Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”). GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.” Read More
Tag Archives: Machine Learning
Generative Adversarial Nets
A new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. Read More
Style-based GANs – Generating and Tuning Realistic Artificial Faces
Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces. While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face. Read More
Is a master algorithm the solution to our machine learning problems?
Suppose there’s an algorithm that knows what we’re searching for on Google, what we’re buying on Amazon and what we’re listening to on Apple Music or watching on Netflix. Now this algorithm knows a lot about us and has a better and more complete picture of us. This powerful “master algorithm” is at the heart of work postulated by Pedro Domingos, author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Read More
The Coming Revolution in Recurrent Neural Nets (RNNs)
Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don’t fit well. Several alternatives are already in use and one that’s just been introduced, ODE net is a radical departure from our way of thinking about the solution. Read More
10 Best Frameworks and Libraries for AI
This article looks at top-quality libraries that are used for artificial intelligence, their pros and cons, and some of their features. Let’s dive in and explore the world of these AI libraries! Read More
Machine Learning and AI Frameworks: What’s the Difference and How to Choose?
There are many machine learning frameworks. Given that each takes much time to learn, and given that some have a wider user base than others, which one should you use? Here we look briefly at some of the major ones. Read More
Different types of Machine learning and their types.
Supervised and unsupervised are mostly used by a lot machine learning engineers and data geeks. Reinforcement learning is really powerful and complex to apply for problems. Read More
Types of Machine Learning Algorithms You Should Know
This post explains the types of machine learning algorithms and when you should use each of them. Read More
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