Introduction to Deep Learning (DL). Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence.
— Why Deep Learning? —
Here are five reasons we think (DL) really is different, and stands out from the crowd of other training programs out there:
1. ROBUST STRUCTURE
The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.
That’s why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised (DL). With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.
2. INTUITION TUTORIALS
So many courses and books just bombard you with the theory, and math, and coding… But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that’s how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.
With our intuition tutorials, you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.
3. EXCITING PROJECTS
Are you tired of courses based on over-used, outdated data sets?
Yes? Well, then you’re in for a treat.
Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:
- Artificial Neural Networks to solve a Customer Churn problem
- Convolutional Neural Networks for Image Recognition
- Recurrent Neural Networks to predict Stock Prices
- Self-Organizing Maps to investigate Fraud
- Boltzmann Machines to create a Recommender System
- Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize
— Who Is This Course For? —
As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you’re done with Deep Learning your skills are on the cutting edge of today’s technology.
If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning is structured around special coding blueprint approaches meaning that you won’t get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge (DL) algorithms and techniques (some of which didn’t even exist a year ago) and through this course, you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new (DL) skills and applications.
What Will You Learn?
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Introduction to Deep Learning.
Introduction to Deep Learning.52:52
Recurrent Neural Networks45:28
Convolutional Neural Networks37:20
Deep Generative Modeling40:39
Deep Learning New Frontiers38:09
Generalizable Autonomy for Robot Manipulation47:01
Machine Learning for Scent38:52
Visualization for Machine Learning37:40
Biologically Inspired Neural Networks (IBM)31:29
Image Domain Transfer (NVIDIA)46:32
Sequence Modeling with Neural Networks27:13
Issues in Image Classification17:18
Faster ML Development with TensorFlow19:19
Deep Learning – A Personal Perspective32:56
Beyond Deep Learning: Learning+Reasoning32:20
Computer Vision Meets Social Networks33:58