Neural Networks: A Visual Introduction for Beginners

Main
Computers - Computer Science
Neural Networks: A Visual Introduction...

Neural Networks: A Visual Introduction for Beginners

Michael Taylor

5.0 / 0

Колко ви харесва тази книга?

Какво е качеството на файла?

Изтеглете книгата за оценка на качеството

Какво е качеството на изтеглените файлове?

A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

Категории:

Computers - Computer Science

Година:

2017

Издание:

Paperback

Издателство:

Blue Windmill Media

Език:

english

Страници:

250

ISBN 10:

1549869132

ISBN 13:

9781549869136

Файл:

EPUB, 4.55 MB

IPFS:

,

english, 2017

Четете Онлайн

Преобразуването в се извършва

Преобразуването в е неуспешно

Може да се интересувате от

彭定求 — 全唐诗简体增订本（中华书局）

LazyProgrammer — Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python)

Scott Hartshorn — Hypothesis Testing - A Visual Introduction To Statistical Significance

Scott Hartshorn — Bayes' Theorem Examples - A Visual Guide For Beginners

William Sullivan — Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction

Michael Nielsen — Neural Networks and Deep Learning

Scott Hartshorn — Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

Josh Hugh Learning — Python Machine Learning. A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.

Oliver Theobald — Machine Learning For Absolute Beginners: A Plain English Introduction

Harrison Kinsley, Daniel Kukieła — Neural Networks from Scratch in Python

Simon Haykin — Neural Networks: A Comprehensive Foundation

Alan M. Turing, B. Jack Copeland — The essential Turing: seminal writings in computing, logic, philosophy, artificial intelligence, and artificial life, plus the secrets of Enigma

Christopher M. Bishop — Neural Networks for Pattern Recognition

Eugene Charniak — Introduction to Deep Learning

Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka — Elements of artificial neural networks

David McMahon — Quantum Computing Explained

Michael Taylor, Mark Koning — The Math of Neural Networks

Andrew R. Webb — Statistical pattern recognition

Martin Anthony, Peter L. Bartlett — Neural Network Learning: Theoretical Foundations

George F. Luger, William A. Stubblefield — Artificial Intelligence: Structures And Strategies For Complex Problem Solving

C.S. Krishnamoorthy, S. Rajeev — Artificial intelligence and expert systems for engineers

Martin T. Hagan et al. — Neural Network Design

Richard E. Neapolitan, Xia Jiang — Artificial Intelligence: With an Introduction to Machine Learning

Ragav Venkatesan, Baoxin Li — Convolutional Neural Networks in Visual Computing: A Concise Guide

Bramer M., Devedzic V. (eds.) — Artificial intelligence applications and innovations: IFIP 18th World Computer Congress: TC12 First International Conference on Artificial Intelligence Applications and Innovations

Dinesh P. Mehta, Sartaj Sahni (editors) — Handbook of data structures and applications

Clifford A. Pickover — Artificial Intelligence: An Illustrated History: From Medieval Robots to Neural Networks

Jeff Heaton — Introduction to the Math of Neural Networks

J. Patterson, A. Gibson — Deep Learning: A Practitioner's Approach

Tom Hope, Yehezkel S. Resheff, Itay Lieder — Learning TensorFlow

Seth Weidman — Deep Learning from Scratch

Jeff Heaton — Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms

Mark Girolami, Simon Rogers — A first course in machine learning

Jeff Heaton — Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms

Jeff Heaton — Artificial Intelligence for Humans, Volume 3

Tariq Rashid — Make Your Own Neural Network

Marvin L. — NEURAL NETWORKS with MATLAB

Dan Morris — Bayes’ Theorem Examples : A visual introduction for beginners

Juan Ramon Rabunal, Julian Dorrado — Artificial Neural Networks in Real-life Applications

Ronald T. Kneusel — Math for Deep Learning: What You Need to Know to Understand Neural Networks

Krishna Rungta — TensorFlow in 1 Day Make your own Neural Network

Sinan Ozdemir — Principles of Data Science: Learn the techniques and math you need to start making sense of your data

Най-често използвани термини

Свързани списъци с книги