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Python: Beginner's Guide to Artificial Intelligence Copyright o 2018 Packt Publishing All rights reserved. no part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals However packt Publishing cannot guarantee the accuracy of this information First published: December 2018 Production reference: 1211218 Published by Packt Publishing Ltd Livery place 35 Livery street Birmingham B3 2PB, UK ISBN978-1-78995-732-7 Mapt napt. io Mapt is an online digital library that gives you full access to over 5, 000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website Why subscribe? Spend less time learning and more time coding with practical eBooks and Videos from over 4, 000 industry professionals Improve your learning with Skill Plans built especially for you Get a free ebook or video every month mapt is fully searchable Copy and paste, print, and bookmark content Packt. com Did you know that Packt offers e book versions of every book published, with PDF and epubfilesavailableYoucanupgradetotheebooKversionatwww.packt.comandasaprint book customer, you are entitled to a discount on the e book copy. Get in touch with us at customercare@packtpub.comformoredetails Atwww.packt.comyoucanalsoreadacollectionoffreetechnicalarticlessignupfora range of free newsletters and receive exclusive discounts and offers on Packt books and ebooks Contributors About the authors Denis rothman graduated from I'Universite Paris-Sorbonne and lUniversite Paris-Diderot writing one of the very first word2matrix embedding solutions. He began his career authoring one of the first AI cognitive nLp chatbots applied as a language teacher for moet et Chandon and other companies. he authored an ai resource optimizer for ibM and apparel producers. He then authored an Advanced Planning and Scheduling(aPs) solution used worldwide Matthew Lamons's background is in experimental psychology and deep learning. Founder and Ceo of skejul-the ai platform to help people manage their activities. Named by Gartner, Inc. as a"Cool vendor in the"Cool vendors in unified communication 2017 report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate aI tech startups based on the success of his Applied AI MasterMinds group. Matthew's global community of more than 85K are leaders in Al, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision reinforcement, and deep learning matthew invites you to join him on his mission to simplify the future and to build al for good Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher His expertise in building multilingual nlU systems and large-scale AI infrastructures has brought him to Copenhagen where he leads a large team of ai engineers as chief a Scientist at jatana Often invited to speak at ai conferences he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he's currently involved in various research projects on Quantum Computing(QC), high-performance computing(HPC), and the brain computer interaction (BCD) Abhishek Nagaraja was born and raised in India. Graduated Magna Cum Laude from the University of Illinois at Chicago, United States with a masters Degree in mechanical Engineering with a concentration in mechatronics and data Science. abhishek specializes in Keras and TensorFlow for building and evaluation of custom architectures in deep learning recommendation models. His deep learning skills and interest span computationa linguistics and nlp to build chatbots to computer vision and reinforcement learning He has been working as a data Scientist for skejul Inc. building an al-powered activity forecast engine and engaged as a Deep Learning Data Scientist with The Intelligence Factory building solutions for enterprise clients Amir Ziai is a senior data scientist at Netflix, where he works on streaming securit involving petabyte-scale machine learning platforms and applications. He has worked as a data scientist in AdTech, HealthTech, and FinTech companies. He holds a master's degree in data science from uC Berkeley Ankit Dixit is a deep learning expert at aira Matrix in Mumbai, India and having an experience of 7 years in the field of computer vision and machine learning. He is currently working on the development of full slide medical image analysis solutions in his organization. His work involves designing and implementation of various customized deep neural networks for image segmentation as well as classification tasks. he has worked with different deep neural network architectures such as vGG, ResNet, Inception, Recurrent Neural Nets (rNn) and FRCNN. He holds a masters degree in computer vision specialization he has also authored an alml book Packt is searching for authors like you Ifyou'reinterestedinbecominganauthorforPackt, and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for or submit your own idea Table of contents Preface Chapter 1: Become an Adaptive Thinker Technical requirements How to be an adaptive thinker 899 Addressing real-life issues before coding a solution 10 Step 1-MDP in natural language Step 2-the mathematical representation of the Bellman equation and MDP 14 From MDP to the Bellman equation 14 Step 3-implementing the solution in Python 18 The lessons of reinforcement learning 20 How to use the outputs 21 Machine learning versus traditional applications 25 Summary 26 Chapter 2: Think Like a Machine 27 Technical requirements 28 Designing datasets- where the dream stops and the hard work begins 29 Designing datasets in natural language meetings 29 Using the McCulloch-Pitts neuron 30 The McCulloch-Pitts neuron 31 The architecture of Python Tensor Flow 35 Logistic activation functions and classifiers 37 Overall architecture 37 Logistic classifier 38 Logistic function 38 Softmax 40 Summary 43 Chapter 3: Apply Machine Thinking to a Human Problem 44 Technical requirements 45 Determining what and how to measure 45 Convergence 47 Implicit convergence 48 Numerical- controlled convergence 48 Applying machine thinking to a human problem 50 Evaluating a position in a chess game 50 Applying the evaluation and convergence process to a business problem 54 Using supervised learning to evaluate result qualit 56 Summary 60 Table of contents Chapter 4: Become an Unconventional Innovator 61 Technical requirements 62 The XOR limit of the original perceptron 62 XOR and linearly separable models 62 Linearly separable models 63 The XOR limit of a linear model, such as the original perceptron 64 Building a feedforward neural network from scratch 64 Step 1- Defining a feedforward neural network 65 Step 2-how two children solve the XoR problem every day 66 Implementing a vintage XOR solution in Python with an FNN and backpropagation 70 A simplified version of a cost function and gradient descent Linear separability was achieved 75 Applying the FNN XOR solution to a case study to optimize subsets f data Summary 83 Chapter 5: Manage the Power of Machine Learning and Deep Learning 84 Technical requirements 85 Building the architecture of an fnn with TensorFlow 85 Writing code using the data flow graph as an architectural roadmap 86 a data flow graph translated into source code 87 The input data layer 87 The hidden layer 88 The output layer 89 The cost or loss function 90 Gradient descent and backpropagation 90 Running the session 92 Checking linear separability 93 Using TensorBoard to design the architecture of your machine learning and deep learning solutions 94 Designing the architecture of the data flow graph 94 Displaying the data flow graph in Tensor Board 96 The final source code with tensorflow and tensor board 96 Using TensorBoard in a corporate environment 97 Using Tensor Board to explain the concept of classifying customer products to a ceo 98 Q Sun ill your views on the project survive this meeting? 98 mmary 101 hapter 6: Focus on Optimizing Your Solutions 102 Technical requirements 103 Dataset optimization and control 103 Designing a dataset and choosing an ML/DL model 104 Approval of the design matrix 105 Agreeing on the format of the design matrix 105 Dimensionality reduction 107 The volume of a training dataset 108 Table of contents Implementing a k-means clustering solution 108 The vision 109 The data 109 Conditioning management 110 The strategy 111 The k-means clustering program 111 The mathematical definition of k-means clustering 113 Lloyd's algorithm 114 The goal of k-means clustering in this case study The Python program 115 1-The training dataset 115 2-Hyperparameters 116 3-The k-means clustering algorithm 116 4-Defining the result labels 117 5-Displaying the results-data points and clusters 117 Test dataset and prediction 118 Analyzing and presenting the results AGV virtual clusters as a solution 120 Summary 122 Chapter 7: When and How to Use artificial Intelligence 123 Technical requirements 124 Checking whether Al can be avoided 124 Data volume and applying k-means clustering 125 Proving your point 126 NP-hard- the meaning of P 126 NP-hard- The meaning of non-deterministic 127 The meaning of hard 127 Random sampling 127 The law of large numbers-LLN 128 The central limit theorem 129 Using a monte carlo estimator 129 Random sampling applications 130 Cloud solutions-AWs 130 reparing your baseline model 130 Training the full sample training dataset 130 Training a random sample of the training dataset 131 Shuffling as an alternative to random sampling 133 AWs-data management 135 Buckets 35 Uploading fil 136 Access to output results 136 Sage Maker notebook 137 Creating a job 138 Running a job 140 Reading the results 141 Recommended strategy 142 Summary 142

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