Deep Learning Using Keras Online Course

Deep Learning Using Keras Online Course

This course provides a comprehensive introduction to deep learning, starting with Python basics and key libraries like NumPy, Pandas, and Matplotlib. You'll then explore deep learning frameworks such as Theano, TensorFlow, and Keras before diving into neural networks, covering activation functions, loss functions, and optimizers. The course includes hands-on projects using multi-layer neural networks for text data and CNNs for image data, along with model optimization techniques like image augmentation. By the end, you'll have a solid grasp of deep learning and be ready to apply it to real-world projects.

Who is this Course for?

• Ideal for beginners in deep learning
• Covers basic to advanced concepts
• No prior experience required
• Basic computer knowledge recommended

What you will learn

• Master Python programming basics
• Work with NumPy, Matplotlib, and Pandas
• Understand artificial neurons & neural networks
• Explore activation functions, loss functions, and optimizers
• Build multi-layer neural networks for text data
• Develop CNNs for image-based datasets

Course Table of Contents

Course Introduction

• Course Introduction and Table of Contents

Introduction

• Introduction to AI (Artificial Intelligence) and Machine Learning
• Introduction to Deep learning

Setting Up Computer

• Installing Anaconda

Python Basics

• Assignment
• Flow Control - Part 1
• Flow Control - Part 2
• List and Tuples
• Dictionary and Functions - part 1
• Dictionary and Functions - part 2

NumPy Basics

• NumPy Basics - Part 1
• NumPy Basics - Part 2

Matplotlib Basics

• Matplotlib Basics - part 1
• Matplotlib Basics - part 2

Pandas Basics

• Pandas Basics - Part 1
• Pandas Basics - Part 2

Installing Libraries

• Installing Deep Learning Libraries

Artificial Neuron and Neural Network

• Basic Structure

Activation Functions

• Introduction

Popular Activation Functions

• Popular Types of Activation Functions

Popular Types of Loss Functions

• Popular Types of Loss Functions

Popular Types of Optimizers

• Popular Optimizers

Popular Neural Network Types

• Popular Neural Network Types

King County House Sales Regression Model

• Step 1 - Fetch and Load Dataset
• Step 2 and 3 - EDA (Exploratory Data Analysis) and Data Preparation - Part 1
• Step 2 and 3 - EDA and Data Preparation - Part 2
• Step 4 - Defining the Keras Model - Part 1
• Step 4 - Defining the Keras Model - Part 2
• Step 5 and 6 - Compile and Fit Model
• Step 7 - Visualize Training and Metrics
• Step 8 - Prediction Using the Model

Heart Disease Binary Classification Model

• Heart Disease Binary Classification Model - Introduction
• Step 1 - Fetch and Load Data
• Step 2 and 3 - EDA and Data Preparation - Part 1
• Step 2 and 3 - EDA and Data Preparation - Part 2
• Step 4 - Defining the Model
• Step 5 and 6 - Compile Fit and Plot the Model
• Step 7 - Predicting Heart Disease Using Model

Red Wine Quality Multiclass Classification Model

• Introduction
• Step 1 - Fetch and Load Data
• Step 2 and 3 - EDA and Data Visualization
• Step 4 - Defining the Model
• Step 5 and 6 - Compile Fit and Plot the Model
• Step 7 - Predicting Wine Quality using Model
• Serialize and Save Trained Model for Later Use

Digital Image Basics

• Digital Image
• Basic Image Processing Using Keras Functions - Part 1
• Basic Image Processing Using Keras Functions - Part 2
• Basic Image Processing Using Keras Functions - Part 3

Image Augmentation

• Keras Single Image Augmentation - Part 1
• Keras Single Image Augmentation - Part 2
• Keras Directory Image Augmentation
• Keras Data Frame Augmentation

Convolutional Neural Network

• CNN (Convolutional Neural Networks) Basics
• Stride Padding and Flattening Concepts of CNN
• Flowers CNN Image Classification Model
• Fetch Load and Prepare Data
• Create Test and Train Folders
• Defining the Model - Part 1
• Defining the Model - Part 2
• Defining the Model - Part 3
• Training and Visualization
• Save Model for Later Use
• Load Saved Model and Predict
• Improving Model - Optimization Techniques
• Dropout Regularization
• Padding and Filter Optimization
• Augmentation Optimization
• Hyper Parameter Tuning - Part 1
• Hyper Parameter Tuning - Part 2

Transfer Learning Using Pretrained Models

• VGG Introduction
• VGG16 and VGG19 Prediction
• VGG16 and VGG19 Prediction - Part 1
• VGG16 and VGG19 Prediction - Part 2

ResNet50

• ResNet50 Prediction

Transfer Learning Training Flowers Dataset

• VGG16 - Part 1
• VGG16 - Part 2

Transfer Learning Flower Prediction

• VGG16 Transfer Learning Flower Prediction

VGG16 Transfer Learning Using Google Colab GPU

• Preparing and Uploading Dataset
• Training and Prediction

VGG19 Transfer Learning using Google Colab GPU

• Training and Prediction

ResNet-50 Transfer Learning using Google Colab GPU

• Training and Prediction