GANs with Keras Online Course

GANs with Keras Online Course

This course is designed to take learners from the basics of Python to mastering Generative Adversarial Networks (GANs). You will begin by understanding Python programming fundamentals, then progress to deep learning concepts, and finally explore advanced GAN architectures. Topics covered include convolutional neural networks (CNNs), image processing, artificial neural networks (ANNs), and optimization techniques. The course will help you build deep learning models from scratch and train GANs to generate realistic images. By the end of the course, you will be equipped with the knowledge and skills to develop and deploy cutting-edge AI models.

Key Benefits

• Master deep learning from the ground up with hands-on projects.
• Learn Python programming essentials for data science and AI.
• Understand artificial neural networks (ANNs) and their applications.
• Explore convolutional neural networks (CNNs) for image classification.
• Gain expertise in Generative Adversarial Networks (GANs).
• Develop fully connected GANs, Deep Convolutional GANs (DCGANs), and Conditional GANs (CGANs).
• Work with Google Colab GPUs to train deep learning models.
• Optimize deep learning models using hyperparameter tuning and transfer learning.

Target Audience

This course is ideal for:

• Beginners in deep learning and artificial intelligence (AI).
• Machine learning engineers and AI researchers interested in GANs and deep learning.
• Data scientists who want to explore image processing and neural networks.
• Software developers and Python programmers looking to specialize in deep learning.
• Students and professionals aiming to build a career in AI and deep learning.

Prerequisites:

• Basic Python programming knowledge.
• Understanding of AI and machine learning fundamentals.
• Familiarity with deep learning libraries (Keras, TensorFlow) is beneficial.

Learning Objectives

• Learn the fundamentals of AI and machine learning.
• Master Python programming for deep learning applications.
• Build and train artificial neural networks (ANNs) from scratch.
• Develop convolutional neural networks (CNNs) for image classification.
• Gain hands-on experience in image processing and augmentation.
• Understand Generative Adversarial Networks (GANs) and their working principles.
• Implement fully connected GANs, DCGANs, and CGANs.
• Train deep learning models using Google Colab GPUs.
• Optimize neural networks with transfer learning and hyperparameter tuning.

Course Content Overview

The GANs with Keras Exam covers the following topics - 

Domain 1. Introduction to the Course

• Overview of the course structure and learning roadmap.
• Understanding the key topics covered in the course.

Domain 2. Understanding AI and Machine Learning

• Introduction to Artificial Intelligence (AI) and its applications.
• Overview of Machine Learning (ML) and its role in AI systems.

Domain 3. Introduction to Deep Learning and Neural Networks

• Exploring deep learning and its importance in AI development.
• Understanding the basic structure of a neural network.

Domain 4. Setting Up the Development Environment

• Installing Anaconda to set up a Python-based deep learning environment.

Domain 5. Python Programming Basics

• Understanding flow control (conditional statements and loops).
• Exploring lists and tuples in Python.
• Learning how to use dictionaries and functions for structured programming.

Domain 6. Working with NumPy for Numerical Computing

• Introduction to NumPy and its role in deep learning.
• Performing basic operations on NumPy arrays.

Domain 7. Data Visualization Using Matplotlib

• Learning how to use Matplotlib for data visualization.
• Understanding different types of plots and graphs.

Domain 8. Data Handling with Pandas

• Exploring Pandas for data manipulation and analysis.
• Working with DataFrames for structured data processing.

Domain 9. Installing and Using Deep Learning Libraries

• Setting up Keras and TensorFlow for deep learning.

Domain 10. Understanding Artificial Neural Networks (ANNs)

• Learning about artificial neurons and their structure.
• Exploring how neurons combine to form a neural network.

Domain 11. Understanding Activation Functions

• Introduction to activation functions and their significance.
• Exploring different activation functions used in neural networks.

Domain 12. Understanding Loss Functions in Neural Networks

• Learning about loss functions and their role in optimization.

Domain 13. Popular Optimizers for Neural Networks

• Exploring commonly used optimization techniques.

Domain 14. Understanding Neural Network Architectures

• Overview of different types of neural networks and their applications.

Domain 15. Building a Regression Model Using Deep Learning

• Fetching and loading a dataset for King County house sales prediction.
• Performing Exploratory Data Analysis (EDA) and data preprocessing.
• Defining and training a Keras regression model.
• Visualizing model training progress and evaluating predictions.

Domain 16. Building a Binary Classification Model for Heart Disease Prediction

• Loading the heart disease dataset for binary classification.
• Performing EDA and data transformation.
• Defining a deep learning model for heart disease prediction.
• Training and evaluating the classification model.

Domain 17. Building a Multi-Class Classification Model for Wine Quality Prediction

• Fetching and processing the Redwine quality dataset.
• Visualizing data distribution and feature correlations.
• Training and evaluating a multi-class classification model.

Domain 18. Saving and Loading Deep Learning Models

• Serializing and saving trained models for later use.

Domain 19. Introduction to Image Processing for Deep Learning

• Understanding digital images and basic image processing techniques.
• Performing image augmentation using Keras functions.

Domain 20. Understanding Convolutional Neural Networks (CNNs)

• Introduction to CNNs and their architecture.
• Exploring stride, padding, and flattening in CNNs.

Domain 21. Building a CNN Model for Flower Classification

• Fetching and preparing image datasets.
• Creating training and testing datasets for CNN models.
• Defining, training, and optimizing a CNN model for image classification.

Domain 22. Optimizing CNN Models for Better Performance

• Understanding regularization techniques like dropout.
• Exploring filter optimization and augmentation strategies.

Domain 23. Hyperparameter Tuning for Deep Learning Models

• Learning how to optimize model performance using hyperparameter tuning.

Domain 24. Transfer Learning Using Pre-Trained CNN Models

• Exploring VGG16, VGG19, and ResNet50 for image classification.
• Fine-tuning CNN models using transfer learning.

Domain 25. Introduction to Generative Adversarial Networks (GANs)

• Understanding the GAN architecture and how it generates new data.
• Exploring how generators and discriminators work together.

Domain 26. Building a Fully Connected GAN Using MNIST Dataset

• Fetching and loading the MNIST dataset for GAN training.
• Defining the Generator and Discriminator functions.
• Training the GAN model and generating synthetic images.

Domain 27. Understanding Deep Convolutional GANs (DCGANs)

• Exploring the differences between Fully Connected GANs and DCGANs.
• Implementing DCGANs for generating realistic images.

Domain 28. Building a Conditional GAN (CGAN) for Label-Specific Image Generation

• Understanding how Conditional GANs use labels to control outputs.
• Training a CGAN model using different datasets.

Domain 29. Training GAN Models on Google Colab GPUs

• Running GAN models efficiently on Google Colab for faster training.

Domain 30. Further Exploration of GANs

• Learning about other popular GAN architectures for advanced research.