Life Of A.I A Complete Guide

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Overview of Artificial Intelligence What is AI ? Importance of AI Speech Recognition Understanding Natural Language Computer Vision Expert Systems Heuristic Classification Early work in AI Logic/Mathematics Computation Psychology / Cognitive Science Biology / Neuroscience Evolution Natural Language Processing Common Techniques AI and related fields Searching And-Or graphs Constraint Satisfaction Search Forward Checking Most-Constrained Variables Heuristic Repair Local Search and Metaheuristics Exchanging Heuristics Iterated Local Search Tabu Search Translating between English and Logic Notation Truth Tables Complex Truth Tables Tautology Equivalence Propositional Logic Predicate Calculus First-Order Predicate Logic Soundness Completeness Reasoning in Modal Logic Possible world representations Dempster- Shafer theory Fuzzy Set Theory Fuzzy Set Fuzzy Membership Fuzzy Operations Fuzzy Properties Fuzzy Relations 3.1 Definition of Fuzzy Relation Forming Fuzzy Relations Max-Min and Min-Max Composition Fuzzy Systems Fuzzy Logic Classical Logic Fuzzification Fuzzy Inference Fuzzy Rule Based System Defuzzification Centroid method Probabilistic Reasoning Bayesian probabilistic inference Definition and importance of knowledge Knowledge Based Systems Representation of knowledge Knowledge Organization Knowledge Manipulation Matching techniques: Measure for Matching Distance Metrics Matching like Patterns The RETE matching algorithm Natural Language Processing : Overview of linguistics Morphological Analysis BNF Basic parsing techniques Augmented Transition Networks Chart Parsing Semantic Analysis Rules for Knowledge Representation Conflict Resolution Rule-Based Expert Systems Architecture of an Expert System The Expert System Shell Knowledge Engineering CLIPS (C Language Integrated Production System) Backward Chaining in Rule-Based Expert Systems CYC AI Deep Learning Frameworks for DS How AI DL systems work AI Main deep learning frameworks AI Main deep learning programming languages How to leverage DL frameworks ETL processes for DL AI Deploying data models AI Assessing a deep learning framework Interpretability Model maintenance AI Building a DL Network Using MXNet Core components Datasets description Classification for mxnet Creating checkpoints for models developed in MXNet Artificial Intelligence Building an Optimizer Based on the Particle Swarm Optimization Algorithm PSO algorithm for AI Firefly optimizer PSO PSO versus other optimization methods AI Maximizing an exponential expression AI Building an Advanced Deep Learning System Standard Genetic Algorithm GAs in action for AI AI Advanced Building Deep Learning System AI CNN components Data flow and functionality CNN Training process AI Visualization of a CNN model Recurrent Neural Networks AI Alternative Frameworks in DS AI Extreme Learning Machines (ELMs) AI Motivation behind ELMs AI Architectures of ELMs AI Capsule Networks AI Motivations behind CapsNets AI Fully connected layer AI Dynamic routing between capsules Fuzzy sets Python Big data Hadoop Apache Spark Machine Learning for AI AI Perceptron & Neural Networks Artificial Intelligence Decision Trees AI Support Vector Machines AI Probabilistic Models AI Dynamic Programming and Reinforcement Learning AI Evolutionary Algorithms AI Time Series Models Artificial Intelligence The Nature of Language Mathematics for AI Algebraic Structures for AI Linear Algebra for AI Internet of Things (IOT) What is the IOT? IOT Programming Connected Devices IOT Digital Switches IOT User Defined Functions Ardos IOT IOT Programming Raspberry Pi with C and Python IOT Python Hellothere.py IOT Python Functions IOT Installation of Vim IOT Programming in C IOT Installing Wiring Pi IOT Raspberry Pi with Raspbian Operating System How to Setup the Raspbian Operating System File System Layout IOT IOT Programming in Raspberry Galileo, Windows, and the IOT IOT Creating the Server Applications IOT Temperature Controller IOT Creation of Tables and Controllers IOT Seeding the Database IOT Custom APIs Conclusion Python for Artificial Intelligence Agents and Control for AI AI Representing Search Problems AI Reasoning with Constraints Deep Learning for AI Introduction 1.2 Historical Trends in Deep Learning 1.2.1 The Many Names and Changing Fortunes of Neural Networks 1.2.2 Increasing Dataset Sizes 1.2.3 Increasing Model Sizes 1.2.4 Increasing Accuracy, Complexity and Real-World Impact Applied Math and Machine Learning Basics Linear Algebra 2.1 Scalars, Vectors, Matrices and Tensors 2.3 Identity and Inverse Matrices 2.4 Linear Dependence and Span 2.5 Norms 2.6 Special Kinds of Matrices and Vectors 2.7 Eigendecomposition 2.8 Singular Value Decomposition 2.9 The Moore-Penrose Pseudoinverse 2.10 The Trace Operator 2.11 The Determinant 2.12 Example: Principal Components Analysis Probability and Information Theory 3.1 Why Probability? 3.2 Random Variables 3.3 Probability Distributions 3.3.1 Discrete Variables and Probability Mass Functions 3.4 Marginal Probability 3.5 Conditional Probability 3.6 The Chain Rule of Conditional Probabilities 3.7 Independence and Conditional Independence 3.8 Expectation, Variance and Covariance 3.9 Common Probability Distributions 3.9.1 Bernoulli Distribution 3.9.2 Multinoulli Distribution 3.9.3 Gaussian Distribution 3.9.4 Exponential and Laplace Distributions 3.9.5 The Dirac Distribution and Empirical Distribution 3.9.6 Mixtures of Distributions 3.10 Useful Properties of Common Functions 3.11 Bayes’ Rule 3.12 Technical Details of Continuous Variables 3.13 Information Theory Reinforcement Learning 1. Introduction 1.1 Reinforcement Learning 1.2 Examples 1.3 Elements of Reinforcement Learning 1.4 An Extended Example: Tic-Tac-Toe 1.5 Summary 1.6 History of Reinforcement Learning 2. Evaluative Feedback 2.1 An -Armed Bandit Problem 2.2 Action-Value Methods 2.3 Softmax Action Selection 2.4 Evaluation Versus Instruction 2.5 Incremental Implementation 2.6 Tracking a Nonstationary Problem 2.7 Optimistic Initial Values 2.8 Reinforcement Comparison 2.9 Pursuit Methods 2.10 Associative Search 2.11 Conclusions 3. The Reinforcement Learning Problem 3.1 The Agent-Environment Interface 3.2 Goals and Rewards 3.3 Returns 3.4 Unified Notation for Episodic and Continuing Tasks 3.5 The Markov Property 3.6 Markov Decision Processes 3.7 Value Functions 3.8 Optimal Value Functions 3.10 Summary Reinforcement Learning algorithms — an intuitive overview Terminologies Model-Free vs Model-Based Reinforcement Learning I. Model-free RL I.1. Policy optimization or policy-iteration methods I.1.1. Policy Gradient (PG) I.1.2. Asynchronous Advantage Actor-Critic (A3C) I.1.3. Trust Region Policy Optimization (TRPO) I.1.4. Proximal Policy Optimization (PPO) I.2. Q-learning or value-iteration methods I.2.1 Deep Q Neural Network (DQN) I.2.2 C51 I.2.3 Distributional Reinforcement Learning with Quantile Regression (QR-DQN) I.2.4 Hindsight Experience Replay (HER) I.3 Hybrid II.1. Learn the Model Asynchronous Advantage Actor Critic (A3C) algorithm Advantages: Role of AI in Autonomous Driving Virtual Assistants in Desktop Environments Virtual Assistants in Mobile Contexts Virtual Assistants and the Internet of Things Virtual Assistants as a type of (Disembodied) Robot Virtual Assistants as Social Robots The Place for AI in Autonomous Driving: AI in “safety-related” Autonomous Driving Functionalities is based on Standards Various approaches and products Communication Displays In-Car Virtual Assistants Gateway to IoT (e.g. home control) Customised Infotainment Personal Health and Well-being Recent Strategic Developments Highlights on AI and Self-Driving Cars Highlights on AI and Self-Driving Cars Highlights on AI and Self-Driving Cars Highlights on AI and Self-Driving Cars Highlights on AI and Self-Driving Cars Social Robots, AI and Self-Driving Cars Social Robots, AI and Self-Driving Cars AI & Industry 4.0 Some Recent Developments ARTIFICIAL INTELLIGENCE AND ROBOTICS