This post is a sort of diary that records all of my training and races from January 2021 to July 2021 when I was in China during the Covid-19 pandemic. Due to an incident during an ultra-trail race in the province of Gansu at the end of May 2021 that provoked the death of many runners, most of the races that had to take place … Continue reading The training schedule of a Spartan in China amid Covid-19
Given a string s and some strings words of the same length. Find all the starting positions of the substrings in s that can be formed by concatenating all the strings in words. Note that the substrings must exactly match the strings in words, and there can be no other characters in the middle, but there is no need to consider the order of the … Continue reading Concatenation of all combinations of words
Generate custom template code to train your 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 a high level of flexible hyperparameters customization. The generated code can be easily downloaded as .py file or Jupyter Notebook so as to immediately start training your model or use it as a … Continue reading Automatic code generator for training Reinforcement Learning policies
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 have 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
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
In this post, we propose to review the 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 researchers 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 on … Continue reading SeqGAN: text generation with generative models
In this post, I’m going to share my experiences on how I prepared for my first Super Spartan Race and my impressions about it. The race took place on November 2020 in Xiamen, a coastal city in China dominated by warm weather all around the year. Before we start, let’s try to understand: what is a Super Spartan Race? There’s nothing mediocre about this middle … Continue reading How I prepared my first Super Spartan Race
Given an array v of n numbers, where n=2k, k>0 be a natural number, find the minimum and maximum element in v. There are no assumptions regarding the orders of the elements of v. A basic iterative approach would require 2(n-1) comparisons (n-1 comparisons to find the minimum and n-1 comparisons to find the maximum). However, your program MUST perform at most 3/2n-1 comparisons. Difficulty: … Continue reading Min-max in array
Given an array v of distinct numbers, and a number k where k is smaller than the size of v, find the k-th smallest element in the array. A simple solution would be first sorting the array in growing order and than selecting the k-th element which can be done in θ(n logn). However, your program MUST run in θ(n) on average. Difficulty: Medium. Input There … Continue reading K-th smallest element in unsorted array
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
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