What you will learn from this course?
Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in day to day projects
Understand the basics behind Convolutional Neural Networks
Apply Convolutional Neural Networks in practice
Apply Recurrent Neural Networks in regular projects
Understand the basics behind Self-Organizing Maps, Boltzmann Machines, and AutoEncoders
About this course
This decade is all about Artificial intelligence. Artificial intelligence is growing exponentially and gaining popularity worldwide. Self-driving cars are clocking up millions of miles and update its database every second, IBM Watson is diagnosing patients better than armies of doctors in the hospitals and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. Artificial intelligence has a wide variety of usage nowadays
But the further AI advances, the more complex problems and challenges occur, and only with the help of Deep Learning we can solve such complex problems and that's why it’s the heart of Artificial intelligence.
Why Deep Learning A-Z?
1. ROBUST STRUCTURE
The first and foremost thing we focused on is giving the course a robust and simple structure at the same time. Deep Learning is very broad and complex and to navigate through this maze you need a clear cut and global vision about the topics and the fundamentals.
That's why the tutorials are grouped into two volumes, representing the two fundamental branches of Deep Learning: Unsupervised Deep Learning and Supervised Deep Learning. With each volume focusing on three different algorithms, this is the best structure to mastering Deep Learning in an effective way.
2. INTUITION TUTORIALS
Our main focus is an intuitive *feel* for the concepts behind Deep Learning algorithms. So many courses and books just bombard you with a lot of math theory and coding... But they forget to explain, why and how these things done
With our online tutorials you are able to understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see a comprehensive overall development in your problem solving department.
3. EXCITING PROJECTS
Throughout this session you will be working on Real-World datasets and projects to be able to cope up and solve Real-World business problems easily and more efficiently. In this course we will solve following problems.
• Boltzmann Machines to create a Recommender System
• Recurrent Neural Networks to predict Stock Prices
• Convolution Neural Networks for Image Recognition
• Artificial Neural Networks to solve a Customer Churn problem
• Self-Organizing Maps to investigate Fraud
Click the link or image below to access the course contents: