How To Be A Deep Learning Engineer

Neural Networks and Deep Learning

In contrast to linear and logistic regressions which are considered linear models, the objective of neural networks is to capture non-linear patterns in data by adding layers of parameters to the model.

In fact, the structure of neural networks is flexible enough to build our well-known linear and logistic regression.

The term Deep learning comes from a neural net with many hidden layers (see next Figure) and encapsulates a wide variety of architectures.

It’s especially difficult to keep up with developments in deep learning, in part because the research and industry communities have doubled down on their deep learning efforts, spawning whole new methodologies every day.

However, in this course I still attempt to teach ground-breaking technologies such as R programming language and Rstudio, alongside popular Deep learning technologies such mxnet, h20, and neural nets

For the best performance, deep learning techniques require a lot of data — and a lot of compute power since the method is self-tuning many parameters within huge architectures.

It quickly becomes clear why deep learning practitioners need very powerful computers enhanced with GPUs (graphical processing units).

In particular, deep learning techniques have been extremely successful in the areas of computer vision (image classification), text, audio and video.

At the end of this course, I hope the student picks up necessary skill and knowledge to be a successful Machine learning engineer