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BL
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| Record Number
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856601
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| Main Entry
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Rothman, Denis
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| Title & Author
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Artificial intelligence by example : : develop machine intelligence from scratch using real artificial intelligence use cases /\ Denis Rothman.
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| Publication Statement
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Birmingham :: Packt Publishing,, [2018]
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, ©2018
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| Page. NO
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1 online resource (xi, 458 pages)
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| ISBN
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1788990021
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: 1788990544
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: 9781788990028
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: 9781788990547
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9781788990547
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| Notes
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Using TensorBoard to explain the concept of classifying customer products to a CEO.
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| Bibliographies/Indexes
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Includes bibliographical references, webology and index.
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| Contents
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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.
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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.
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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.
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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.
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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.
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| Abstract
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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.
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| Subject
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Application software-- Development.
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Artificial intelligence-- Data processing.
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Cloud computing.
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Python (Computer program language)
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Application software-- Development.
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Artificial intelligence-- Data processing.
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Artificial intelligence.
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Cloud computing.
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Computers-- Intelligence (AI) Semantics.
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Computers-- Machine Theory.
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Computers-- Neural Networks.
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Machine learning.
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Mathematical theory of computation.
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Neural networks fuzzy systems.
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Python (Computer program language)
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| Dewey Classification
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006.3
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| LC Classification
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Q335.R684 2018
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