Deep Learning with Tensorflow and Python

Day 1 - Topic 1 Basic Python

Topic 1.1 Get Started with Python

• Overview
• Install Python
• Install Sublime Text & PyCharm
• First Python Script
• Comment

Topic 1.2 Data Types

• Number 
• String 
• List
• Tuple
• Dictionary
• Set

Topic 1.3 Operators

• Arithmetic Operators
• Compound Operators
• Comparison Operators
• Membership Operators
• Logical Operators
• Identity Operators

Topic 1.4 Control Structure

• Conditional
• Loop
• Iterating Over Multiple Sequences
• Break & Continue
• Loop with Else

Topic 1.5 Function

• Function Syntax
• Return Single Value
• Return Multiple Values
• Passing Arguments
• Default Arguments
• Variable Arguments
• Decorator
• Lambda, Map, Filter

Topic 1.6 Modules & Packages

• Modules
• Packages
• Python Standard Libraries
• Install Third Party Packages
• Anaconda Packages

Day 2 - Topic 2 Advanced Python

Topic 2.1 Comprehensions & Generators

• Comprehension Syntax
• Types of Comprehension
• Generator Syntax
• Types of Generators

Topic 2.2 File and Directory Handling

• Read and Write Data to Files
• Manage File and Folders with Python OS Module
• Manage Paths with Python Pathlib Module

Topic 2.3 Object Oriented Programming

• Introduction to Object Oriented Programming
• Create Class and Objects
• Method and Overloading
• Initializer & Destructor
• Inheritance
• Polymorphism

Topic 2.4 Database

• Setup SQLite3 database
• Apply CRUD operations on SQLite3
• Integrate to external databases

Topic 2.5 Error Handling Using Exception

• Exceptions versus Syntax Errors
• Handle Exceptions with Try and Except blocks
• The Else clause
• Clean up with Finally

Topic 2.6 Intro to Useful Packages

• Numpy
• Matplotlib
• Pandas

Python Assessment

Day 3 - Topic 3 Basic Tensorflow

Topic 3.1 Overview of Machine Learning & Tensorflow

• Overview of Machine Learning and Deep Learning
• Introduction to Tensorflow 2.x
• Install Tensorflow 2.x

Topic 3.2 Basic Tensorflow Operations

• Basic Tensor Data Types
• Constant, Variable & Gradient
• Matrix Operations
• Eagle Mode vs Graph Mode

Topic 3.3 Datasets

• MNIST Handwritten Digits and Fashion Datasets
• CIFAR Image Dataset
• IMDB Text Dataset

Topic 3.4 Neural Network for Regression

• Introduction to Neural Network (NN)
• Activation Function
• Loss Function and Optimizer
• Machine Learning Methodology
• Build a NN Predictive Regression Model
• Load and Save Model

Topic 3.5 Neural Network for Classification

• Softmax
• Cross Entropy Loss Function
• Build a NN Classification Model

Day 4 - Topic 4 Advanced Tensorflow

Topic 4.1 Convolutional Neural Network (CNN)

• Introduction to Convolutional Neural Network (CNN)
• Convolution & Pooling
• Build a CNN Model for Image Recognition
• Overfitting and Underfitting Issues
• Methods to Solve Overfitting
• Small Dataset Overfitting Issue
• Data Augmentation & Dropout

Topic 4.2 Transfer Learning 

• Introduction to Transfer Learning
• Pre-trained Models
• Transfer Learning for Feature Extraction & Fine Tuning

Topic 4.2 Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• Types of RNN Architectures
• LSTM and GRU
• Word Embedding
• Build a RNN Model for Sentiment Analysis
• Build a RNN Model for Time Series Prediction

Final Assessment

• Written Assessment (Q&A)
• Written Assessment (Case Study)

Promotion Code

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Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

• Positive Learning Attitude
• Enthusiastic Learner

Experience

• Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

• Machine Learning Engineer
• Data Scientist
• Deep Learning Researcher
• AI Developer
• Neural Network Designer
• Computer Vision Engineer
• NLP Engineer (branching into deep learning)
• AI Product Manager (technical understanding)
• Robotics Engineer (with AI components)
• Bioinformatics Scientist (deep learning applications)
• Medical Imaging Specialist (AI-focused)
• Game Developer (AI-driven features)
• Predictive Analytics Specialist
• AI/ML Educator or Trainer
• Autonomous Systems Developer.

Man Guo Chang: Man Guo Chang graduated from Nanyang Technological University, School of Electrical and Electronic Engineering, major in Computer Engineering.

He has more than 25 years of working experience in the Semiconductor field, specialized in IC Testing, Inline Electrical Testing, Product & Yield Engineering, Data Analysis, System Engineering, and Software Development. He is also an ACTA certified trainer, currently providing STEM training to adult learners in the area of Computer Vision, Internet of Things, Embedded Electronics, and Python Programming

Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners.