GANs with Keras Practice Exam

GANs with Keras Practice Exam

About GANs with Keras Exam

Generative Adversarial Networks (GANs) are a cutting-edge technique in deep learning that allows machines to generate new data that resembles real-world examples. GANs consist of two neural networks—a Generator that creates synthetic data and a Discriminator that evaluates its authenticity. This course covers the journey from basic Python programming to advanced GAN architectures like Deep Convolutional GANs (DCGANs) and Conditional GANs (CGANs). You will also learn image augmentation, transfer learning, and neural network optimization techniques. By the end of the course, you will have the expertise to build and deploy deep learning models for real-world applications.

Skills Required

To get the most out of this course, learners should have:

• Basic Python programming knowledge (lists, dictionaries, functions).
• Understanding of NumPy, Pandas, and Matplotlib for data handling and visualization.
• Familiarity with deep learning concepts like artificial neurons, activation functions, and loss functions.
• Knowledge of convolutional neural networks (CNNs) and their components.
• Experience working with machine learning libraries like Keras and TensorFlow (preferred but not mandatory).

Knowledge Area

This course provides hands-on learning in:

• Fundamentals of Artificial Intelligence (AI) and Machine Learning.
• Python basics for deep learning and data science.
• Building and training artificial neural networks (ANNs) from scratch.
• Understanding convolutional neural networks (CNNs) for image processing.
• Exploring advanced neural network architectures, including GANs.
• Constructing fully connected GANs, Deep Convolutional GANs (DCGANs), and Conditional GANs (CGANs).
• Using Google Colab and GPUs to train deep learning models.

Who should take This Course?

This course is perfect for:

• Beginners in deep learning looking to progress to expert-level AI development.
• Data scientists and AI researchers who want to explore generative models.
• Machine learning engineers interested in learning GANs for synthetic data generation.
• Developers working on deep learning projects requiring image processing and model optimization.
• Students and professionals aiming to build a career in AI and deep learning.

Prerequisites:

• Basic knowledge of Python and programming logic.
• Understanding of fundamental AI and machine learning concepts.
• Familiarity with deep learning libraries such as TensorFlow/Keras is beneficial.

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.