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Generative Models with Pytorch

Generative models are gaining a lot of popularity recently among data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labelled data.

Pytorch is one of the most versatile Deep Learning to implement generative models. In this course, you will learn how to use Pytorch for generative models.

Course Highlights

  • Neural Transfer Using Pytorch
  • Style Transfer with GAN


All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M1044

Course Booking


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Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

Course Details

Module 1 Overview of Generative Models

  • What is Generative Models
  • Application of Generative Models
  • Types of Generative Models
  • Online Generative Model Demo with Sketch-RNN
  • Installing Google Colab

Module 2 DeepDream

  • Recap on Convolutional Neural Networks (CNN)
  • Recap on Transfer Learning
  • What is DeepDream?
  • DeepDream Applications
  • DeepDream Implementation

Module 3 Neural Style Transfer

  • What is Neural Style Transfer?
  • Neural Style Transfer Applications
  • Neural Style Transfer Implementation

Module 4 Variational Autoencoder (VAE)

  • What is Autoencoder
  • Variational Autoencoder (VAE)
  • VAE Implementation

Module 5 Generative Adversarial Networks (GAN)

  • What is GAN?
  • GAN Applications
  • Basic DCGAN Architecture
  • DCGAN Implementation
  • GAN Challenges and Tricks

Module 6 Text Generation (Optional)

  • Recap on Recurrent Neural Networks (RNN)
  • Recap on Long Short Term Memory (LSTM)
  • Char by Char Text Generation with LSTM

Course Admin


This is an intermediate course. Participants should have basic knowledge on the following subjects:

  • Python
  • Pytorch
  • Machine Learning

Software Requirement

We will use Google Colab for this training. Google Colab is a free Jupyter Notebook like software that provides free GPU for model training.

Please follow the instruction below to install Google Colab on your Google Drive

Who Should Attend

  • AI Developers
  • Artificial Intelligence Engineers
  • Data Scientists


Data Science TrainerDr. Aanand is a Full Stack Data Scientist who once had a torrid love affair with Physics. He has consulted and published in the area of Public Health, Electricity Markets, Telecom, BFSI, Advertising & Communication Strategies and Digital & Social Media Technologies. He has worked on assignments with international agencies such as International Monetary Fund, World Bank, Royal Netherland Embassy etc. besides MNCs like Tata Consultancy Services, Kie Square Consulting and several government organizations of national importance.

He regularly conducts general training programs in Python (Pandas, NumPy, SciPy, Matplotlib, Bokeh), R (dplyr, rstanarm, knitR, ggplot2), Data Visualization (Tableau, D3.js) and Machine Learnng (Reinforced Learning, Scikit Learn) and specialized training programs on Structural Equation Modeling and SAP Hana.

He holds a doctorate in Operations Research from Indian Institute of Management Ahmedabad and a post graduate in Physics from University of Mumbai. He has advanced training in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques. When he is not teaching or consulting he can be found meditating or heading for an adventurous trek.

TensorFlow TrainerDr. Nouar Dah is an experienced Lab Instructor at YPU. He has experience has a software engineer and senior hardware engineer. He has a PhD in Mechatronics Engineering from IIUM. He is well-versed in microcontrollers, FPGA, Matlab and VHDL.

Machine Learning TrainerAmir Othman is a software engineer by profession. Being educated in Bauhaus Universität Weimar and Hochschule Ulm, he brings experiences from different facades of the world.

With expertise in web technology, natural language processing and machine learning, he is a freelance data scientist. Some of his works include two international news aggregator and

He also holds an impressive port folio for data visualizations, primarily focusing on web based techniques.

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