In a 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 half is about creating deep learning multi-layer neural network models for text-based datasets and the second half is about creating convolutional neural networks for the image-based datasets. Then finally we will discuss the most popular deep learning neural network types and their basic structure and use cases.įurther, the course is divided into exactly two halves.
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We will also see the details about the most popular optimizers and how to decide in which scenarios we have to use each of them. Like the Activation and loss functions, we have optimizers that will optimize the neural network based on the training feedback. Then we will see what exactly is an activation function, the different types of most popular activation functions, and the different scenarios we have to use each of them.Īfter 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. 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. We will be writing all our future codes in keras.
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And finally, the pandas library is a software library written for the Python programming language for data manipulation and analysis.Īfter the basics, we will then install the deep learning libraries theano, TensorFlow, and the API for dealing with these called Keras. Then we will learn the basics of matplotlib library which is a plotting library for Python for corresponding numerical expressions in NumPy. 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. Deep Learning using Keras – Complete & Compact Dummies Guide The topics include Python assignment, flow-control, functions List and Tuples, Dictionaries, Functions, etc. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. I know some of you may not be coming from a python based programming background.
We will be using the browser based IDE called Jupyter notebook for our further coding exercises. At first, we will have an introductory theory session about Artificial Intelligence, Machine learning, Artificial Neurons based on Deep Learning, and Neural Networks.Īfter 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. Let’s see what are interesting topics included in this course. That makes deep learning the base of all future self intelligent systems.Īnd in this course, 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. 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.
Welcome to my new course ‘Deep Learning from the Scratch using Python and Keras’.Īs you already know the artificial intelligence domain is divided broadly into deep learning and machine learning.
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