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Published on Dec 7, 2015
Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any of the math or code.
Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machine Learning, Neural Networks, and Classification. The field has advanced significantly over the years due to the works of giants like Andrew Ng, Geoff Hinton, Yann LeCun, Adam Gibson, and Andrej Karpathy. Many companies have also invested heavily in Deep Learning and AI research - Google with DeepMind and its Driverless car, nVidia with CUDA and GPU computing, and recently Toyota with its new plan to allocate one billion dollars to AI research.
You've probably looked up videos on YouTube and found that most of them contain too much math for a beginner. The few videos that promise to just present concepts are usually still too high level for someone getting started. Any videos that show complicated code just make these problems worse for the viewers.
There’s nothing wrong with technical explanations, and to go far in this field you must understand them at some point. However, Deep Learning is a complex topic with a lot of information, so it can be difficult to know where to begin and what path to follow.
Does this resonate with you? What are your thoughts? Please comment.
The goal of this series is to give you a road map with enough detail that you’ll understand the important concepts, but not so much detail that you’ll feel overwhelmed. The hope is to further explain the concepts that you already know and bring to light the concepts that you need to know. In the end, you’ll be able to decide whether or not to invest additional time on this topic.
So while the math and the code are important, you will see neither in this series. The focus is on the intuition behind Deep Learning – what it is, how to use it, who’s behind it, and why it’s important. You'll first get an overview of Deep Learning and a brief introduction of how to choose between different models. Then we'll see some use cases. After that, we’ll discuss various Deep Learning tools including important software libraries and platforms where you can build your own Deep Nets.