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- 3 High Quality Generative AI Books
- Learn Generative AI Tools with Practical Projects
- From 0 to Advance Level
Why Buy This Book?
- Basic to Advance No matter what you know, these books teaches you from basic terms to advance level.
- All Concepts Covered With these books you learn each and every aspect of Generative AI.
- Self Sufficient After these books, you'll have complete clarity on Generative AI.
- Global Opportunity With global rise in demand of Generative AI subjects, opportunities are endless in terms of jobs.
What exactly do I get in this?
3 Pack Bundle
What’s Covered?
Table of content
• Introduction to Generative AI
✔ Definition
✔ Sentient?
✔ The Opportunity
✔ Using Generative AI
✔ The ChatGPT Effect
✔ The Drivers
✔ Skeptics
✔ Dangers of Hype
✔ Conclusion
• Data
✔ Value of Data
✔ The Amazing Growth of Data
✔ Big Data
✔ Databases
✔ Cloud Models
✔ Data Strategy
✔ Data Collection
✔ Common Data Sources
✔ Data Evaluation
✔ Data Wrangling
✔ Data Labeling
✔ Quantity
✔ Privacy Laws and Regulations
✔ Generative AI for Data Wrangling
✔ Generative AI for Data Preparation
✔ Chief Data Officer
✔ Conclusion
• AI Fundamentals
✔ Early AI Programs
✔ AI Struggle
✔ Then What Is AI?
✔ Machine Learning
✔ Supervised Learning
✔ Regression Analysis
✔ Support Vector Machines
✔ Random Forest
✔ K-Nearest Neighbor
✔ Naïve Bayes Classifiers
✔ Unsupervised Learning
✔ Clustering
✔ Anomaly Detection
✔ Association
✔ Autoencoders
✔ Reinforcement Learning
✔ Terms and Setup for Deep Learning
✔ How Deep Learning Works
✔ Types of Deep Learning
✔ The Brain and AI
✔ Drawbacks with Deep Learning
✔ Python
✔ AI Tools
✔ AI Systems for Beginners
✔ Conclusion
• Core Generative AI Technology
✔ Generative vs• Discriminative Models
✔ Probability Theory
✔ Types of Generative AI Models
✔ Generative Adversarial Networks (GANs)
✔ Variational Autoencoder (VAE)
✔ Diffusion Models
✔ DALL-E 2
✔ Stability AI and Midjourney
✔ Speech
✔ Trilemma of Generative AI Models
✔ Conclusion
• Large Language Models
✔ Language and Intelligence
✔ Natural Language Processing (NLP)
✔ How NLP Works
✔ Word2Vec Model
✔ Transformers
✔ Dials
✔ BERT (Bidirectional Encoder Representations from Transformers)
✔ GPT Systems and ChatGPT
✔ Dolly
✔ Gopher and Gato
✔ Cohere
✔ AI21 Labs
✔ BLOOM
✔ Megatron-Turing Natural Language Generation Model
✔ GPT-Sw3
✔ Issues
✔ New Startup Model
✔ Prompt Engineering
✔ Character AI
✔ Empathy
✔ Conclusion
• Auto Code Generation
✔ The Developer Shortage
✔ How AI Code Generation Systems Work
✔ Copilot
✔ AlphaCode
✔ Tabnine
✔ Magic
✔ PolyCoder
✔ Blaze
✔ Debugging Code
✔ Data Labeling
✔ Prompt Engineering for Coding
✔ Atera
✔ Large-Scale Projects
✔ Drawbacks and Risks
✔ Conclusion
• The Transformation of Business
✔ Legal
✔ Customer Experience
✔ Sales and Marketing
✔ Anyword
✔ Wope
✔ INK
✔ Regie•ai
✔ Lavender and SellScale
✔ Grammarly
✔ Writer
✔ Cresta
✔ Forethought
✔ Intercom
✔ Product Development
✔ Spoke AI
✔ Presentations
✔ Buy vs• Build
✔ Implementing Generative AI
✔ Identify the Problem to Be Solved
✔ Form a Team
✔ Data Preparation and AI Modeling
✔ Deploy and Monitor the AI System
✔ The Generative AI Process
✔ Conclusion
• The Impact on Major Industries
✔ Music
✔ WaveAI
✔ Education
✔ GPTZero
✔ Duolingo
✔ Journalism
✔ Gaming
✔ Roblox
✔ Healthcare
✔ Creating X-Rays
✔ Finance
✔ Conclusion
• The Future
✔ Challenges
✔ Misuse
✔ Regulation
✔ New Approaches to AI
✔ AGI
✔ Jobs
✔ Conclusion
Table of content
•Introduction to Artificial Intelligence
✔ What is AI?
✔ Why do we need to study AI?
✔ Branches of AI
✔ The five tribes of machine learning
✔ Defining intelligence using the Turing test
✔ Making machines think like humans
✔ Building rational agents
✔ General Problem Solver
✔ Solving a problem with GPS
✔ Building an intelligent agent
✔ Types of models
✔ Installing Python 3
✔ Installing on Ubuntu
✔ Installing on Mac OS X
✔ Installing on Windows
✔ Installing packages
✔ Loading data
✔ Summary
• Fundamental Use Cases for Artificial Intelligence
✔ Representative AI use cases
✔ Digital personal assistants and chatbots
✔ Personal chauffeur
✔ Shipping and warehouse management
✔ Human health
✔ Knowledge search
✔ Recommendation systems
✔ The smart home
✔ Gaming
✔ Movie making
✔ Underwriting and deal analysis
✔ Data cleansing and transformation
✔ Summary
✔ References
• Machine Learning Pipelines
✔ What is a machine learning pipeline?
✔ Problem definition
✔ Data ingestion
✔ Data preparation
✔ Missing values
✔ Duplicate records or values
✔ Feature scaling
✔ Inconsistent values
✔ Inconsistent date formatting
✔ Data segregation
✔ Model training
✔ Candidate model evaluation and selection
✔ Model deployment
✔ Performance monitoring
✔ Model performance
✔ Operational performance
✔ Total cost of ownership (TCO)
✔ Service performance
✔ Summary
• Feature Selection and Feature Engineering
✔ Feature selection
✔ Feature importance
✔ Univariate selection
✔ Correlation heatmaps
✔ Wrapper-based methods
✔ Filter-based methods
✔ Embedded methods
✔ Feature engineering
✔ Imputation
✔ Outlier management
✔ One-hot encoding
✔ Log transform
✔ Scaling
✔ Date manipulation
✔ Summary
• Classification and Regression
✔ Supervised Learning
✔ Supervised versus unsupervised learning
✔ What is classification?
✔ Preprocessing data
✔ Binarization
✔ Mean removal
✔ Scaling
✔ Normalization
✔ Label encoding
✔ Logistic regression classifiers
✔ The Naïve Bayes classifier
✔ Confusion matrices
✔ Support Vector Machines
✔ Classifying income data using Support Vector Machines
✔ What is regression?
✔ Building a single-variable regressor
✔ Building a multivariable regressor
✔ Estimating housing prices using a Support Vector Regressor
✔ Summary
• Predictive Analytics with Ensemble Learning
✔ What are decision trees?
✔ Building a decision tree classifier
✔ What is ensemble learning?
✔ Building learning models with ensemble learning
✔ What are random forests and extremely random forests?
✔ Building random forest and extremely random forest classifiers
✔ Estimating the confidence measure of the predictions
✔ Dealing with class imbalance
✔ Finding optimal training parameters using grid search
✔ Computing relative feature importance
✔ Predicting traffic using an extremely random forest regressor
✔ Summary
• Detecting Patterns with Unsupervised Learning
✔ What is unsupervised learning?
✔ Clustering data with the K-Means algorithm
✔ Estimating the number of clusters with the Mean Shift algorithm
✔ Estimating the quality of clustering with silhouette scores
✔ What are Gaussian Mixture Models?
✔ Building a classifier based on Gaussian Mixture Models
✔ Finding subgroups in the stock market using the Propagation model
✔ Segmenting the market based on shopping patterns
✔ Summary
• Building Recommender Systems
✔ Extracting the nearest neighbors
✔ Building a K-nearest neighbors classifier
✔ Computing similarity scores
✔ Finding similar users using collaborative filtering
✔ Building a movie recommendation system
✔ Summary
• Logic Programming
✔ What is logic programming?
✔ Understanding the building blocks of logic programming
✔ Solving problems using logic programming
✔ Installing Python packages
✔ Matching mathematical expressions
✔ Validating primes
✔ Parsing a family tree
✔ Analyzing geography
✔ Building a puzzle solver
✔ Summary
• Heuristic Search Techniques
✔ Is heuristic search artificial intelligence?
✔ What is heuristic search?
✔ Uninformed versus informed search
✔ Constraint satisfaction problems
✔ Local search techniques
✔ Simulated annealing
✔ Constructing a string using greedy search
✔ Solving a problem with constraints
✔ Solving the region coloring problem
✔ Building an 8puzzle solver
✔ Building a maze solver
✔ Summary
• Genetic Algorithms and Genetic Programming
✔ The evolutionist tribe
✔ Understanding evolutionary and genetic algorithms
✔ Fundamental concepts in genetic algorithms
✔ Generating a bit pattern with predefined parameters
✔ Visualizing the evolution
✔ Solving the symbol regression problem
✔ Building an intelligent robot controller
✔ Genetic programming use cases
✔ Summary
✔ References
• Artificial Intelligence on the Cloud
✔ Why are companies migrating to the cloud?
✔ The top cloud providers
✔ Amazon Web Services (AWS)
✔ Amazon SageMaker
✔ Alexa, Lex, and Polly – conversational agents
✔ Amazon Comprehend – natural language processing
✔ Amazon Rekognition – image and video
✔ Amazon Translate
✔ Amazon Machine Learning
✔ Amazon Transcribe – transcription
✔ Amazon Textract – document analysis
✔ Microsoft Azure
✔ Microsoft Azure Machine Learning Studio
✔ Azure Machine Learning Service
✔ Azure Cognitive Services
✔ Google Cloud Platform (GCP)
✔ AI Hub
✔ Google Cloud AI Building Blocks
✔ Summary
• Building Games with Artificial Intelligence
✔ Using search algorithms in games
✔ Combinatorial search
✔ The Minimax algorithm
✔ Alpha Beta pruning
✔ The Negamax algorithm
✔ Installing the easyAI library
✔ Building a bot to play Last Coin Standing
✔ Building a bot to play Tic-Tac-Toe
✔ Building two bots to play Connect Four™ against each other
✔ Building two bots to play Hexapawn against each other
✔ Summary
• Building a Speech Recognizer
✔ Working with speech signals
✔ Visualizing audio signals
✔ Transforming audio signals to the frequency domain
✔ Generating audio signals
✔ Synthesizing tones to generate music
✔ Extracting speech features
✔ Recognizing spoken words
✔ Summary
• Natural Language Processing
✔ Introduction and installation of packages
✔ Tokenizing text data
✔ Converting words to their base forms using stemming
✔ Converting words to their base forms using lemmatization
✔ Dividing text data into chunks
✔ Extracting the frequency of terms using the Bag of Words model
✔ Building a category predictor
✔ Constructing a gender identifier
✔ Building a sentiment analyzer
✔ Topic modeling using Latent Dirichlet Allocation
✔ Summary
• Chatbots
✔ The future of chatbots
✔ Chatbots today
✔ Chatbot concepts
✔ A well-architected chatbot
✔ Chatbot platforms
✔ Creating a chatbot using Dialogflow
✔ DialogFlow setup
✔ Integrating a chatbot into a website using a widget
✔ Integrating a chatbot into a website using Python
✔ How to set up a webhook in DialogFlow
✔ Enabling webhooks for intents
✔ Setting up training phrases for an intent
✔ Setting up parameters and actions for an intent
✔ Building fulfillment responses from a webhook
✔ Checking responses from a webhook
✔ Summary
• Sequential Data and Time Series Analysis
✔ Understanding sequential data
✔ Handling time series data with Pandas
✔ Slicing time series data
✔ Operating on time series data
✔ Extracting statistics from time series data
✔ Generating data using Hidden Markov Models
✔ Identifying alphabet sequences with Conditional Random Fields
✔ Stock market analysis
✔ Summary
• Image Recognition
✔ Importance of image recognition
✔ OpenCV
✔ Frame differencing
✔ Tracking objects using color spaces
✔ Object tracking using background subtraction
✔ Building an interactive object tracker using the CAMShift algorithm
✔ Optical flow based tracking
✔ Face detection and tracking
✔ Using Haar cascades for object detection
✔ Using integral images for feature extraction
✔ Eye detection and tracking
✔ Summary
• Neural Networks
✔ Introduction to neural networks
✔ Building a neural network
✔ Training a neural network
✔ Building a Perceptron-based classifier
✔ Constructing a single-layer neural network
✔ Constructing a multi-layer neural network
✔ Building a vector quantizer
✔ Analyzing sequential data using recurrent neural networks
✔ Visualizing characters in an optical character recognition database
✔ Building an optical character recognition engine
✔ Summary
• Deep Learning with Convolutional Neural Networks
✔ The basics of Convolutional Neural Networks
✔ Architecture of CNNs
✔ CNNs vs• perceptron neural networks
✔ Types of layers in a CNN
✔ Building a perceptron-based linear regressor
✔ Building an image classifier using a single-layer neural network
✔ Building an image classifier using a Convolutional Neural Network
✔ Summary
✔ Reference
• Recurrent Neural Networks and Deep Learning Models
✔ The basics of Recurrent Neural Networks
✔ Step function
✔ Sigmoid function
✔ Tanh function
✔ ReLU function
✔ Architecture of RNNs
✔ A language modeling use case
✔ Training an RNN
✔ Summary
• Creating Intelligent Agents
✔ Reinforcement Learning
✔ Understanding what it means to learn
✔ Reinforcement learning versus supervised learning
✔ Real-world examples of reinforcement learning
✔ Building blocks of reinforcement learning
✔ Creating an environment
✔ Building a learning agent
✔ Summary
• Artificial Intelligence and Big Data
✔ Big data basics
✔ Crawling
✔ Indexing
✔ Ranking
✔ Worldwide datacenters
✔ Distributed lookups
✔ Custom software
✔ The three V’s of big data
✔ Volume
✔ Velocity
✔ Variety
✔ Big data and machine learning
✔ Apache Hadoop
✔ MapReduce
✔ Apache Hive
✔ Apache Spark
✔ Resilient distributed datasets
✔ DataFrames
✔ SparkSQL
✔ Apache Impala
✔ NoSQL Databases
✔ Types of NoSQL databases
✔ Apache Cassandra
✔ MongoDB
✔ Redis
✔ Neo4j
Table of content
● Introduction
What Is Artificial Intelligence? Brain Science and Problem Solving The Turing Test and Chatterbots The History of AI The First Beginnings Logic Solves (Almost) All Problems The New Connectionism Reasoning Under Uncertainty Distributed, Autonomous and Learning Agents AI Grows Up The AI Revolution AI and Society Does AI Destroy Jobs? AI and Transportation Service Robotics Agents Knowledge-Based Systems● Propositional Logic
Syntax Semantics Proof Systems Resolution Horn Clauses Computability and Complexity Applications and Limitations● First-order Predicate Logic
Syntax Semantics Equality Quantifiers and Normal Forms Proof Calculi Resolution Resolution Strategies Equality Automated Theorem Provers Mathematical Examples Applications● Limitations of Logic
The Search Space Problem Decidability and Incompleteness The Flying Penguin Modeling Uncertainty● Logic Programming with PROLOG
PROLOG Systems and Implementations Simple Examples Execution Control and Procedural Elements Lists Self-modifying Programs A Planning Example Constraint Logic Programming● Search, Games and Problem Solving
Introduction Uninformed Search Breadth-First Search Depth-First Search Iterative Deepening Comparison Cycle Check Heuristic Search Greedy Search A-Search Route Planning with the A Search Algorithm IDA★-Search Empirical Comparison of the Search Algorithms Games with Opponents Minimax Search Alpha-Beta-Pruning Non-deterministic Games Heuristic Evaluation Functions Learning of Heuristics State of the Art Chess Go● Reasoning with Uncertainty
Computing with Probabilities Conditional Probability The Principle of Maximum Entropy An Inference Rule for Probabilities Maximum Entropy Without Explicit Constraints Conditional Probability Versus Material Implication MaxEnt-Systems The Tweety Example LEXMED, an Expert System for Diagnosing Appendicitis Appendicitis Diagnosis with Formal Methods Hybrid Probabilistic Knowledge Base Application of LEXMED Function of LEXMED Risk Management Using the Cost Matrix Performance Application Areas and Experiences Reasoning with Bayesian Networks Independent Variables Graphical Representation of Knowledge as a Bayesian Network Conditional Independence Practical Application Software for Bayesian Networks Development of Bayesian Networks Semantics of Bayesian Networks● Machine Learning and Data Mining
Data Analysis The Perceptron, a Linear Classifier The Learning Rule Optimization and Outlook The Nearest Neighbor Method Two Classes, Many Classes, Approximation Distance Is Relevant Computation Times Summary and Outlook Case-Based Reasoning Decision Tree Learning A Simple Example Entropy as a Metric for Information Content Information Gain Application of C4 5 Learning of Appendicitis Diagnosis Continuous Attributes Pruning—Cutting the Tree Missing Values Cross-Validation and Overfitting Learning of Bayesian Networks Learning the Network Structure The Naive Bayes Classifier Text Classification with Naive Bayes One-Class Learning Nearest Neighbor Data Description Clustering Distance Metrics k-Means and the EM Algorithm Hierarchical Clustering How is the Number of Clusters Determined? Data Mining in Practice The Data Mining Tool KNIME● Neural Networks
From Biology to Simulation The Mathematical Model Hopfield Networks Application to a Pattern Recognition Example Analysis Summary and Outlook Neural Associative Memory Correlation Matrix Memory The Binary Hebb Rule A Spelling Correction Program Linear Networks with Minimal Errors Least Squares Method Application to the Appendicitis Data The Delta Rule Comparison to the Perceptron The Backpropagation Algorithm NETtalk: A Network Learns to Speak Learning of Heuristics for Theorem Provers Problems and Improvements Support Vector Machines Deep Learning Nature as Example Stacked Denoising Autoencoder Other Methods Systems and Implementations Applications of Deep Learning Creativity Applications of Neural Networks● Reinforcement Learning
Introduction The Task Uninformed Combinatorial Search Value Iteration and Dynamic Programming A Learning Walking Robot and Its Simulation Q-Learning Q-Learning in a Nondeterministic Environment Exploration and Exploitation Approximation, Generalization and Convergence Applications AlphaGo, the Breakthrough in Go Curse of Dimensionality Still Have Questions?
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