easy introduction
An Easy Introduction To Generative Adversarial Networks
Google opened the doors to its Machine Learning Crash Course, which had already been taken by more than 18, 000 Googlers, in March 2018.
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An easy introduction to unsupervised learning with 4 basic techniques
Deep Learning has gotten a lot of love from both the AI community and the general public. But most recently, researchers have started to question and doubt that deep learning is really the future of AI. The prominent deep learning techniques used today all rely on supervised learning, yet we see quite clearly that humans learn things, patterns, and concepts without much supervision at all. In a sense, our learning is quite unsupervised. Unsupervised learning doesn't get as much love and there's a few clear reasons for that.
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An Easy Introduction to Generative Adversarial Networks in Deep Learning
Generative Adversarial Networks (GANs) are classified within the group of generative models. That means that they're able to produce, i.e generate completely new, "valid" data. By valid data, we mean that the network's output should be something that we would deem acceptable for our target. To illustrate, consider an example where we wish to generate some new images for training an image classification network. Of course, for such an application we want our training data to be as realistic as possible, perhaps quite similar in style to other image classification training data sets.
An Easy Introduction to Machine Learning Recommender Systems
How does YouTube know what videos you'll watch? How does Google always seem to know what news you'll read? They use a Machine Learning technique called Recommender Systems. Practically, recommender systems encompass a class of techniques and algorithms which are able to suggest "relevant" items to users. Ideally, the suggested items are as relevant to the user as possible, so that the user can engage with those items: YouTube videos, news articles, online products, and so on. Items are ranked according to their relevancy, and the most relevant ones are shown to the user.
An easy introduction to Pytorch for Neural Networks
Deep Learning has reignited the public interest in AI. The reason is simple: Deep Learning just works. It's given us the ability to build technologies that we previously weren't able to. It's created new business opportunities and improved the technology world as a whole. To do Deep Learning, you're going to need to know how to code, especially with Python.
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