GANs with Keras Online Course

GANs with Keras Online Course

Start with Python fundamentals—covering flow control, lists, dictionaries, and functions—before moving into NumPy for array and matrix operations. Build a strong foundation in deep learning by understanding artificial neurons, neural networks, CNNs, text-based models, and image classification. Progress to Generative Adversarial Networks (GANs), exploring core concepts, transfer learning, conditional GANs, and advanced DCGAN implementations. Through hands-on projects, you’ll define generator and discriminator functions, fine-tune models, and apply deep learning techniques to real-world tasks. By the end, you’ll be equipped to design, build, and deploy powerful AI solutions.

Who should take this Course?

The Kafka Streams API for Developers Online Course is ideal for software developers, data engineers, and backend programmers who want to build real-time, event-driven applications using Apache Kafka’s Streams API. It is also suitable for students, architects, and professionals seeking hands-on experience in stream processing, stateful computations, and data pipeline development to manage and process high-volume data efficiently.

What you will learn

• Learn about Artificial Intelligence (AI) and machine learning
• Understand deep learning and neural networks
• Learn about lists, tuples, dictionaries, and functions in Python
• Learn Pandas, NumPy, and Matplotlib basics
• Explore the basic structure of artificial neurons and neural network
• Understand Stride, Padding, and Flattening concepts of CNNs

Course Outline

• Introduction
• Introduction to AI and Machine Learning
• Introduction to Deep learning and Neural Networks
• Setting Up Computer - Installing Anaconda
• Python Basics - Flow Control
• Python Basics - Lists and Tuples
• Python Basics - Dictionaries and Functions
• NumPy Basics
• Matplotlib Basics
• Pandas Basics
• Installing Deep Learning Libraries
• Basic Structure of Artificial Neuron and Neural Network
• Activation Functions Introduction
• Popular Types of Activation Functions
• Popular Types of Loss Functions
• Popular Optimizers
• Popular Neural Network Types
• King County House Sales Regression Model - Step 1 Fetch and Load Dataset
• Steps 2 and 3 - EDA and Data Preparation
• Step 4 - Defining the Keras Model
• Steps 5 and 6 - Compile and Fit Model
• Step 7 Visualize Training and Metrics
• Step 8 Prediction Using the Model
• Heart Disease Binary Classification Model - Introduction
• Step 1 - Fetch and Load Data
• Steps 2 and 3 - EDA and Data Preparation
• Step 4 - Defining the Model
• Step 5 – Compile, Fit, and Plot the Model
• Step 5 - Predicting Heart Disease Using Model
• Step 6 - Testing and Evaluating Heart Disease Model
• Redwine Quality Multiclass Classification Model - Introduction
• Redwine Quality Multiclass Classification Model - Introduction
• Step1 - Fetch and Load Data
• Step 2 - EDA and Data Visualization
• Step 3 - Defining the Model
• Step 4 – Compile, Fit, and Plot the Model
• Step 5 - Predicting Wine Quality Using Model
• Serialize and Save Trained Model for Later Usage
• Digital Image Basics
• Basic Image Processing Using Keras Functions
• Keras Single Image Augmentation
• Keras Directory Image Augmentation
• Keras Data Frame Augmentation
• CNN Basics
• Stride, Padding, and Flattening Concepts of CNN
• Flowers CNN Image Classification Model – Fetch, Load, and Prepare Data
• Flowers Classification CNN - Create Test and Train Folders
• Flowers Classification CNN - Defining the Model
• Flowers Classification CNN - Training and Visualization
• Flowers Classification CNN - Save Model for Later Use
• Flowers Classification CNN - Load Saved Model and Predict
• Flowers Classification CNN - Optimization Techniques - Introduction
• Flowers Classification CNN - Dropout Regularization
• Flowers Classification CNN - Padding and Filter Optimization
• Flowers Classification CNN - Augmentation Optimization
• Hyperparameter Tuning
• Transfer Learning Using Pre-Trained Models - VGG Introduction
• VGG16 and VGG19 Prediction
• ResNet50 Prediction
• VGG16 Transfer Learning Training Flowers Dataset
• VGG16 Transfer Learning Flower Prediction
• VGG16 Transfer Learning Using Google Colab GPU - Preparing and Uploading Dataset
• VGG16 Transfer Learning Using Google Colab GPU - Training and Prediction
• VGG19 Transfer Learning Using Google Colab GPU - Training and Prediction
• ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction
• Popular Neural Network Types
• Generative Adversarial Networks GAN Introduction
• Simple Transpose Convolution Using a Grayscale Image
• Generator and Discriminator Mechanism Explained
• A fully Connected Simple GAN Using MNIST Dataset - Introduction
• Fully Connected GAN - Loading the Dataset
• Fully Connected GAN - Defining the Generator Function
• Fully Connected GAN - Defining the Discriminator Function
• Fully Connected GAN - Combining Generator and Discriminator Models
• Fully Connected GAN - Compiling Discriminator and Combined GAN Models
• Fully Connected GAN - Discriminator Training
• Fully Connected GAN - Generator Training
• Fully Connected GAN - Saving Log at Each Interval
• Fully Connected GAN - Plot the Log at Intervals
• Fully Connected GAN - Display Generated Images
• Saving the Trained Generator for Later Use
• Generating Fake Images Using the Saved GAN Model
• Fully Connected GAN Versus Deep Convoluted GAN
• Fully Connected GAN Versus Deep Convoluted GAN
• Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset
• Deep Convolutional GAN - Defining the Generator Function - Part 1
• Deep Convolutional GAN - Defining the Discriminator Function
• Deep Convolutional GAN - Combining and Compiling the Model
• Deep Convolutional GAN - Training the Model
• Deep Convolutional GAN - Training the Model Using Google Colab GPU
• Deep Convolutional GAN - Loading the Fashion MNIST Dataset
• Deep Convolutional GAN - Loading the Fashion MNIST Dataset
• Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPU
• Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Defining the Generator
• Deep Convolutional GAN - Defining the Discriminator
• Deep Convolutional GAN CIFAR-10 - Training the Model
• Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU
• Vanilla GAN Versus Conditional GAN
• Conditional GAN - Defining the Basic Generator Function
• Conditional GAN - Label Embedding for Generator
• Conditional GAN - Defining the Basic Discriminator Function
• Conditional GAN - Label Embedding for Discriminator
• Conditional GAN - Combining and Compiling the Model
• Conditional GAN - Training the Model
• Conditional GAN - Display Generated Images
• Conditional GAN - Training the MNIST Model Using Google Colab GPU
• Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU
• Other Popular GANs - Further Reference and Source Code Link
• Other Popular GANs - Further Reference and Source Code Link