4 Weeks Only TensorFlow Training Course in Kuala Lumpur

Events in Kedah / Kedah

4 Weeks TensorFlow Training course is being delivered from November 17, 2020 - December 10, 2020 US Pacific Time.

About this Event

This event has been UPDATED since it was first published. View the UPDATED & Detailed TensorFlow Training course for beginners Information here.

4 Weeks TensorFlow Training course is being delivered from November 17, 2020 - December 10, 2020 US Pacific Time for 16 hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

  • All Published Ticket Prices are in US Dollars
  • The course will be taught in English language


4 Weeks TensorFlow Training Schedule


Features and Benefits

  • 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided training
  • Training material, instructor handouts and access to useful resources on the cloud provided
  • Practical Hands-on Lab exercises provided
  • Real-life Scenarios



  • It is recommended that participants are familiar with programming (preferably in Python), along with familiarity with statistics, algebra, and probability.
  • A prior exposure to data science would be beneficial.


Course Objectives

  • Understand TensorFlow concepts, functions, operations and the execution pipeline.
  • Understand neural networks, deep learning algorithms, and data abstraction layers.
  • Master advanced topics including convolutional neural networks, deep neural networks, recurrent neural networks, and high-level interfaces.
  • Learn how to build deep learning models in TensorFlow and interpret the results
  • Understand the fundamental concepts of artificial neural networks


Course Outline

1. Introduction to Deep Learning

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Discuss the idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go Deep
  • Real-Life use cases of Deep Learning
  • Scenarios where Deep Learning is applicable
  • The Math behind Machine Learning: Linear Algebra
  • The Math Behind Machine Learning: Statistics
  • Review of Machine Learning Algorithms
  • Reinforcement Learning
  • Underfitting and Overfitting
  • Optimization
  • Convex Optimization

2. Fundamentals of Neural Networks

  • Defining Neural Networks
  • The Biological Neuron
  • The Perceptron
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Backpropagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Activation Functions
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters

3. Fundamentals of Deep Networks

  • Defining Deep Learning
  • Defining Deep Networks
  • Common Architectural Principals of Deep Networks
  • Reinforcement Learning application in Deep Networks
  • Parameters
  • Layers
  • Activation Functions – Sigmoid, Tanh, ReLU
  • Loss Functions
  • Optimization Algorithms
  • Hyperparameters
  • Summary

4. Introduction to TensorFlow

  • What is TensorFlow?
  • Use of TensorFlow in Deep Learning
  • Working of TensorFlow
  • How to install Tensorflow
  • HelloWorld with TensorFlow
  • Running a Machine learning algorithms on TensorFlow

5. Convolutional Neural Networks (CNN)

  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

6. Recurrent Neural Networks (RNN)

  • Introduction to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

7. Restricted Boltzmann Machine(RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Variational Autoencoders
  • Deep Belief Network
Date and Time
Wed, Dec 09, 2020
9:30 - 11:30 Malaysia Time

Science & Technology

Event Type

Entrance Fee
$425.98 – $532.07