30
May 2020

2-Day Deep Learning with TensorFlow(LIVE Stream)

Course Information

Start Date30 May 2020, Saturday
End Date31 May 2020, Sunday
Time09:00 am - 05:00 pm
VenueOnline LIVE Stream via ZOOM
Fee$1,200 (Excluding GST) Inclusive of e-certificate, e-materials
Contact6720 3333 (Ms Rina Lim) training.aventis@gmail.com
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LIVE Stream

INTRODUCTION

The Complete Guide to Understanding TensorFlow for Machine & Deep Learning for Artificial Intelligence

Tensorflow is the Most Popular Open Source Deep Learning Framework (Google)

Much of the world’s data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.

Tensorflow is the most popular and powerful open source machine learning/deep learning framework developed by Google for everyone. It is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them.

Tensorflow has many powerful Machine Learning API such as Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Word Embedding, Seq2Seq, Generative Adversarial Networks (GAN), Reinforcement Learning, and Meta Learning.

Learn & Add this In-Demand Skills to Your Skills Set

Gain the core knowledge to Tensorflow for Deep Learning and Machine Learning. Let Data Scientist, Dr Sudipta share provide you with the core foundation of TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Upon wrapping up this course, you’ll have the knowledge you need to continue your coding journey in whichever language piques your interest.

Key Takeaways

In this hands-on 2 Day TensorFlow course, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

  • Understanding of the subtle differences between machine and deep learning
  • Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
  • Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
  • Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
  • Understand how Neural Networks Work for Classification and Regression Tasks
  • Learn Basic Tensorflow 2 operations
  • How to use TensorFlow for Time Series Analysis with Recurrent Neural Networks
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
  • Create Generative Adversarial Networks with TensorFlow
  • Understand how to build neural network models using tensorflow and keras on particular use cases
  • Recurrent Neural Network for Sequential Data
Who Should Attend?
  • Individuals with a basic understanding of Python Programming and are seeking more advanced coding skills.
  • Machine Learning Engineers, and Developers, Data Analyst, Programmers, IT Engineers & Data Scientist.

 

Note: Participant is required to bring their own laptop with access to internet (WiFi network will be provided)

Data Scientist: Dr Sudipta Samanta, Senior Research Engineer at Temasek Life Sciences Lab

Dr. Sudipta Samanta has over 15 years of research and teaching experience. Dr Sudipta received his Ph.D. for his research in the field of computational Biophysics and has over 10 years of research experience as a Research Scientist. His current interests include Machine Learning, Deep Learning, Health Care Data analysis and computer simulation. Prior to joining Temasek, Dr Sudipta was Visiting Scientist of Internationally renowned MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA from 2011 to 2018 and a Research Scientist at Singapore-MIT Alliance for Research & Technology.

Workshop Outline

Day 1
Topic 1 Overview of Machine Learning & Tensorflow
• Machine Learning vs Deep Learning
• Introduction to Tensorflow
• Install Tensorflow 2.x

Topic 2 Basic Operations with Tensorflow
• Basic Tensor Data Types
• Constant, Variable & Gradient
• Matrix Operations
• Examples

Topic 3 Important Datasets and pre-processing steps
• Tabular datasets (Pima Diabetic Dataset)
• MNIST Hand written digit Dataset
• MNIST Fashion Datasets
• CIFAR-10 Image Dataset

Topic 4 Fully connected Neural Network (FcNN)
• Introduction to Neural Network (NN)
• Activation Functions (Sigmoid, ReLU, etc.)
• Loss Function and Optimizer
• Learning rate and gradient calculation
• Build Regression Model with FcNN
• Load and Save Model
• Build Classification Model with FcNN
• Softmax activation function for classification
• Exercise: Classification model building with Image data
• Ladder wise and End-to-End Training
• Fine Tune the model

Topic 5 Fully Connected Auto Encoder
• What is Auto-encoder?
• Importance of Auto-encoder
• De-noising Auto-encoder
• Build Auto-Encoder model with MNIST dataset

 

Day 2

Topic 6 Convolutional Neural Network (CNN)
• Introduction to Convolutional Neural Network (CNN)
• FcNN vs CNN
• Convolution, Stride, Padding, and Activation
• Pooling (Max-pooling and Average pooling)
• Build CNN-based Image classification models.
• Exercise (Model building for dog-cat dataset)
• Model Fine-tuning
• Data Augmentation & Dropout

Topic 7 Transfer Learning
• Pre-trained Deep-CNN models
• Vgg16, Resnet34, InceptionV3, DenseNet, etc.
• Fine tuning of pre-trained models

Topic 8 Recurrent Neural Network (RNN)
• Sequential Data (Language, music, DNA, speech, etc.)
• What is Recurrent Neural Network (RNN)?
• Types of RNN Architectures
• Handling long term dependencies with LSTM cells
• Word Embedding
• Build a RNN Model for Text Classification