Deep learning is all the rage. So what do you need for deep learning?
Step 1: Understand machine learning in general
- The math you’ll need
- Get your basics in Python down
- Make sure you understand the necessary statistics
- And then it’s time to master the basics of machine learning
Does your machine have the necessary requirements?
You’ve got to have a good enough GPU and CPU. The basic levels is a 4 GB GPU and a decent CPU.
Step 2: Get started with deep learning
Check out the very informative Hacker Guide to Neural Networks by Andrej Karpathy who learned what he knows at Stanford. Blogs not your thing? Prefer a textbook? Then grab the free online book Neural Networks and Deep Learning written by Michael Nielsen.
That not your cup of tea either? Then you’ll be glad to know that there is also the possibility to get the video. It has been very conveniently divided up into 27 parts so that you can really digest one bit before you decide to head on to the next one.
But wait, there’s more for you to learn! You’ve also got to get to grips with the different deep learning libraries and software packages. Yup, I never said it was going to be easy. To get a good idea of what’s going on check out the Wikipedia page.
Step 3: Choosing your area
Deep learning has found its way into several fields. This includes vision, natural language processing, speech and audio, and reinforcement learning. You can choose each one of these areas to really start to get to grips with the topic.
Deep Learning for Computer Vision
read the DL for Computer Vision blog, which will give you the basic ideas that you’re going to need to delve into this particular project. The project itself is called Facial Keypoint Detection. And what of the library, you ask. I’m glad you did. It’s called Nolearn.
Deep Learning for Natural Language Processing
Here the primer is called Deep Learning, NLP, and Representations. With it, you’ve got the opportunity to build chatbots, which – as you no doubt know – is an incredibly fast-growing area within the computer science community with a huge number of companies jumping on board in order to please customers without having to keep a huge staff of living reps on hand to handle the traffic.
Check out these two parts of the project that you will need. The library that you’ll be required to use is the Tensorflow library.
Deep Learning for Speech/Audio
It’s incredible to think that only a few years ago it wasn’t possible for computers to recognize different types of speech. That’s all changed now, with computers no longer calling mom every time you try to dial anybody else.
The blog post you should look into is called Deep Speech: Lessons from deep learning. The project title is called Music Generation Using Magenta (Tensorflow) and the necessary library? This will surprise you. It’s called Magenta. A real shocker, don’t you think?
Deep Learning for Reinforcement Learning
Reinforcement learning allows computers to get better through the process of trial and error, which honestly is pretty cool and has made it so that computers can now beat humans at more traditional games. Can you take it to the next level and have computers beat us at the more complex and involved modern games?
If you think you can then you should check out Deep Reinforcement Learning: Pong from Pixels. This will serve you as both the primer and the project. You’ll also be happy to learn that you don’t need any library (was happy too strong a word?).
Step 4: BUILD simple something first
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