Genetic Algorithms, Vaes & Gans In Dnns- An Introduction

Genetic Algorithms, Variational AutoEncoders, RL, Generative Adversarial Networks & Bayesian Statistics in Deep Learning

What you'll learn
Introduction to Genetic Algorithms
Implementation of Genetic Algorithms in Python
Generative Adversarial Networks & Variational Auto-encoders (VAEs)
Introduction to Statistical Inference using Bayesian Networks
Genetic Algorithms for Hyper- Parameters Optimisation
Introduction to Reinforcement Learning & Implementation in Python
Requirements
No prior experience required
Description
This course will provide a prospect for participants to establish or progress their considerate on the Genetic Algorithms, GANs and Variational Auto- encoders and their implementation in Python framework. This course encompasses algorithm processes, approaches, and application dimensions.Genetic algorithm which reflects the process of natural selection though selection of fittest individuals is explained thoroughly. Further its implementation in Python Library is exhibited step- wise. Similarly, Generative Adversarial Networks, or GANs for short, are introduced as an approach to generative modelling. Generative modelling is explained as an unsupervised learning task to generate or output new examples that plausibly could have been drawn from the original dataset. Both the Generator and Discriminator modules are explained in Depth. The two models are explained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.The course introduces elements of the research process within quantitative, qualitative, and mixed methods domains. Participants will use these underpinnings to begin to critically understand design thinking and its large-scale optimization. They would be able to develop an understanding to formulate a research question and answer it by framing an effective research methodology based on suitable methodologies. Furthermore, they would learn to derive meaningful inferences and to put them together in the form of a quality research paper. In the last few years, deep learning based generative models have gained more and more interest due to (and implying) some amazing improvements in the field. Relying on huge amount of data, well-designed networks architectures and smart training techniques, deep generative models have shown an incredible ability to produce highly realistic pieces of content of various kind, such as images, texts and sounds. Among these deep generative models, two major families stand out and deserve a special attention: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).The key topics covered in this course are;1. An Introduction to Genetic Algorithms.2. Implementation of Genetic Algorithms in Python using case examples. 3. Framing a hypothesis based on the nature of the study.4. An Introduction to Generative Adversarial Networks (GANs). 5. Implementations of GANs in Python. 6.  Meta-Analysis & Large Scale Graph Mining.7. Design Thinking Using Immersion and Sense-Making.8. An Introduction to Reinforcement Learning Algorithms in Deep Learning. 9. An Introduction Bayesian Statistical Inferences. 10. An Introduction to Autoencoders.11. Concept of latent space in Variational Auto- Encoders (VAEs).12. Regularisation and to generate new data from VAEs.

Overview

Section 1: Genetic Algorithms- An Introduction

Lecture 1 Introduction to Genetic Algorithms

Lecture 2 Basic Components of Genetic Algorithm

Lecture 3 NLP IN CUTTING EDGE RESEARCH

Section 2: Novel Search in Genetic Algorithms (GAs)

Lecture 4 Novelty Search in GAs

Section 3: PyGAD- Python Library for Genetic Algorithms

Lecture 5 PyGAD for Genetic Algorithms- Python Package

Section 4: Implementation of Genetic Algorithms

Lecture 6 Python Implementation of Genetic Algorithms

Section 5: Fundamentals of Variational Auto- Encoders (VAEs)

Lecture 7 Building Blocks of Variational Auto- Encoders

Section 6: Introduction to Generative Adversarial Networks (GANs)

Lecture 8 What are GANs- Part I

Lecture 9 What are GANs- Part II

Lecture 10 How GANs work?

Section 7: Python Implementation of GANs

Lecture 11 Implementation of GANs in Python Framework

Section 8: Introduction to Bayesian Statistical Inference

Lecture 12 Bayesian Networks

Section 9: Introduction to Reinforcement Learning

Lecture 13 Fundamentals of Reinforcement Learning- Part I

Lecture 14 Fundamentals of Reinforcement Learning- Part II

Section 10: Research Algorithms- Research Dimensions

Lecture 15 Optimization through Graph Theory In Deep Learning

Lecture 16 How to use large scale graphs using Google analytics

Lecture 17 Understanding The Research Terminology & Research Process

Lecture 18 Research Ethics and Integrity

Lecture 19 Research Thinking From Creativity to Innovation

Lecture 20 Qualitative Research and Methods

Lecture 21 Quantitative Research and its Types

Lecture 22 Random, Stratified, Systematic and Clustered Sampling Techniques

Lecture 23 Mixed Methods and their research implications

Lecture 24 Why use RCTs for Trials in Research

Section 11: Publishing Research: Research Paper Writing

Lecture 25 Advanced Research Techniques including machine learning approaches

Lecture 26 Why systematic Review and Meta Analysis is important for Evidence based studies

Lecture 27 How a research topic is to be selected

Lecture 28 Selection of a Research Journals for Publishing

Lecture 29 How to prepare a paper/ manuscript for publication?

Lecture 30 Students would learn about Informed Consent & Competitive Interests

Lecture 31 Moving Averages, Dynamic Moving Averages and Momentum

Lecture 32 Why its imperative to understand disruptive innovation cycle

Lecture 33 Key differences between different hashing algorithms

Section 12: Graph Neural Networks

Lecture 34 Introduction to GNNs

Lecture 35 Large Language Algorithms

Lecture 36 Transformer Algorithms for NLP

Section 13: Kernel Algorithms

Lecture 37 Introduction to Kernel Algorithms

Computer science, engineering and research students involved in basic and applied modelling using Algorithms,Beginners who want to keep themselves abreast with leading algorithms