How Do I Start Using Deep Learning?

Where you start depends on what you already know.

The prerequisites for really understanding deep learning are linear algebra, calculus and statistics, as well as programming and some machine learning. The prerequisites for applying it are just learning how to deploy a model.

In the case of Deeplearning4j, you should know Java well and be comfortable with tools like the IntelliJ IDE and the automated build tool Maven. Skymind’s SKIL also includes a managed Conda environment for machine learning tools using Python.

Below you’ll find a list of resources. The sections are roughly organized in the order they will be useful.


Free Machine- and Deep-learning Courses Online



If you do not know how to program yet, you can start with Java, but you might find other languages easier. Python and Ruby resources convey the basic ideas in a faster feedback loop.

If you want to jump into deep-learning from here without Java, we recommend Theano and the various Python frameworks built atop it, including Keras and Lasagne.


Once you have programming basics down, tackle Java, the world’s most widely used programming language, and the language of Hadoop.


With that under your belt, we recommend you approach Deeplearning4j through its examples.

Once you get those up and running, and you’ve understood the API, you’re ready for a full install.

Other Resources

Most of what we know about deep learning is contained in academic papers. We’ve linked to a number of them here.

While individual courses have limits on what they can teach, the Internet does not. Most math and programming questions can be answered by Googling and searching sites like Stackoverflow and Math Stackexchange.

Beginner’s Guides for Deep Learning and Machine Learning

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