Get an introduction to deep neural networks, their history and reasons for their popularity in the fields of natural language processing and image recognition.
Learn the basic mathematical foundations required for deep neural networks including problem representation via tensors, tensor operations and gradient descent.
Get a step-by-step introduction to the deep learning terminology and build your first models from scratch using Keras. See deep neural networks in action applied to text mining and image recognition use cases. Understand the inner workings of Tensorflow and learn for which other general use cases it can be applied. Build your model fitting pipelines and decide for which learning problems it makes sense to apply deep neural networks.
Train your networks on high-performance custom GPU instances and select the best one during the model selection process. Learn how to put your models into production using Docker container images. Support the full model development life cycle including performance re-evaluation and model updates.
The Quantargo certificate in “Deep Learning with R” demonstrates that your are literate in the topics covered by this course. You have...
We have built a complete online Learning Hub with hands-on quizzes, code exercises and viedeos and an online Workspace that lets you immediately apply learned skills by trying out code.
Track progress and learn in your own pace with our interactive tutorials. In the CourseCockpit you’ll find all assets and course materials.
A complete RStudio environment to try out new ideas and play around with different packages and features. All files are synced automatically.