Sumit Pandey,Karan Jain
Publisher: Apress
ISBN: 1484236858
Category: Computers
Page: 277
View: 9498
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.
Author: Palash Goyal,Sumit Pandey,Karan Jain
Publisher: Apress
ISBN: 1484236858
Category: Computers
Page: 277
View: 9498
Fundamentals of Machine Learning for Predictive Data Analytics
Algorithms, Worked Examples, and Case Studies
Author: John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher: MIT Press
ISBN: 0262029448
Category: Computers
Page: 624
View: 4078
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Author: John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher: MIT Press
ISBN: 0262029448
Category: Computers
Page: 624
View: 4078
Deep Learning
A Practitioner's Approach
Author: Josh Patterson,Adam Gibson
Publisher: "O'Reilly Media, Inc."
ISBN: 1491914211
Category: Computers
Page: 532
View: 4341
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Author: Josh Patterson,Adam Gibson
Publisher: "O'Reilly Media, Inc."
ISBN: 1491914211
Category: Computers
Page: 532
View: 4341
Machine Learning with R
Author: Brett Lantz
Publisher: Packt Publishing Ltd
ISBN: 1782162151
Category: Computers
Page: 396
View: 5696
Fundamentals of Deep Learning
Designing Next-Generation Machine Intelligence Algorithms
Author: Nikhil Buduma,Nicholas Locascio
Publisher: "O'Reilly Media, Inc."
ISBN: 1491925566
Category: Computers
Page: 298
View: 954
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
Author: Nikhil Buduma,Nicholas Locascio
Publisher: "O'Reilly Media, Inc."
ISBN: 1491925566
Category: Computers
Page: 298
View: 954
Deep Learning
Author: Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher: MIT Press
ISBN: 0262337371
Category: Computers
Page: 800
View: 2863
Principles of Data Science
Author: Sinan Ozdemir
Publisher: Packt Publishing Ltd
ISBN: 1785888927
Category: Computers
Page: 388
View: 4729