Creating AI for GameBoy Part 4: Q-Learning and Variations
6 hours ago, towardsdatascience

When the boss dies, he asks us a fitting questionCreating AI for GameBoy Part 4: Q-Learning and VariationsThe part we’ve all been waiting forHello and welcome to part 4 of Building an AI for Gameboy! This is where the real magic happens — we’ve built our tools and now it is time to set them in motion. A quick recap of our journey so far: first, we built a controller so that we could run the game using Python scripts; second, we used a combination of open source and homemade image processing tool...

Colorizing Old B&W Photos and Videos With the Help of AI
6 hours ago, towardsdatascience

This project is based on a research work developed at the University of California, Berkeley by Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful Image Colorization.The idea behind this tutorial is to develop a fully automatic approach that will generate realistic colorizations of Black & White (B&W) photos and by extension, videos. As explained in the original paper, the authors, embraced the underlying uncertainty of the problem by posing it as a classification task using class-rebal...

Evaluating Keras neural network performance using Yellowbrick visualizations
6 hours ago, towardsdatascience

Photo by Mockaroon on UnsplashIf you have ever used Keras to build a machine learning model, you’ve probably made a plot like this one before:{training, validation} {loss, accuracy} plots from a Keras model training runThis is a matrix of training loss, validation loss, training accuracy, and validation accuracy plots, and it’s an essential first step for evaluating the accuracy and level of fit (or overfit) for our model. But there are many nuances to model performance that escape these simple ...

BERT in Keras with Tensorflow hub
9 hours ago, towardsdatascience

At Strong Analytics, many of our projects involve using deep learning for natural language processing. In one recent project we worked to encourage kids to explore freely online while making sure they stayed safe from cyberbullying and online abuse, while another involved predicting deductible expenses from calendar and email events.A key component of any NLP project is the ability to rapidly test and iterate using techniques. Keras offers a very quick way to prototype state-of-the-art deep lear...

Data science productionization: portability
10 hours ago, towardsdatascience

Portability decreases the time it takes to get value from your code by decreasing the amount of code you have to rewrite when your goals change.This is the second part of a five-part series on data science productionization. I’ll update the following list with links as the posts become available:What does it mean to “productionize” data science?PortabilityMaintenanceScaleTrustThe first step to productionizing data science is to make it portable. To explain what I mean, let’s look at a simple exa...

Playing Poker on Mars: How AI Mastered the Game
10 hours ago, towardsdatascience

Or, the Edge of Trillions of HandsBy Dirk Knemeyer and Jonathan FollettFigure 01: Poker, the quintessentially human game of gamblers and dreamers[Illustration: Le Poker (Poker) by Félix Vallotton, 1896 woodcut, National Gallery of Art, Open Access]Poker seems like a quintessentially human game. At a superficial level poker is a more casual, social, and approachable strategy game than chess or Go. Gambling is inextricably wound into poker, bringing professional gamblers on one extreme and recrea...

From software engineering to Data Science: What resources helped me?
10 hours ago, towardsdatascience

Almost 2 years ago, I took the decision to quit my job as a software engineer and to start looking for a job in the machine learning field. Right away after quitting my job, I wrote an article in my blog Up to my new Tech challenges and from there the journey started.Photo by Ian Schneider on UnsplashIn this article, I’m happy to share how I landed the job I dreamt of. Yeah, I got it! I’ve been working as a Data Scientist for Remerge, in Berlin, for one year.Let’s start now!Choose the right cour...

Review: MR-CNN & S-CNN — Multi-Region & Semantic-aware CNNs (Object Detection)
10 hours ago, towardsdatascience

Review: MR-CNN & S-CNN — Multi-Region & Semantic-aware CNNs (Object Detection)Using Multi-Region Features and Semantic Segmentation Features for Object DetectionPASCAL VOC 2012 DatasetIn this story, an object detection approach using MR-CNN & S-CNN, by Université Paris-Est, is reviewed. Two Convolutional Neural Network (CNN) pathways are proposed:Multi-Region CNN (MR-CNN): Object representation using multiple regions to capture several different aspects of an object.Segmentation-aware CNN (S-CNN...

Building a Music Recommendation Engine with Probabilistic Matrix Factorization in PyTorch
11 hours ago, towardsdatascience

Recommendation systems are one of the most widespread forms of machine learning in modern society. Whether you are looking for your next show to watch on Netflix or listening to an automated music playlist on Spotify, recommender systems impact almost all aspects of the modern user experience. One of the most common ways to build a recommendation system is with matrix factorization, which finds ways to predict a user’s rating for a specific product based on previous ratings and other users’ pref...

Data Cleaning with R and the Tidyverse: Detecting Missing Values
14 hours ago, towardsdatascience

Data cleaning is one of the most important aspects of data science.As a data scientist, you can expect to spend up to 80% of your time cleaning data.In a previous post I walked through a number of data cleaning tasks using Python and the Pandas library.That post got so much attention, I wanted to follow it up with an example in R.In this post you’ll learn how to detect missing values using the tidyr and dplyr packages from the Tidyverse.The Tidyverse is the best collection of R packages for data...

The technologies that every analytics group needs to have
17 hours ago, towardsdatascience

Four game-changing technologies can enable a world-class analytics groupAs data becomes more and more pervasive in workplaces, many executives are now realizing the value of having an analytics function to support their mission, whatever that is. However, some think that by hiring a few analysts or data scientists they have done all they need to do.Building a great analytics function is not just about having the right people. It’s also about setting the environment up in a way that encourages hi...

10-Step guide to schedule your script using cloud services
17 hours ago, towardsdatascience

10-Step guide to schedule your script using cloud services (for free)credit: https://www.pexels.com/photo/bandwidth-close-up-computer-connection-1148820/Why can’t I just run it on my laptop?Well…, you can run it locally and I believe many people probably know task scheduler function if you are Windows users. We set the trigger time and conditions, the script will be run based on our pre-defined criteria. However, the drawback on this is that you need to keep your computer ‘power on’ at the sched...

What Are The Pros And Cons Of Using Vue.js
17 hours ago, towardsdatascience

Vue.js is a well-known term in the development of state-of-the-art web applications. It is currently one of the most emerging front-end technologies that are often mentioned in connection with Angular and React.js. Basically, Vue.js is similar to React.js, which is a JavaScript library.Vue.js, like React.js, is an open-source library, but unlike React.js and Angular, it supports compact file size. In fact, Vue.js is a combination of Angular and React.js. because it uses concepts such as directiv...

Learn Machine Learning and Computer Vision using Chicken Rice
22 hours ago, towardsdatascience

I spoke at the recent PechaKucha on “How to replace A.I. before it replaces you“. To demonstrate how smart the machine learning algorithm we have today, I gave an example that we can easily train a machine learning model to differentiate different types of chicken rice (A typical Malaysian / Singaporean favorite cuisine).Many participants contacted me right after the event and asked questions like:Is that real? Can we really train machine models to recognize chicken rice?Other than chicken rice,...

How I won the Flipkart ML challenge
1 day ago, towardsdatascience

Flipkart recently hosted its month-long annual machine learning challenge for students of Indian engineering colleges, with a total prize pool of ₹5,20,000 (~7,500 USD). My team won, out of about 6700 participants. Before I tell you how we did it, let’s keep in mind that I’d probably struggle to keep within the top 10%, had this challenge not been confined to such a small target demographic. As an aside, this blog will mostly be theoretical.The problem statementGiven an image, localize the predo...

Model Based Reinforcement Learning
1 day ago, towardsdatascience

A top view of how Model Based Reinforcement Learning works.Photo by Sebastian Herrmann on UnsplashIntroductionIf you have ever played a real-time strategy game (RTS) such as Age of Empires or others, you surely know that you start by an almost black screen. The first thing you do is to send units in every direction to scout the terrain and discover the enemy location, as well as the strategic locations and resources such as mines, forests etc…What you are actually doing is in fact building a map...

How To Query the Future
1 day ago, towardsdatascience

The snapshot images that proved that horses do leave the ground, and sparked the invention of the motion picture camera.A new physics of business intelligence, and thinkingBI hasn’t had a breakthrough in a decade.10 years ago, visual analytics pioneers ushered in a new era of business intelligence with tools that connected data at the speed of thought. A $16B software market was born.But since then, improvements have been incremental. Location analytics help put data in a geographical context. N...

Kaggle vs. Colab Faceoff — Which Free GPU Provider is Tops?
1 day ago, towardsdatascience

Kaggle vs. Colab Faceoff — Which Free GPU Provider is Tops?Specs, UX, and deep learning experiments with fastai and mixed precision trainingGoogle has two products that let you use GPUs in the cloud for free: Colab and Kaggle. They are pretty awesome if you’re into deep learning and AI. The goal of this article is to help you better choose when to use which platform.Kaggle just got a speed boost with Nvida Tesla P100 GPUs. 🚀 However, as we’ll see in a computer vision experiment, Colab’s mixed-pr...

How to Evaluate Potential Clients - Data Science Maturity
1 day ago, towardsdatascience

An actionable checklist ✅Photo by Amogh Manjunath on UnsplashGreat news! The market for data science skills has been, is, and will very likely continue to be terrific.On the supply side, bootcamps and MOOCs are revolutionizing education and will continue to allow so many talented individuals from various backgrounds to take part in the spoils of the data economy. Bright academic minds coming out of PhD programs have a fantastic and real opportunities to build life-changing commercial solutions.A...

Predicting “Bikeability” in U.S. Cities
1 day ago, towardsdatascience

According to the United Nations, more than half of the world’s population now live in cities. As the worldwide poverty rate continues to fall and people get richer, the number of private vehicles on the roads has surged. These dual phenomena mean higher traffic congestion, which, in turn, exacerbates climate-change-causing greenhouse gas emissions. Alternative “green” modes of transit, namely, walking, scootering, and biking, can improve urban mobility and help cities to meet emissions-reduction...

Next