This is a course which naturally extends into your career.
#Nytimes learning network how to#
In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.
This way you can follow along and understand exactly how the code comes together and what each line means. In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. We haven't seen this method explained anywhere else in sufficient depth. *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize.Boltzmann Machines to create a Recomender System.Self-Organizing Maps to investigate Fraud.Recurrent Neural Networks to predict Stock Prices.Convolutional Neural Networks for Image Recognition.Artificial Neural Networks to solve a Customer Churn problem.In this course we will solve six real-world challenges: (Definitely not the boring iris or digit classification datasets that we see in every course).
Inside this class we will work on Real-World datasets, to solve Real-World business problems.
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 This is a game-changer.Īre you tired of courses based on over-used, outdated data sets? With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms. 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. So many courses and books just bombard you with the theory, and math, and coding. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. The first and most important thing we focused on is giving the course a robust structure. Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there: And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. 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.īut the further AI advances, the more complex become the problems it needs to solve. Artificial intelligence is growing exponentially.