Description

Jeff McErlain’s Blues-Rock Evolution

Jeff McErlain’s Blues-Rock Evolution
.MP4, AVC, 854×480, 30 fps | English, AAC, 2 Ch | 2h 40m | + Charts (.pdf), Jam Tracks (.mp3) | 761 MB
Modern blues rock rhythm, soloing and improvisational techniques
New York guitarist, producer and top-notch educator Jeff McErlain shows you how to pull your blues apart and put them back together again so they’re hotter, more fluid and far more exciting to play. Blues has been evolving since before Muddy Waters and Howlin’ Wolf cut their first sides, and Jeff delves deep into the fretboard to reveal the genetic code and evolution of blues-based rockers from Jeff Beck, Jimmy Page and Jimi Hendrix to David Grissom, Scott Henderson and Michael Landau, revealing more than just a few of his own favorite innovations along the way.

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 Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.

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.

Are you tired of courses based on over-used, outdated data sets?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