This is a quick dive into the trove of Chinese state troll tweets released by Twitter on Aug 19. More to come in the coming days and weeks. An example of Chinese state troll tweet exposed by Twitter on Aug 19. On August 19, Twitter dropped a new trove of state troll tweets that the … Read moreTARGET HK: A Quick Dive Into China’s Disinformation Campaign On Twitter

We can see that it is organised by layers and contains details such as colour, presence of roots, descriptions of the pores, textural class (estimated proportion of clay, silt and sand), etc. Most of the time the descriptions follow some recommended format but they might contain more or less free-form text depending on the study. … Read moreGeoVec: word embeddings for geosciences

As you progress in your career as a data scientist, you will inevitable come across the Kullback–Leibler (KL) divergence. We can think of the KL divergence as distance metric (although it isn’t symmetric) that quantifies the difference between two probability distributions. One common scenario where this is useful is when we are working with a … Read moreKL Divergence Python Example

V. Test Set Results and Interpretability of Predictions A. Out-of-Sample Results First, to gauge the model’s ability to generalize on unseen data, let’s have a look at the test set loss. Figure 5 plots the ensemble loss relative to its validation loss (dashed black line normalized to 1). The red line draws the average loss … Read moreDeep Learning and Momentum Investing

This guide is based on this repo. Some sections of the code will be skipped or modified for readability of the article. Initial Setup First, we need to import the necessary libraries. This guide assumes all libraries are already installed locally. We’re declaring the necessary libraries and hooking Syft with Torch. To demonstrate how PATE … Read moreMaking PATE Bidirectionally Private

Netflix relies on data to deliver personalized experiences for 130 million Netflix members worldwide. According to the Netflix Tech Blog: Every day more than 1 trillion events are written into a streaming ingestion pipeline, which is processed and written to a 100PB cloud-native data warehouse. And every day, our users run more than 150,000 jobs … Read moreData Science Roles: A Classification Problem

You should feel very accomplished if you get to the on-site interview, but the hardest part is yet to come! On-sites can be grueling affairs of interviewing with 4–10 people in 3–6 hours, especially if you’re not prepared. Knowing what to expect and doing realistic preparation beforehand go a long way toward reducing fear and … Read moreHow to Prepare for Your Data Engineering Interview

A few weeks ago while in lecture, I was asked the following question: “What’s the likelihood of making a living by playing the lottery?” Not very high you think? Well in 2005, a group of MIT students got together and formed a betting syndicate. They had found the game they wanted to bet on, calculated … Read moreYou Don’t Have to be Struck by Lightning to Win the Lottery

In this guide, I’ll use BERT to train a sentiment analysis classifier and Cortex to deploy it as a web API on AWS. The API will autoscale to handle production workloads, support rolling updates so that models can be updated without any downtime, stream logs to make debugging easy, and support inference on CPUs and … Read moreDeploying BERT in production

Automatic Investing on Lending Club Using a Neural Network while Controlling Risk in Loan Selection In this article I describe how to train a neural network to evaluate loans that are offered on the crowd lending platform Lending Club. I also cover how to test the model, how to adjust the risk in loan selection, … Read moreIntelligent Loan Selection for Peer-to-Peer Lending

A guide for getting started NLP and spaCy A major challenge of text data is extracting meaningful patterns and using those patterns to find actionable insights. NLP can be thought of as a two part problem: Processing. Converting the text data from its original form into a form the computer can understand. This includes data … Read morespaCy Basics

When applying deep Reinforcement Learning (RL) to robotics, we are faced with a conundrum: how do we train a robot to do a task when deep learning requires hundreds of thousands, even millions, of examples? To achieve 96% grasp success on never-before-seen objects, researchers at Google and Berkeley trained a robotic agent through 580,000 real-world … Read moreSimulate Images for ML in PyBullet — The Quick & Easy Way

The main aim of the local Docker container is to maintain as much as possible the most important features of the AWS-hosted instance while enhancing the experience with the local-run capability. Followings are the features that have been replicated: Jupyter Notebook and Jupyter Lab This is simply taken from Jupyter’s official Docker images with a … Read moreRun Amazon SageMaker Notebook locally with Docker container

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend … Read moreMeta-Transfer Learning for Few-shot Learning

A quick Simulation and Optimization study for rapid fast delivery. Instacart, Amazon Prime now, Farmstead and many more startups in delivery space are tackling very interesting supply, demand, simulation and logistic optimization problems. Back in 2018 my cofounder Ricky Wong and I wanted to validate a radical grocery delivery idea : get groceries delivered under … Read moreCan you have your groceries delivered in under 15 minutes?

TLDR: Google TTS -> Noise augment -> {wav files} ->SnowBoy ->{.pmdl models} -> Raspberry Pi OK, so it’s that time of the year again. You know there’s *that* thing in the desert. Last time around, I rigged up a Google AIY vision kit and added espeak on Chip and Terra , the art installations of … Read moreBuild your own custom hotword detector with zero training data and $0!

Deep Learning has gotten a lot of love from both the AI community and the general public. But most recently, researchers have started to question and doubt that deep learning is really the future of AI. The prominent deep learning techniques used today all rely on supervised learning, yet we see quite clearly that humans … Read moreAn easy introduction to unsupervised learning with 4 basic techniques

Overfitting, the process by which a model performs well for training samples but fails to generalize, is one of the main challenges in machine learning. In the proceeding article, we’ll cover how we can use regularization to help prevent overfitting. To be specific, we’ll talk about Ridge Regression, a distant cousin of Linear Regression, and … Read moreRidge Regression Python Example

The most important non-technical skill for a Data Scientist According to Cameron Warren, in his Towards Data Science article Don’t Do Data Science, Solve Business Problems, “…the number one most important skill for a Data Scientist above any technical expertise — [is] the ability to clearly evaluate and define a problem.” As a data scientist … Read moreDefining A Data Science Problem

So how did I calculate that a team has 23% better odds if they have an extra timeout at the end of the game? Using the same dataset, I ran a logistic regression algorithm that used the time left remaining in the game (in seconds), if the team with possession of the ball was the … Read moreDon’t Show Your Stupidity By Wasting A Timeout