Category: Machine Learning

Machine Learning, NLP

Sentences sentiment analysis with CNN

Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to the voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range … Continue reading Sentences sentiment analysis with CNN

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Machine Learning, NLP

Word similarity and analogy with Skip-Gram

In this post, we are going to show word similarities and word analogies learned by 3 Skip-Gram models trained to learn word embedding from a 3GB corpus size taken scraping text from Wikipedia pages. Skip-Gram is unsupervised learning used to find the context words of given a target word. During its training process, Skip-Gram will learn a powerful vector representation for all of its vocabulary … Continue reading Word similarity and analogy with Skip-Gram

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Deep Learning, Machine Learning

RNN: Recurrent Neural Networks

In normal feed-forward neural networks the activation flows only in one direction, from the input layer to the output layer, eventually passing through a set of hidden layers. Conversely, recurrent neural networks (RNN) have also connections pointing backward, thus allowing them to take also the temporal dimension into account. This novel architecture enables them to take as their input not just the current input xi … Continue reading RNN: Recurrent Neural Networks

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Computer Vision, Machine Learning

Generating new Anime faces with DCGAN

If I ask you the question “do you like anime characters?”, then it’s very likely that most of you would answer “yes” and that some of you would even admit that anime has been part of their childhood. Although most people, regardless their age, enjoy watching them, only a few people can actually draw them from scratch and even less people have mastered this skill … Continue reading Generating new Anime faces with DCGAN

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Machine Learning, Unsupervised Learning

Teaching AI to play Snake with Genetic Algorithm

Supervised learning, unsupervised learning, and reinforcement learning are commonly recognized as the three main ways to train machine learning models. We can have a fourth one if we include the union of the first two, that is, semi-supervised learning. However, in this post, we are going to introduce an alternative algorithm that can be used to both train and optimize neural network models: Genetic Algorithm. … Continue reading Teaching AI to play Snake with Genetic Algorithm

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Machine Learning, Reinforcement Learning

Teaching AI to play Snake with Reinforcement Learning

It is well known that two of the most fascinating fields of computer science are gaming and artificial intelligence. The gaming field saw its origins back in the 1970s when gaming consoles such as Atari 2600, along with graphics on computer screens and home computer games were introduced to the general public giving birth to different kinds of arcade games like Pong and Pacman. In … Continue reading Teaching AI to play Snake with Reinforcement Learning

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Machine Learning, Reinforcement Learning

Introduction to Deep Reinforcement Learning

Deep Reinforcement Learning is the result of the combination of two well-known machine learning approaches: Deep Learning and Reinforcement Learning. Its main goal is the one to create a single agent able to handle any human-level task but achieving super-human results on it. A famous AI implementing this technique is AlphaGo that, in March 2016, defeated for the first time in the history a 9-dan … Continue reading Introduction to Deep Reinforcement Learning

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Machine Learning, Unsupervised Learning

Donut: unsupervised anomaly detection using VAE

In this post, we are going to use Donut, an unsupervised anomaly detection algorithm based on Variational Autoencoder which can work when the data is unlabeled but can also take advantage of the occasional labels when available. In particular, we are going to focus on detecting anomalies on time series KPIs (key performance indicators) which are time-series data, measuring metrics such as the number of … Continue reading Donut: unsupervised anomaly detection using VAE

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Machine Learning, Reinforcement Learning

Balancing CartPole with policy gradients algorithm

In this post, we are going to analyze a type of reinforcement learning algorithm called policy gradients and we will use it to train an agent to balance a pole on a moving cart. This type of environment is also known as CartPole. In the field of reinforcement learning, we have an agent making observations and taking actions within an environment in order to receive … Continue reading Balancing CartPole with policy gradients algorithm

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Machine Learning, Unsupervised Learning

GAN: Generative Adversarial Networks

Imagine a scenario where a forger attempts to produce fake currencies and the policeman has to try to distinguish those fake currencies from the real ones. At the beginning, both don’t have much experience, the forger will just come with a piece of paper with a dollar bill scribbled on it. Obviously, is that is a fake currency, but the unexperienced policeman still will struggle … Continue reading GAN: Generative Adversarial Networks

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