Neural Networks and Deep Learning

Main
Computers - Computer Science
Neural Networks and Deep Learning

Neural Networks and Deep Learning

Michael Nielsen

5.0 / 3.5

Πόσο σας άρεσε αυτό το βιβλίο;

Ποια είναι η ποιότητα του ληφθέντος αρχείου;

Κατεβάστε το βιβλίο για να αξιολογήσετε την ποιότητά του

Ποια είναι η ποιότητα των ληφθέντων αρχείων;

Κατηγορίες:

Computers - Computer Science

Γλώσσα:

english

Αρχείο:

PDF, 5.45 MB

IPFS:

,

english0

Διαβάστε online

Η μετατροπή σε βρίσκεται σε εξέλιξη

Η μετατροπή σε απέτυχε

Ίσως σας ενδιαφέρει

Daniel Jurafsky, James H. Martin — Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola — Dive into Deep Learning

Michael Nielsen — 神经网络与深度学习

Daniel Voigt Godoy — Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Ian Goodfellow, Yoshua Bengio and Aaron Courville — Deep Learning

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

Adrian Rosebrock — Deep Learning for Computer Vision with Python, Volume 2

Dan Jurafsky and James H. Martin — Speech and Language Processing (3rd ed. draft)

Andrew Glassner — Deep Learning, Vol. 2: From Basics to Practice

Andrew Glassner — Deep Learning, Vol. 1: From Basics to Practice

Andrew Ng — Machine Learning Yearning (Draft Version)

Michael Nielsen — Neural networks and deep learning

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.

Andrew Ng — Machine Learning Yearning

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

Simon Haykin — Neural Networks: A Comprehensive Foundation

Christopher M. Bishop — Neural Networks for Pattern Recognition

Ian Goodfellow, Yoshua Bengio, Aaron Courville — Deep Learning

Terrence J. Sejnowski — The Deep Learning Revolution: Machine Intelligence Meets Human Intelligence

Eugene Charniak — Introduction to Deep Learning

Simon J.D. Prince — Understanding Deep Learning

Marvin Minsky, Seymour A. Papert — Perceptrons: An Introduction to Computational Geometry

Christopher M. Bishop — Pattern Recognition and Machine Learning

Vladimir N. Vapnik — Statistical Learning Theory

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

Martin T. Hagan et al. — Neural Network Design

Michael A. Nielsen, Isaac L. Chuang — Quantum Computation and Quantum Information: 10th Anniversary Edition

Mahmoud Hassaballah, Ali Ismail Awad — Deep learning in computer vision: principles and applications

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

Jeff Heaton — Introduction to the Math of Neural Networks

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

Nikhil Buduma, Nicholas Locascio — Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Aurélien Géron — Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurelien Geron [Géron, Aurélien] — Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Michael Nielsen — Neural networks and deep learning

Gant Laborde — Learning Tensorflow.js: Powerful Machine Learning in JavaScript

Tariq Rashid — Make Your Own Neural Network

Michael Taylor — Neural Networks: A Visual Introduction for Beginners

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin — Learning From Data: A short course

Li Deng, Dong Yu — Deep Learning: Methods and Applications

Francois Chollet — Deep Learning with Python, 2nd Edition (Final Release)

Shanqing Cai, Stan Bileschi, Eric Nielsen — Deep Learning with JavaScript: Neural networks in TensorFlow.js

Φράσεις κλειδιά

Σχετικές λίστες βιβλίων