Genetic Algorithms, VAEs & GANs in DNNs- An Introduction
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.
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
Instructor
![Junaid Zafar](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.0 Instructor Rating
- 441 Reviews
- 43,839 Students
- 9 Courses
Prof. Dr. Engr. Junaid Zafar is currently working as Chairperson in Department of Electrical and Computer Engineering, Government College University, Lahore. He is also Director, Office of Research, innovation and Commercialization. He has completed his PhD in Electrical and Electronics Engineering, The University of Manchester University, UK, and BSc in Electrical Engineering from U.E.T Lahore. He is Academic visitor to the University of Cambridge, UK, MMU, UK and National University of Ireland. He remained Dual Degree programme coordinator at the Lancaster University, UK. Dr. Engr. Junaid Zafar received Roll of Honors for National Education Commission and Outstanding Teacher/ Researcher Awards from the Higher Education Commission, Pakistan. He is leading the macine learning and Artificial Intelligence centre with GC University, Lahore. He is member of Universal Association of Electronics & Computer Engineers, International Association of Computer Science & Information, and member of International Association of Engineers, IAENG Society of Artificial Intelligence, IAENG Society of Electrical Engineering, Science & Engineering Institute, IAENG Society of Imaging Engineering, Institute of Research Engineers & Doctors, and IAENG Society of Wireless Networks. He is member of editorial board in Journal of Future Technologies & Communications, Technical Programme committee, Frontiers of Information & Technologies, and Technical Programme Committee, Multi- Conference on Sciences & Technology. He is also serving as reviewer for IEEE Transactions on Microwave Theory & Techniques, IEEE Transactions on Antennas, IEEE Antenna & Wireless Propagation Letters, IEEE Transactions on Plasma Science, IEEE Transactions on Magnetics, International Journal of Electronics, and IET Antennas & Radio- wave Propagation. He has so far taught over twenty diffrent online courses based on outcome based student oriented models. He has also supervised more than 100 Masters/ MPhil thesis. He has published over 50 high impact factor publications and presented his work at several national and international renowned platforms.