عنوان
پدید آورنده
موضوع
رده
Q335 .R684 2018
Q335
.
R684
2018
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1788990021
(Number (ISBN
1788990544
(Number (ISBN
9781788990028
(Number (ISBN
9781788990547
Erroneous ISBN
9781788990547
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Artificial intelligence by example :
General Material Designation
[Book]
Other Title Information
develop machine intelligence from scratch using real artificial intelligence use cases /
First Statement of Responsibility
Denis Rothman.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Birmingham :
Name of Publisher, Distributor, etc.
Packt Publishing,
Date of Publication, Distribution, etc.
[2018]
Date of Publication, Distribution, etc.
©2018
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xi, 458 pages)
GENERAL NOTES
Text of Note
Using TensorBoard to explain the concept of classifying customer products to a CEO.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references, webology and index.
CONTENTS NOTE
Text of Note
Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Become an Adaptive Thinker; Technical requirements; How to be an adaptive thinker; Addressing real-life issues before coding a solution; Step 1 -- MDP in natural language; Step 2 -- the mathematical representation of the Bellman equation and MDP; From MDP to the Bellman equation; Step 3 -- implementing the solution in Python; The lessons of reinforcement learning; How to use the outputs; Machine learning versus traditional applications; Summary; Questions; Further reading.
Text of Note
Chapter 2: Think like a MachineTechnical requirements; Designing datasets -- where the dream stops and the hard work begins; Designing datasets in natural language meetings; Using the McCulloch-Pitts neuron ; The McCulloch-Pitts neuron; The architecture of Python TensorFlow; Logistic activation functions and classifiers; Overall architecture; Logistic classifier; Logistic function; Softmax; Summary; Questions; Further reading; Chapter 3: Apply Machine Thinking to a Human Problem; Technical requirements; Determining what and how to measure; Convergence; Implicit convergence.
Text of Note
Numerical -- controlled convergenceApplying machine thinking to a human problem; Evaluating a position in a chess game; Applying the evaluation and convergence process to a business problem; Using supervised learning to evaluate result quality; Summary; Questions; Further reading; Chapter 4: Become an Unconventional Innovator; Technical requirements; The XOR limit of the original perceptron; XOR and linearly separable models; Linearly separable models; The XOR limit of a linear model, such as the original perceptron; Building a feedforward neural network from scratch.
Text of Note
Step 1 -- Defining a feedforward neural networkStep 2 -- how two children solve the XOR problem every day; Implementing a vintage XOR solution in Python with an FNN and backpropagation; A simplified version of a cost function and gradient descent; Linear separability was achieved; Applying the FNN XOR solution to a case study to optimize subsets of data; Summary; Questions; Further reading; Chapter 5: Manage the Power of Machine Learning and Deep Learning; Technical requirements; Building the architecture of an FNN with TensorFlow.
Text of Note
Writing code using the data flow graph as an architectural roadmapA data flow graph translated into source code; The input data layer; The hidden layer; The output layer; The cost or loss function; Gradient descent and backpropagation; Running the session; Checking linear separability; Using TensorBoard to design the architecture of your machine learning and deep learning solutions; Designing the architecture of the data flow graph; Displaying the data flow graph in TensorBoard; The final source code with TensorFlow and TensorBoard; Using TensorBoard in a corporate environment.
0
8
8
8
8
SUMMARY OR ABSTRACT
Text of Note
Artificial Intelligence(AI), gets your system to think smart and intelligent. This book is packed with some of the smartest and easy-peasy examples through which you will learn the fundamentals of AI. You will have acquired the foundation of AI and understood the practical case studies in this book.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Packt Publishing
Stock Number
9781788990028
OTHER EDITION IN ANOTHER MEDIUM
Title
Artificial Intelligence By Example : Develop machine intelligence from scratch using real artificial intelligence use cases.
TOPICAL NAME USED AS SUBJECT
Application software-- Development.
Artificial intelligence-- Data processing.
Cloud computing.
Python (Computer program language)
Application software-- Development.
Artificial intelligence-- Data processing.
Artificial intelligence.
Cloud computing.
Computers-- Intelligence (AI) & Semantics.
Computers-- Machine Theory.
Computers-- Neural Networks.
Machine learning.
Mathematical theory of computation.
Neural networks & fuzzy systems.
Python (Computer program language)
(SUBJECT CATEGORY (Provisional
COM-- 004000
DEWEY DECIMAL CLASSIFICATION
Number
006
.
3
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
Q335
Book number
.
R684
2018
PERSONAL NAME - PRIMARY RESPONSIBILITY
Rothman, Denis
ORIGINATING SOURCE
Date of Transaction
20200823060520.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
