Deep Learning with Tensorflow and Python (CITREP+)

by Tertiary Infotech Pte. Ltd

These are the exercise files used for Deep Learning with Tensorflow and Python (CITREP+) course.

The course outline can be found in

https://www.tertiarycourses.com.sg/deep-learning-with-tensorflow-and-python-citrep.html

Day 1
Module 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
Module 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
Module 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
Module 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 Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• Types of RNN Architectures
• LSTM and GRU
• Word Embedding
• Build a RNN Model for Text Classification

Topic 4.3 Transfer Learning & Tensorflow Hub

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

Tensorflow Assessment