Genetic Algorithms, Vaes & Gans In Dnns- An Introduction

本课程将为参与者提供一个前景，以建立或推进他们对遗传算法、GAN 和变分自动编码器及其在 Python 框架中的实施的思考。本课程包括算法过程、方法和应用维度。详细解释了通过选择最适合的个体来反映自然选择过程的遗传算法。此外，它在 Python 库中的实现是逐步展示的。类似地，生成对抗网络（简称 GAN）被引入作为生成建模的一种方法。生成式建模被解释为一种无监督学习任务，用于生成或输出可能从原始数据集中提取的新示例。生成器和鉴别器模块都在深度中进行了解释。

这两个模型在零和游戏中一起解释，对抗性，直到鉴别器模型被愚弄了一半，这意味着生成器模型正在生成似是而非的例子。本课程介绍了定量、定性和混合研究过程的要素方法域。参与者将利用这些基础知识开始批判性地理解设计思维及其大规模优化。他们将能够理解以制定研究问题并通过基于适当方法的有效研究方法来回答它。

此外，他们将学会得出有意义的推论，并以高质量的研究论文的形式将它们组合在一起。在过去的几年里，基于深度学习的生成模型由于（并暗示）该领域的一些惊人改进而引起了越来越多的兴趣。依靠海量数据、精心设计的网络架构和智能训练技术，深度生成模型显示出令人难以置信的能力，可以生成各种高度逼真的内容片段，例如图像、文本和声音。

在这些深度生成模型中，两大家族脱颖而出，值得特别关注：生成对抗网络 (GAN) 和变分自动编码器 (VAE)。本课程涵盖的关键主题是：1。遗传算法简介.2。使用案例示例在 Python 中实现遗传算法。3. 根据研究的性质提出假设。 4．生成对抗网络 (GAN) 简介。5. GAN 在 Python 中的实现。6. 元分析和大规模图挖掘。7。使用沉浸式和Sense-Making.8 的设计思维。深度学习中的强化学习算法简介。9. 贝叶斯统计推断简介。10. 自动编码器简介.11。变分自动编码器 (VAE).12 中潜在空间的概念。

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

Last updated 7/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.97 GB | Duration: 5h 1m

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