Automatic code generator for training Reinforcement Learning policies

Generate custom template code to train you reinforcement learning policy using a simple web UI built with streamlit. It includes different environments and can be expanded to support multiple policies and frameworks with an high level of flexible hyperparameters customization. The generated code can be easily downloaded as .py file or Jupyter Notebook so to immediately start training your model or use it as a baseline … Continue reading Automatic code generator for training Reinforcement Learning policies

How Genify used a Transformer-based model to build a recommender system that outperforms industry benchmarks

The rapid ascension of AI, and more recently of deep learning, comported a succession of many breakthroughs in the field of computer science. These have had a profound impact on both the academic and the business world. In particular, modern deep learning techniques applied to the pre-existing concept of recommender systems has given birth to a new, superior class of neural recommender systems, which are … Continue reading How Genify used a Transformer-based model to build a recommender system that outperforms industry benchmarks

Genify’s experience testing Amazon Personalize: learnings and limitations

Challenges of machine learning Machine learning is a complex field that borrows elements from different areas such as computer science, algebra and statistics. Hence, it is not immediate, even for experts in the field, to build strong machine learning models to solve predefined task. Furthermore, those models should also be optimized with a time-consuming and repetitive hyper-parameters search in order to find the best set … Continue reading Genify’s experience testing Amazon Personalize: learnings and limitations

SeqGAN: text generation with generative models

In this post we propose to review recent history of research in the Natural Language Generation (NLG) tasks of the Natural Language Processing domain. Realistic human-like language generation has been a challenge for researches that has recently come into greater focus with the release of large neural models for NLP like the GPT and BERT models. In this post we propose to focus ourselves on … Continue reading SeqGAN: text generation with generative models

Adversarial policies: attacking TicTacToe multi-agent environment

In a previous post we discussed about the possibility for an attacker to fool image classification models by injecting adversarial noise directly to the input images. Similarly, in this post we are going to see how is it possible to attack deep reinforcements learning agents on multi-agent environments (where two or more agents interact within the same environment) such that one or more agents are … Continue reading Adversarial policies: attacking TicTacToe multi-agent environment

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

Word similarity and analogy with Skip-Gram

In this post, we are going to show words similarities and words analogies learned by 3 Skip-Gram models trained to learn words 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

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

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

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