Unsupervised or Indirectly Supervised Learning


Artificial Intelligence Explained: What Are Generative Adversarial Networks (GANs)?

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The field of artificial intelligence (AI) is fast-moving, and new breakthroughs are regularly made. One of the more recent terms rising to prominence is Generative Adversarial Network (GAN) – but what does it mean? Artificial Intelligence Explained: What Are Generative Adversarial Networks (GANs)? The principle behind the GAN was first proposed in 2014, and at its most basic level, it describes a system that pits two AI systems (neural networks) against each other to improve the quality of their results. To understand how they work, imagine a blind forger trying to create copies of paintings by great masters.


4,382 viewsJun 12, 2019, 12:23am Artificial Intelligence Explained: What Are Generative Adversarial Networks (GANs)?

#artificialintelligence

The field of artificial intelligence (AI) is fast-moving, and new breakthroughs are regularly made. One of the more recent terms rising to prominence is Generative Adversarial Network (GAN) – but what does it mean? Artificial Intelligence Explained: What Are Generative Adversarial Networks (GANs)? The principle behind the GAN was first proposed in 2014, and at its most basic level, it describes a system that pits two AI systems (neural networks) against each other to improve the quality of their results. To understand how they work, imagine a blind forger trying to create copies of paintings by great masters.


Gaussian Mixture Models Explained

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In the world of Machine Learning, we can distinguish two main areas: Supervised and unsupervised learning. The main difference betweeen both lies in the nature of the data as well as the approaches used to deal with it. Clustering is an unsupervised learning problem where we intend to find clusters of points in our dataset that share some common characteristics. Let's suppose we have a dataset that looks like this: Our job is to find sets of points that appear close together. Please note that we are now introducing some additional notation.


New DeepMind Unsupervised Image Model Challenges AlexNet

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While supervised learning has tremendously improved AI performance in image classification, a major drawback is its reliance on large-scale labeled datasets. This has prompted researchers to explore the potential of unsupervised learning and semi-supervised learning -- techniques that forego data annotation but have their own drawback: diminished accuracy. A new paper from Google's UK-based research company DeepMind addresses this with a model based on Contrastive Predictive Coding (CPC) that outperforms the fully-supervised AlexNet model in Top-1 and Top-5 accuracy on ImageNet. CPC was introduced by DeepMind in 2018. The unsupervised learning approach uses a powerful autoregressive model to extract representations of high-dimensional data to predict future samples.



Unsupervised learning and its role in the knowledge discovery process

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Unlike supervised learning, unsupervised learning not working with labeled data, it is not showing the machine the correct answer. Instead, it is using different algorithms to let the machine create connections by studying and observing the data. Learning and improving by trial and error is the key to unsupervised learning. However, the Knowledge Discovery process is the field of data mining is concerned with the development of methods, techniques and algorithm which can make sense of the available data. It is useful in finding trends, patterns, correlations and anomalies in the databases which is helpful to make accurate decisions for the future.


K-Means Clustering: Unsupervised Learning for Recommender Systems

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Unsupervised Learning has been called the closest thing we have to "actual" Artificial Intelligence, in the sense of General AI, with K-Means Clustering one of its simplest, but most powerful applications. I am not here to discuss whether those claims are true or not, as I am not an expert nor a philosopher. I will however state, that I am often amazed by how well unsupervised learning techniques, even the most rudimentary, capture patterns in the data that I would expect only people to find. Today we'll apply unsupervised learning on a Dataset I gathered myself. It's a database of professional Magic: The Gathering decks that I crawled from mtgtop8.com, an awesome website if you're into Magic: the Gathering.


NVIDIA Blog: Supervised Vs. Unsupervised Learning

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There are a few different ways to build IKEA furniture. Each will, ideally, lead to a completed couch or chair. But depending on the details, one approach will make more sense than the others. Getting the hang of it? Toss the manual aside and go solo.


This eye does not exist

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Since I had zero experience with generative adversarial networks, I thought I should document some problems I had to overcome. Quoting Wikipedia: "A generative adversarial network (GAN) is a class of machine learning systems. This technique can generate photographs that look at least superficially authentic to human observers, having many realistic characteristics. It is a form of unsupervised learning." I'm not doing any introduction about how a GAN works since there are a lot of materials online with far better insights than the ones I could give.


Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data: Ankur A. Patel: 9781492035640: Amazon.com: Books

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Most of the successful commercial applications to date--in areas such as computer vision, speech recognition, machine translation, and natural language processing--have involved supervised learning, taking advantage of labeled datasets. However, most of the world's data is unlabeled. In this book, we will cover the field of unsupervised learning (which is a branch of machine learning used to find hidden patterns) and learn the underlying structure in unlabeled data. According to many industry experts, such as Yann LeCun, the Director of AI Research at Facebook and a professor at NYU, unsupervised learning is the next frontier in AI and may hold the key to AGI. For this and many other reasons, unsupervised learning is one of the trendiest topics in AI today.