AI

1. Artificial Intelligence

An Introduction to Artificial Intelligence

History of Artificial Intelligence

Future and Market Trends in Artificial Intelligence

Intelligent Agents – Perceive-Reason-Act Loop

Search and Symbolic Search

Constraint-based Reasoning

Simple Adversarial Search (Game-Playing)

Neural Networks and Perceptions

Understanding Feedforward Networks

Boltzmann Machines and Autoencoders

Exploring Backpropagation

2. Deep Networks and Structured Knowledge

Deep Networks/Deep Learning

Knowledge-based Reasoning

First-order Logic and Theorem

Rules and Rule-based Reasoning

Studying Blackboard Systems

Structured Knowledge: Frames, Cyc, Conceptual Dependency

Description Logic

Reasoning with Uncertainty

Probability & Certainty-Factors

What are Bayesian Networks? 

Understanding Sensor Processing

Natural Language Processing

Studying Neural Elements

Convolutional Networks

Recurrent Networks

Long Short-Term Memory (LSTM) Networks 

3. Machine Learning and Hacking

Machine learning

Reprise: Deep Learning

Symbolic Approaches and Multiagent Systems

Societal/Ethical Concerns

Hacking and Ethical Concerns

Behaviour and Hacking

Job Displacement & Societal Disruption

Ethics of Deadly AIs

Danger of Displacement of Humanity 

The future of Artificial Intelligence

4. Natural Language Processing

Natural Language Processing 

Natural Language Processing in Python

Natural Language Processing in R

Studying Deep Learning

Artificial Neural Networks

ANN Intuition

Plan of Attack

Studying the Neuron

The Activation Function

Working of Neural Networks

Exploring Gradient Descent

Stochastic Gradient Descent

Exploring Backpropagation

5. Artificial and Conventional Neural Network

Understanding Artificial Neural Network

Building an ANN

Building Problem Description

Evaluation the ANN

Improving the ANN

Tuning the ANN

Conventional Neural Networks

CNN Intuition

Convolution Operation

ReLU Layer

Pooling and Flattening

Full Connection

Softmax and Cross-Entropy 

Building a CNN

Evaluating the CNN

Improving the CNN

Tuning the CNN

6. Recurrent Neural Network

Recurrent Neural Network

RNN Intuition

The Vanishing Gradient Problem

LSTMs and LSTM Variations

Practical Intuition

Building an RNN

Evaluating the RNN

Improving the RNN

Tuning the RNN

7. Self-Organizing Maps

Self-Organizing Maps

SOMs Intuition 

Plan of Attack

Working of Self-Organizing Maps

Revisiting K-Means

K-Means Clustering

Reading an Advanced SOM

Building an SOM

8. Boltzmann Machines

Energy-Based Models (EBM)

Restricted Boltzmann Machine

Exploring Contrastive Divergence

Deep Belief Networks

Deep Boltzmann Machines

Building a Boltzmann Machine

Installing Ubuntu on Windows

Installing PyTorch

9. AutoEncoders

AutoEncoders: An Overview

AutoEncoders Intuition

Plan of Attack

Training an AutoEncoder

Overcomplete hidden layers

Sparse Autoencoders

Denoising Autoencoders

Contractive Autoencoders

Stacked Autoencoders

Deep Autoencoders

10. PCA, LDA, and Dimensionality Reduction

Dimensionality Reduction

Principal Component Analysis (PCA)

PCA in Python

PCA in R

Linear Discriminant Analysis (LDA)

LDA in Python

LDA in R

Kernel PCA

Kernel PCA in Python

Kernel PCA in R

11. Model Selection and Boosting

K-Fold Cross Validation in Python

Grid Search in Python

K-Fold Cross Validation in R

Grid Search in R

XGBoost

XGBoost in Python

XGBoost in R