Introduction to AI Agents
PURPOSE This document serves as an introduction to AI Agents and outlines the foundational components required to build one. As we step into…
Deep Reinforcement Learning
Preface In classical reinforcement learning, you learnt about the agent : environment interaction, Markov Decision Process, dynamic…
Model Free Methods
Preface Monte-Carlo Methods * Points to consider Monte-Carlo Prediction Monte-Carlo Control Off Policy Importance Sampling Temporal…
Model Based Methods - Dynamic Programming
Preface Dynamic Programming Policy Iteration Steps Algorithm Policy Evaluation - Prediction * Points to consider Policy Improvement…
Fundamental Equations of Reinforcement Learning
RL Equations - State Value Function Expected value Questions RL Equations - Action Value Function Example Questions Understanding the RL…
Classical Reinforcement Learning
Preface The Evolution of RL What is Reinforcement Learning Questions Agent-Environment Interaction Example Two Types of Tasks Questions…
NLP - Topic Modelling
Preface Topic Modelling Applications How it works Question Algorithms Non-Negative Matrix Factorisation Question Process Preface Suppose you…
NLP - Semantic Processing
Preface Knowledge Graphs Types of Relationships Word Sense Disambiguation References Preface Syntactic processing involved the study of…
NLP - Distributional Semantics
Preface Distributional Semantics Geometric Representation Cosine Similarity Machine Learning Approach Types of Models Binary Classification…
NLP - Parsing
Preface Constituency Parsing * Question Dependency Parsing References Preface A key task in syntactic processing is parsing. It means to…
NLP - Named Entity Recognition
Preface Named Entity Recognition Noun POS Tags Simple rule-based NER tagger IOB Labelling Sequence Labelling Conditional Random Field CRF…
NLP - Syntactic Processing
Preface Syntactic Processing Applications Lexical Processing vs Syntactic Processing POS Tagging Open Class Closed Class POS Tagging Model…
NLP - Advanced Lexical Processing
Purpose Canonicalisation Phonetic Hashing Edit Distance Spell Corrector Pointwise Mutual Information Summary Purpose Even after basic…
NLP - Lexical Processing
Preface Word Frequencies and Stop Words Tokenisation Bag-of-Words Representation Stemming and Lemmatization Stemming Lemmatization TF-IDF…
NLP - Regular Expressions
Preface Areas of Application Understanding Text Text Encoding Encoding Standard Regular Expressions Quantifiers White Space Parentheses Pipe…
Modifications to Neural Networks
Purpose Deep Learning is a subset of machine learning and hence it borrows a lot of common use cases from it. Here we will be looking at the…
Backpropagation
Neural Network Topology Training a Neural Network Mathematically Complexity of the Loss Function Example Gradient Descent Questions Gradient…
Convolutional Neural Networks (CNNs)
Purpose CNN - Introduction Challenges in Image Processing CNNs - A specialised architecture for visual data Applications of CNNs Receptive…
Long, Short-term Memory, Gated Recurrent Unit
Bi-Directional RNN Problems with Vanilla RNNs Long, Short-term Memory Networks Characteristics of an LSTM Cell LSTM Cell Common Activation…
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks What are Sequences RNN Formulation Architecture of RNN The flow of information in RNNs is as follows Example…