Keras Deep Learning Python Crash Course: Learn Keras Today!

COURSE OBJECTIVE 

The primary objective of this course is to give teach you the practical hands-on skills you need to solve concrete deep learning problems -  without wasting time having to learn all of the underlying math behind deep learning & neural networks.

In this course you will learn about the most widely used types of deep neural networks that is being used in many fields like computer vision, natural language processing and many more. 

You will learn how to use Keras in your applications to solve problems and compile models - in a practical way!

I will demonstrate and solve two practical problems for each neural network type, so you can follow along and learn while I explain what I am doing.

The course will have a final test after the lessons, so you can test your Deep Learning & Keras knowledge and start practicing your new skills.

WHAT IS DEEP LEARNING & KERAS?

Deep Learning is one of the fastest growing  technologies in recent years. Deep Learning is a type of Machine Learning technique that uses neural network to make predictions, after being trained on a huge dataset.

You can compare it to the way human beings take in information and learns something, only here it is a computer that is fed with huge amounts of data and learns by itself from this data.

There are lots of operations that needs to be performed by the computer, for it to learn itself.

However this is not useful for production purposes and quick prototyping. For this reason we use deep learning frameworks, where we don’t have to deal with the low level operations.

Keras is one of the most popular and easy application to use deep learning frameworks, by which we can build a very complex deep learning model very quickly, just with a few lines of codes.

It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras. 

Keras can use three back-end implementations: Tensorflow, CNTK & Theano. So learning Keras can help you working with all of these neural networks. In this course I will primarily work with Theano.

TOP 3 BENEFITS OF LEARNING KERAS & DEEP LEARNING

1: Experts in Deep Learning & Keras are in high demand & and the demand is only growing

2: Deep Learning is a recent technology. With the availability of lots of data and huge computation power, this field is advancing very rapidly and is very exiting.

3: Get Chosen over your competitors: The addition of Deep Learning knowledge with your IT background will enable you to stand apart from your competitors and help you secure the dream job.
 

FREQUENTLY ASKED QUESTIONS 

Why should I learn Keras and not other frameworks?
In this course I will show you how much easier Keras is than competing framworks, when dealing with any type of deep learning problems. Together we will solve some real world deep learning problems and afterwards you will be convinced about the superiority of Keras.

Why should I take this course when there are lots of courses of Deep Learning out there?
This course is different in that it is designed to be a quick-start course. You will learn the basic theory and then try to solve deep learning problems, without learning all of the underlying math. This will give you practical knowledge and help you get started faster on using deep learning for your own applications.

GUARANTEE

If within 30 days of buying the course you decide that it's not for you, please get a Udemy-backed refund. No questions asked — just press the refund button, and all of your money will be returned to your credit card.  

ARE YOU READY TO LEARN DEEP LEARNING & KERAS?

Please press the "Take This Course" button and start learning 2 minutes from now!