A Visual Introduction (and Review) of Machine Learning
The following links are extremely well done visualizations which cover the important machine learning concepts. I highly recommend you check it out. In fact, they are the best visualizations of machine learning concepts on the web:
Here is another video by Luis Serrano, one of the course developers of Udacity Machine Learning Engineer Nanodegree. Must watch for beginners, and a helpful review if you have just learnt the core machine learning concepts.
Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.
I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.
This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.
Deep learning. Neural networks. Backpropagation. Over the past year or two, I've heard these buzz words being tossed around a lot, and it's something that has definitely seized my curiosity recently. Deep learning is an area of active research these days, and if you've kept up with the field of computer science, I'm sure you've come across at least some of these terms at least once.
Deep learning can be an indimidating concept, but it's becoming increasingly important these days. Google's already making huge strides in the space with the Google Brain project and its recent acquisition of the London-based deep learning startup DeepMind. Moreover, deep learning methods are beating out traditional machine learning approaches on virtually every single metric.
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Note to the Reader
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Variational autoencoders were introduced to efficiently to solve the inference problem in deep generative models. A generative model generates data by sampling as follows: z ~ p(z), x ~ p(x|z). p in this case can be modeled using deep neural networks. But using deep neural networks makes it difficult to infer z, given an x. This paper introduces a novel method based on calculus of variations to approximately infer z given x. The authors start with an intuitive objective to optimize, reduce it to the problem of optimization of a variational lower bound and finally illustrate an example using deep neural networks to model the probability distributions. In contrast to GANs (which can easily diverge even with small variations), variational autoencoders are easier to train. Similar to GANs, trained variational autoencoders have be used to generate new images and interpolate between images.