MP4 | Video: AVC 1920x1080 30 fps | Audio: AAC 48 KHz 2ch | Duration: 3h 52m

Genre: eLearning | Language: English | Size: 4.72 GB

Wow! A brand new set of techniques to study and apply. The videos are great, amazingly organized, and go step by step to introduce such a complex topic.

Arnaldo Ayala, Software Architect, Consultores Informáticos

The Keras package for R brings the power of deep learning to R users. Deep Learning with R in Motion locks in the essentials of deep learning and teaches you the techniques you'll need to start building and using your own neural networks for text and image processing.

Instructor Rick Scavetta takes you through a hands-on ride through the powerful Keras package, a TensorFlow API. You'll start by digging into case studies for how and where to use deep learning. Then, you'll master the essential components of a deep learning neural network as you work hands-on through your first examples. You'll continue by exploring dense and recurrent neural networks, convolutional and generative networks, and how they all work together.

And that's just the beginning! You'll go steadily deeper, making your network more robust and efficient. As your work through each module, you'll train your network and pick up the best practices used by experts like expert instructor Rick Scavetta, Keras library creator and author of Deep Learning in Python François Chollet, and JJ Allaire, founder of RStudio, creator of the R bindings for Keras, and coauthor of Deep Learning in R! You'll beef up your skills as you practice with R-based applications in computer vision, natural-language processing, and generative models, ready for the real-world.

Machine learning has made remarkable progress in recent years. Deep learning systems have revolutionized image recognition, natural-language processing, and other applications for identifying complex patterns in data. The Keras library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep learning tasks!

Inside:

The 4 steps of Deep Learning

Using R with Keras and TensorFlow

Working with the Universal Workflow

Computer vision with R

Recurrent neural networks

Everyday best practices

Generative deep learning