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Book Overview & Buying
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Table Of Contents
Keras Deep Learning and Generative Adversarial Networks (GAN)
By :
Keras Deep Learning and Generative Adversarial Networks (GAN)
By:
Overview of this book
The course begins with the fundamentals of Python, encompassing concepts such as assignment, flow control, lists, tuples, dictionaries, and functions. We then move on to the Python NumPy library, which supports large arrays and matrices.
Before embarking on the journey of deep learning, a comprehensive theoretical session awaits, expounding upon the essential structure of an artificial neuron and its amalgamation to form an artificial neural network. The exploration then delves into the realm of CNNs, text-based models, binary and multi-class classification, and the intricate world of image processing. The transformation continues with an in-depth exploration of the GAN paradigm, spanning from fundamental principles to advanced strategies. Attendees will have the opportunity to construct models, harness transfer learning techniques, and venture into the realm of conditional GANs.
Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and define the Discriminator function.
By the end of the course, you will wield the skills to create, fine-tune, and deploy cutting-edge AI solutions, setting you apart in this evolving landscape.
Table of Contents (105 chapters)
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
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
Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset
Deep Convolutional GAN - Defining the Generator Function
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 - 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
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