Keras Deep Learning & Generative Adversarial Networks (GAN)
Keras Deep Learning & Generative Adversarial Networks (GAN), Learn From the Scratch to Expert Level: Deep Learning & Generative Adversarial Networks (GAN) using Python with Keras.
Description
Hi There!
Hello and welcome to my new course Deep Learning with Generative Adversarial Networks (GAN). This course is divided into two halves. In the first half we will deal with Deep Learning and Neural Networks and in the second half on top of that, we will continue with Generative Adversarial Networks or GAN or we can call it as ‘gan’. So lets see what are the topics that are included in each module. At first, the Deep Learning one..
As you already know the artificial intelligence domain is divided broadly into deep learning and machine learning. In-fact deep learning is machine learning itself but Deep learning with its deep neural networks and algorithms try to learn high-level features from data without human intervention. That makes deep learning the base of all future self intelligent systems.
I am starting from the very basic things to learn like learning the programming language basics and other supporting libraries at first and proceed with the core topic.
Let’s see what are the interesting topics included in this course. At first we will have an introductory theory session about Artificial Intelligence, Machine learning, Artificial Neurons based Deep Learning and Neural Networks.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine. We will be using the browser based IDE called Jupyter notebook for our further coding exercises.
I know some of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions List and Tuples, Dictionaries, Functions etc.
Then we will start with learning the basics of the Python Numpy library which is used to adding support for large, multi-dimensional arrays and matrices, along with a large collection of classes and functions. Then we will learn the basics of matplotlib library which is a plotting library for Python for corresponding numerical expressions in NumPy. And finally the pandas library which is a software library written for the Python programming language for data manipulation and analysis.
After the basics, we will then install the deep learning libraries theano, tensorflow and the API for dealing with these called as Keras. We will be writing all our future codes in keras.
Then before we jump into deep learning, we will have an elaborate theory session about the basic Basic Structure of an Artificial Neuron and how they are combined to form an artificial Neural Network. Then we will see what exactly is an activation function, different types of most popular activation functions and the different scenarios we have to use each of them.
After that we will see about the loss function, the different types of popular loss functions and the different scenarios we have to use each of them.
Like the Activation and loss functions, we have optimizers which will optimize the neural network based on the training feedback. We will also see the details about most popular optimizers and how to decide in which scenarios we have to use each of them.
Then finally we will discuss about the most popular deep learning neural network types and their basic structure and use cases.
Further the course is divided into exactly two halves. The first half is about creating deep learning multi-layer neural network models for text based dataset and the second half about creating convolutional neural networks for image based dataset.
In Text based simple feed forward multi-layer neural network model we will start with a regression model to predict house prices of King County USA. The first step will be to Fetch and Load Dataset from the kaggle website into our program.
Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model.
Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matplotlib.
Finally we have our already trained model. We will try doing a prediction of the king county real estate price using our deep learning model and evaluate the results.
That was a text based regression model. Now we will proceed with a text based binary classification model. We will be using a derived version of Heart Disease Data Set from the UCI Machine Learning Repository. Our aim is to predict if a person will be having heart disease or not from the learning achieved from this dataset. The same steps repeat here also.
The first step will be to Fetch and Load Dataset into our program.
Then as the second step, we will do an EDA or an Exploratory Data Analysis of the loaded data and we will then prepare the data for giving it into our deep learning model. Then we will define the Keras Deep Learning Model.
Once we define the model, we will then compile the model and later we will fit our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics like accuracy, loss etc can be evaluated and visualized using matplotlib.
Finally we have our already trained model. We will try doing a prediction for heart disease using our deep learning model and evaluate the results.
After the text based binary classification model. Now we will proceed with a text based multi class classification model. We will be using the Red Wine Quality Data Set from the kaggle website. Our aim is to predict the multiple categories in which a redwine sample can be placed from the learning achieved from this dataset. The same steps repeat here also.
