Artificial Intelligence By Example : Develop Machine Intelligence From Scratch Using Real Artificial Intelligence Use Cases

Publisher's Note: This edition from 2018 is outdated! A new second edition, completely updated for Python 3.x and its latest libraries, and TensorFlow 2.x, is now available. It features new and more practical examples executed on various platforms like TensorBoard, IBMQ, Google Dialogflow, Quirk, and more.Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceDevelop your own method for future AI solutionsAcquire advanced AI, machine learning, and deep learning design skillsBook DescriptionArtificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies.Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks.You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.What you will learnUse adaptive thinking to solve real-life AI case studiesRise beyond being a modern-day factory code workerAcquire advanced AI, machine learning, and deep learning designing skillsLearn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technologyUnderstand future AI solutions and adapt quickly to themDevelop out-of-the-box thinking to face any challenge the market presentsWho this book is forArtificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of artificial intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. Contents......Page 3 Preface......Page 14 1 Adaptive Thinker......Page 20 How to be an adaptive thinker......Page 21 Addressing real-life issues before coding a solution......Page 22 Step 1 – MDP in natural language......Page 23 From MDP to the Bellman equation......Page 26 Step 3 – implementing the solution in Python......Page 30 The lessons of reinforcement learning......Page 32 How to use the outputs......Page 33 Machine learning versus traditional applications......Page 37 Summary......Page 38 Further reading......Page 39 2 Think like Machine......Page 40 Technical requirements......Page 41 Designing datasets in natural language meetings......Page 42 Using the McCulloch-Pitts neuron ......Page 43 The McCulloch-Pitts neuron......Page 44 The architecture of Python TensorFlow......Page 48 Overall architecture......Page 50 Logistic function......Page 51 Softmax......Page 52 Summary......Page 56 Further reading......Page 57 3 Machine Thinking to Human Problem......Page 58 Determining what and how to measure......Page 59 Convergence......Page 61 Numerical – controlled convergence......Page 62 Evaluating a position in a chess game......Page 64 Applying the evaluation and convergence process to a business problem......Page 68 Using supervised learning to evaluate result quality......Page 70 Summary......Page 74 Further reading......Page 75 4 Unconventional Innovator......Page 76 XOR and linearly separable models......Page 77 Linearly separable models......Page 78 The XOR limit of a linear model, such as the original perceptron......Page 79 Step 1 – Defining a feedforward neural network......Page 80 Step 2 – how two children solve the XOR problem every day......Page 81 Implementing a vintage XOR solution in Python with an FNN and backpropagation......Page 85 A simplified version of a cost function and gradient descent......Page 87 Linear separability was achieved......Page 90 Applying the FNN XOR solution to a case study to optimize subsets of data......Page 92 Summary......Page 98 Further reading......Page 99 5 Machine Learning & Deep Learning......Page 100 Building the architecture of an FNN with TensorFlow......Page 101 Writing code using the data flow graph as an architectural roadmap......Page 102 The input data layer......Page 103 The hidden layer......Page 104 The output layer......Page 105 Gradient descent and backpropagation......Page 106 Running the session......Page 108 Checking linear separability......Page 109 Designing the architecture of the data flow graph......Page 110 The final source code with TensorFlow and TensorBoard......Page 112 Using TensorBoard in a corporate environment......Page 113 Will your views on the project survive this meeting?......Page 114 Summary......Page 117 References......Page 118 6 Optimizing Solutions......Page 119 Dataset optimization and control......Page 120 Designing a dataset and choosing an ML/DL model......Page 121 Agreeing on the format of the design matrix......Page 122 Dimensionality reduction......Page 124 Implementing a k-means clustering solution......Page 125 The data......Page 126 Conditioning management......Page 127 The k-means clustering program......Page 128 The mathematical definition of k-means clustering......Page 130 The goal of k-means clustering in this case study......Page 131 1 – The training dataset......Page 132 3 – The k-means clustering algorithm......Page 133 5 – Displaying the results – data points and clusters......Page 134 Test dataset and prediction......Page 135 Analyzing and presenting the results......Page 136 AGV virtual clusters as a solution......Page 137 Questions......Page 139 Further reading......Page 140 7 When & How to use AI......Page 141 Checking whether AI can be avoided......Page 142 Data volume and applying k-means clustering......Page 143 NP-hard – the meaning of P......Page 144 Random sampling......Page 145 The law of large numbers – LLN......Page 146 Using a Monte Carlo estimator......Page 147 Training the full sample training dataset......Page 148 Training a random sample of the training dataset......Page 149 Shuffling as an alternative to random sampling......Page 151 Buckets......Page 153 Access to output results......Page 154 SageMaker notebook......Page 155 Creating a job......Page 156 Running a job......Page 158 Recommended strategy......Page 159 Questions......Page 160 Further reading......Page 161 8 Revolutions & Disruptive Innovations......Page 162 Is AI disruptive?......Page 163 AI is based on mathematical theories that are not new......Page 164 Cloud server power, data volumes, and web sharing of the early 21st century started to make AI disruptive......Page 165 Inventions versus innovations......Page 166 Where to start?......Page 167 Getting started......Page 168 The header......Page 169 Implementing Google's translation service ......Page 170 Google Translate from a linguist's perspective......Page 171 Lexical field theory......Page 172 Jargon......Page 173 Translating is not just translating but interpreting......Page 174 How to check a translation......Page 175 AI as a new frontier......Page 176 Lexical field and polysemy......Page 177 Exploring the frontier – the program......Page 179 k-nearest neighbor algorithm......Page 180 The KNN algorithm......Page 181 The knn_polysemy.py program......Page 183 Implementing the KNN compressed function in Google_Translate_Customized.py......Page 185 Conclusions on the Google Translate customized experiment......Page 193 Summary......Page 194 Questions......Page 195 Further reading......Page 196 9 Getting Neurons to work......Page 197 Technical requirements......Page 198 Defining a CNN......Page 199 Initializing the CNN......Page 201 Kernel......Page 202 Intuitive approach......Page 203 Developers' approach......Page 204 Mathematical approach......Page 205 Shape......Page 206 ReLu......Page 207 Pooling......Page 209 Next convolution and pooling layer......Page 210 Dense layers......Page 211 Dense activation functions......Page 212 The goal......Page 213 Quadratic loss function......Page 214 Binary cross-entropy......Page 215 Adam optimizer......Page 216 Training dataset......Page 217 Loading the data......Page 218 Data augmentation......Page 219 Training with the classifier......Page 220 Saving the model......Page 221 Summary......Page 222 Further reading and references......Page 223 10 Applying Biomimicking to AI......Page 224 Technical requirements......Page 225 TensorFlow, an open source machine learning framework......Page 226 Does deep learning represent our brain or our mind?......Page 227 Input data......Page 229 Layer 1 – managing the inputs to the network......Page 231 Weights, biases, and preactivation......Page 232 Displaying the details of the activation function through the preactivation process......Page 235 The activation function of Layer 1......Page 237 Dropout and Layer 2......Page 238 Layer 2......Page 239 Correct prediction......Page 240 accuracy......Page 241 Cross-entropy......Page 243 Training......Page 244 Optimizing speed with Google's Tensor Processing Unit......Page 245 Summary......Page 248 Further reading......Page 249 11 Conceptual Representation Learning......Page 250 Technical requirements......Page 251 Inductive thinking......Page 252 The problem AI needs to solve......Page 253 Loading the model to optimize training......Page 255 Loading the model to use it......Page 258 Using transfer learning to be profitable or see a project stopped......Page 261 Applying the model......Page 262 Making the model profitable by using it for another problem......Page 263 The trained models used in this section......Page 264 GAP – loaded or unloaded......Page 265 GAP – jammed or open lanes......Page 268 Generalizing the Γ(gap conceptual dataset)......Page 270 Generative adversarial networks......Page 271 Generating conceptual representations......Page 272 The use of autoencoders......Page 273 The curse of dimensionality ......Page 274 Scheduling and blockchains......Page 275 Chatbots......Page 276 Summary......Page 277 Further reading......Page 278 12 Automated Planning & Scheduling......Page 279 Technical requirements......Page 280 Planning and scheduling today and tomorrow......Page 281 A real-time manufacturing revolution......Page 282 An apparel manufacturing process......Page 286 Training the CRLMM......Page 288 Food conveyor belt processing – positive pγ and negative nγ gaps......Page 289 Apparel conveyor belt processing – undetermined gaps......Page 290 The beginning of an abstract notion of gaps......Page 291 Modifying the hyperparameters......Page 293 Running a prediction program......Page 294 Building the DQN-CRLMM......Page 295 Implementing a CNN-CRLMM to detect gaps and optimize......Page 296 Q-Learning – MDP ......Page 297 The input is a neutral reward matrix......Page 298 The standard output of the MDP function......Page 299 A graph interpretation of the MDP output matrix......Page 300 The optimizer......Page 301 Implementing Z – squashing the MDP result matrix......Page 302 Implementing Z – squashing the vertex weights vector......Page 303 Finding the main target for the MDP function......Page 305 Circular DQN-CRLMM – a stream-like system that never starts nor ends......Page 307 Further reading......Page 312 13 AI & Internet of Things (IoT)......Page 313 Technical requirements......Page 314 Setting up the DQN-CRLMM model......Page 315 The dataset......Page 316 Training and testing the model......Page 317 Motivation – using an SVM to increase safety levels......Page 318 Definition of a support vector machine......Page 320 Python function ......Page 322 Finding a parking space......Page 324 Deciding how to get to the parking lot......Page 327 Support vector machine......Page 328 The itinerary graph......Page 330 The weight vector......Page 331 Questions......Page 332 References......Page 333 14 Optimizing Blockchains with AI......Page 334 Mining bitcoins......Page 335 Using cryptocurrency ......Page 336 Using blockchains......Page 337 Creating a block......Page 339 Exploring the blocks......Page 340 A naive Bayes example......Page 341 The blockchain anticipation novelty......Page 343 Step 1 the dataset......Page 344 Step 2 frequency......Page 345 Step 4 naive Bayes equation......Page 346 Gaussian naive Bayes......Page 347 The Python program......Page 348 Summary......Page 350 Questions......Page 351 Further reading......Page 352 15 Cognitive NLP Chatbots......Page 353 Intents......Page 354 Testing the subsets......Page 356 Entities......Page 357 Dialog flow......Page 359 Scripting and building up the model......Page 360 A cognitive chatbot service......Page 362 A cognitive dataset......Page 363 Cognitive natural language processing......Page 364 Activating an image + word cognitive chat......Page 366 Solving the problem ......Page 368 Implementation......Page 369 Questions......Page 370 Further reading......Page 371 16 Improve Emotional Intelligence Deficiencies of Chatbots......Page 372 Technical requirements......Page 373 Building a mind ......Page 374 How to read this chapter......Page 375 Restricted Boltzmann Machines......Page 376 The connections between visible and hidden units......Page 377 Energy-based models......Page 379 Running the epochs and analyzing the results......Page 380 Parsing the datasets......Page 382 Profiling with images......Page 384 RNN for data augmentation......Page 386 RNNs and LSTMs......Page 387 RNN, LSTM, and vanishing gradients......Page 388 Step 2 – running an RNN......Page 389 Word embedding......Page 390 The Word2vec model......Page 391 Principal component analysis......Page 394 Variance......Page 395 Eigenvalues and eigenvectors......Page 397 TensorBoard Projector......Page 399 Using Jacobian matrices......Page 400 Questions......Page 401 Further reading......Page 402 17 Quantum Computers that think......Page 403 Technical requirements......Page 404 Quantum computer speed......Page 405 Representing a qubit......Page 408 The position of a qubit ......Page 409 Radians, degrees, and rotations......Page 410 Bloch sphere......Page 411 Quantum gates with Quirk......Page 412 A quantum computer score with Quirk......Page 414 A quantum computer score with IBM Q......Page 415 Representing our mind's concepts ......Page 418 Expanding MindX's conceptual representations......Page 420 Positive thinking......Page 421 Negative thinking......Page 422 Distances......Page 424 The embedding program......Page 425 The MindX experiment......Page 427 Transformation Functions – the situation function......Page 428 Transformation functions – the quantum function......Page 430 Creating and running the score......Page 431 Using the output......Page 432 IBM Watson and scripts......Page 433 Summary......Page 434 Further reading......Page 435 Chapter 1 – Become an Adaptive Thinker......Page 436 Chapter 2 – Think like a Machine......Page 438 Chapter 3 – Apply Machine Thinking to a Human Problem......Page 439 Chapter 4 – Become an Unconventional Innovator......Page 440 Chapter 5 – Manage the Power of Machine Learning and Deep Learning......Page 442 Chapter 6 – Don't Get Lost in Techniques, Focus on Optimizing Your Solutions......Page 443 Chapter 7 – When and How to Use Artificial Intelligence......Page 445 Chapter 8 – Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies......Page 447 Chapter 9 – Getting Your Neurons to Work......Page 449 Chapter 10 – Applying Biomimicking to AI......Page 451 Chapter 11 – Conceptual Representation Learning......Page 453 Chapter 12 – Automated Planning and Scheduling......Page 455 Chapter 13 – AI and the Internet of Things......Page 456 Chapter 14 – Optimizing Blockchains with AI......Page 457 Chapter 15 – Cognitive NLP Chatbots......Page 458 Chapter 16 – Improve the Emotional Intelligence Deficiencies of Chatbots......Page 460 Chapter 17 – Quantum Computers That Think......Page 461 Index......Page 465 Be an adaptive thinker that leads the way to Artificial Intelligence About This Book AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your a .. Be An Adaptive Thinker That Leads The Way To Artificial Intelligence Key Features Ai-based Examples To Guide You In Designing And Implementing Machine Intelligence Develop Your Own Method For Future Ai Solutions Acquire Advanced Ai, Machine Learning, And Deep Learning Design Skills Book Description Artificial Intelligence Has The Potential To Replicate Humans In Every Field. This Book Serves As A Starting Point For You To Understand How Ai Is Built, With The Help Of Intriguing Examples And Case Studies. Artificial Intelligence By Example Will Make You An Adaptive Thinker And Help You Apply Concepts To Real-life Scenarios. Using Some Of The Most Interesting Ai Examples, Right From A Simple Chess Engine To A Cognitive Chatbot, You Will Learn How To Tackle The Machine You Are Competing With. You Will Study Some Of The Most Advanced Machine Learning Models, Understand How To Apply Ai To Blockchain And Iot, And Develop Emotional Quotient In Chatbots Using Neural Networks. You Will Move On To Designing Ai Solutions In A Simple Manner Rather Than Get Confused By Complex Architectures And Techniques. This Comprehensive Guide Will Be A Starter Kit For You To Develop Ai Applications On Your Own. By The End Of This Book, Will Have Understood The Fundamentals Of Ai And Worked Through A Number Of Case Studies That Will Help You Develop Business Vision. What You Will Learn Use Adaptive Thinking To Solve Real-life Ai Case Studies Rise Beyond Being A Modern-day Factory Code Worker Acquire Advanced Ai, Machine Learning, And Deep Learning Designing Skills Learn About Cognitive Nlp Chatbots, Quantum Computing, And Iot And Blockchain Technology Understand Future Ai Solutions And Adapt Quickly To Them Develop Out-of-the-box Thinking To Face Any Challenge The Market Presents Who This Book Is For Artificial Intelligence By Example Is A Simple, Explanatory, And Descriptive Guide For Junior Developers, Experienced Developers, Technology Consultants, And Those Interested In Ai Who Want To Understand The Fundamentals Of Artificial Intelligence And Implement It Practically By Devising Smart Solutions. Prior Experience With Python And Statistical Knowledge Is Essential To Make The Most Out Of This Book.