Genetic, Generative to Variational: Emerging AI Algorithms
Variational, Generative, Adversarial, Genetic Algorithm & Bayesian Inferences: Beginners Course on Machine Algorithms
Note: 4.2/5 (25 notes) 10,275 students
Instructor(s): Junaid Zafar
Last update: 2022-08-03
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
- No prior experience required
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.
Who this course is for
- Computer science, engineering and research students involved in basic and applied modelling using Algorithms
- Beginners who want to keep themselves abreast with leading algorithms
- Genetic Algorithms- An Introduction
- Introduction to Genetic Algorithms
- Basic Components of Genetic Algorithm
- NLP IN CUTTING EDGE RESEARCH
- Novel Search in Genetic Algorithms (GAs)
- Novelty Search in GAs
- PyGAD- Python Library for Genetic Algorithms
- PyGAD for Genetic Algorithms- Python Package
- Implementation of Genetic Algorithms
- Python Implementation of Genetic Algorithms
- Fundamentals of Variational Auto- Encoders (VAEs)
- Building Blocks of Variational Auto- Encoders
- Introduction to Generative Adversarial Networks (GANs)
- What are GANs- Part I
- What are GANs- Part II
- How GANs work?
- Python Implementation of GANs
- Implementation of GANs in Python Framework
- Introduction to Bayesian Statistical Inference
- Bayesian Networks
- Introduction to Self Organising Maps
- Restricted Boltzmann Machine- Algorithm
- RBMs – An Introduction
- Knowledge Distillation- Algorithm for Machine Learning
- Knowledge Distillation for Dimension Reduction
- Introduction to Reinforcement Learning
- Fundamentals of Reinforcement Learning- Part I
- Fundamentals of Reinforcement Learning- Part II
- Research Algorithms- Research Dimensions
- Optimization through Graph Theory In Deep Learning
- How to use large scale graphs using Google analytics
- Understanding The Research Terminology & Research Process
- Research Ethics and Integrity
- Research Thinking From Creativity to Innovation
- Qualitative Research and Methods
- Quantitative Research and its Types
- Random, Stratified, Systematic and Clustered Sampling Techniques
- Mixed Methods and their research implications
- Why use RCTs for Trials in Research
- Publishing Research: Research Paper Writing
- Advanced Research Techniques including machine learning approaches
- Why systematic Review and Meta Analysis is important for Evidence based studies
- How a research topic is to be selected
- Selection of a Research Journals for Publishing
- How to prepare a paper/ manuscript for publication?
- Students would learn about Informed Consent & Competitive Interests
- Moving Averages, Dynamic Moving Averages and Momentum
- Why its imperative to understand disruptive innovation cycle
- Key differences between different hashing algorithms
- Graph Neural Networks
- Introduction to GNNs
- Large Language Algorithms
- Transformer Algorithms for NLP
- Kernel Algorithms
- Introduction to Kernel Algorithms
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