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The art of creating self-imposed deadlines is crucial not only to go above and beyond meeting requirements, but also to make our working progress in achieving small and big goals a lot smoother. Love them or hate them; they are incredibly motivational deadlines! However, here are two things we should try to avoid when dealing … Read moreUtilize Your Self-Imposed Deadlines | Punch Today in The Face

After we have defined the matrix multiplication strategy, its time to defined the ReLU function and the forward pass for the Neural Network. I would request the readers to go through the Part — 1 of the series to get the background of the data used below. The Neural Network is defined as below: output … Read moreSteps to basic modern NN model from scratch

Code it! Suppose we flip a fair coin 10 times. Unfortunately we get 7 heads and 3 tails. The evidence that the coin is fair is weak. The posterior looks like this: the peak occurs at 70%, because we get 7 heads out of 10 trials. To build credible interval, we simply truncate a left … Read moreDo You Know Credible Interval

Quickly turn data into something insightful. Shutterstock by majcot Is there a such thing as data visualization block for analysts? Like a painter who stares at a blank canvas or a writer who can’t write that first sentence — you might be an analyst just staring at a wall of data in Excel. Or maybe … Read more5 Amazing Tips for Data Visualization

Big Questions: After an A/B testing, to what extent can we trust our small sample can represent the entire population of our customers? If we repeatedly sample the same size, how would our estimates vary? If we obtain different estimators after repeated sampling, can we gauge the distribution of the population? If we don’t know … Read moreA Quick Short Look Into Bootstrapping

If you’re into math equations, let us now turn to formal representations of the theorems in order to understand their claims and the relationship between the two a bit more precisely. Let be independent and identically distributed random variables with expected value μ and finite variance σ². Then converges towards the Standard Normal Distribution in … Read moreCentral Limit & Large Numbers

Building a machine learning model that can identify high-risk states in 2019. According to the Center for Disease Control, “About 610,000 people die of heart disease in the United States every year–that’s 1 in every 4 deaths.” It is unlikely anyone reading this hasn’t been affected by this disease in some way. I, myself, lost … Read morePredicting Heart Disease Mortality

And why it is so important You’ve heard the news — deep learning is the hottest thing since sliced bread. It promises to solve your most complicated problems for the small price of an enormous amount of data. The only problem is you are not working at Google nor Facebook and data are scarce. So … Read moreHow To Use Deep Learning Even with Small Data

1. Find a comfortable working space Most programming and coding jobs are flexible enough that allow to work from home, a common space, a library or even a coffee shop, without having to be at an office 8 hours per day 5 days per week. However, the working environment will always have a highly significant … Read moreHow to code effectively without dying in the attempt

A step-by-step Guide to Design of Experiments Data science professionals, have you ever faced any of the following challenges? Story 1: Machine learning does not mean experimental design You are asked to design an experiment due to your statistical expertise, but realized your machine learning tools do not help you design an experiment. Story 2: … Read moreDesign of Experiments for Your Change Management

In my application, I used a simple convolutional neural network (CNN) to classify random inputted strands of DNA (around 500 bp long) based on which STRs were found on them. The goal of this project was when given a strand of DNA, be able to classify where it would be located based on patterns of … Read moreLeveraging CNNs for Detecting STRs in DNA

Employee benefits packages can help candidates choose to take job offers or look elsewhere. They can also factor into how long a worker stays at a company and how happy they are while there. If they realize that other companies offer better benefits and they’re frustrated with their job already, they may decide it’s not … Read moreWhy Companies Are Using Data Science and Analytics to Inform Benefits Packages

Individually reasonable correlations can cause an AI to gain a racial bias Even well-designed AI systems can still end up with a bias. This bias can cause the AI to exhibit racism, sexism, or other types of discrimination. Entirely by accident. This is usually considered a political problem, and ignored by scientists. The result is … Read moreWhy your AI might be racist and what to do about it

The development of Batch Normalization(BN) as a normalization technique was a turning point in the development of deep learning models, it enabled various networks to train and converge. Despite its great success, BN exhibits drawbacks that are caused by its distinct behavior of normalizing along the batch dimension. One of the major disadvantages of BN … Read moreAn Alternative To Batch Normalization

As a native extension of the Spark ML API, the library offers the capability to train, customize and save models so they can run on a cluster, other machines or saved for later. It is also easy to extend and customize models and pipelines, as we’ll get in detail during this article series. Spark NLP … Read moreIntroduction to Spark NLP: Foundations and Basic Components

Use Data Pipelines, Transfer Learning and BERT to achieve 85% accuracy in Sentiment Analysis Photo by Jirsak, courtesy of Shutterstock When I first started working with Deep Learning, I went through Coursera and fast.ai courses, but afterwards I wondered where to go from here. I started asking questions like “How do I develop a data … Read moreBest Practices for NLP Classification in TensorFlow 2.0

Conventional positions within the NBA do not accurately reflect the playing style or functional role a player provides to their team. The overall style of play has changed drastically and various era’s within the NBA indicate that. Similarly a player’s style of play is also reflective of this change. Currently the league is fast paced … Read moreUsing K-Means Clustering Algorithm to Redefine NBA Positions and Explore Roster Construction

The Circuitous Path to AI Writing By Dirk Knemeyer and Jonathan Follett Figure 01: Our languages and our writing mirror the societies in which we live.[Illustration: Saint Jerome Writing by Hans Springinklee German, 1522 woodcut, National Gallery of Art, Open Access] You may not be aware of it, but AI is already writing stories that … Read moreA Story Told by a Machine

The dominant narrative around who is living on the street — and why it is so difficult to help them — is that people experiencing homelessness are all drug addicts and/or severely mentally ill. This damaging narrative dehumanizes people experiencing homelessness in a cynical attempt to justify inaction. But it is also factually incorrect: according … Read moreThe City of the Homeless: Humanitarian Crisis on the Streets of Los Angeles

Neural Transfer Learning for Natural Language Processing by Sebastian Ruder Pan, S. J. and Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359. Xia, R., Zong, C., Hu, X., and Cambria, E. (2015). Feature Ensemble plus Sample Selection: A Comprehensive Approach to Domain Adaptation for Sentiment Classification. Proceedings … Read moreTransfer Learning in NLP

20.6k upvotes. 1.9k comments. Cross-posted on 10 other subreddits. Not super impressive. But is there more to these figures which supposedly reflect the success (or popularity) of the bar chart I posted on the subreddit /r/dataisbeautiful? Recently I posted a race bar chart showing the evolution of the top 10 countries of origin of international … Read moreHow Data Creates a Collective Storytelling Voice on a Global Issue

Efficiently onboard your new data champions. Your new employee will be ready to save the world in no time! Onboarding is Hard Onboarding is so important but very difficult. Most traditional jobs have clear expectations, documentation, and processes for onboarding. Data Science roles are completely different! This, of course, is a result of being a … Read moreOnboarding a New Data Scientist

Apply Basics to 2 Dimensional Space In last post, we have applied basic Bayes rules and total probability to localise a moving car in a 1-dimension world. Let’s reinforce our understanding and apply them to a 2-dimension world. Consider a 2 dimensional world, the robot can move only left, right, up, or down. It cannot … Read moreKalman Filter(2) — Grid World Localisation

Make the Next-to-last mistake Supervised Learning Supervised learning algorithms involves direct supervision of operation. We teach or train the machine using data, which means that the data is labelled with the right answer. We use an algorithm to analyse the training data and learn the function that maps inputs with their outputs. The function can … Read moreBeat The Heat with Machine Learning Cheat Sheet

GPU accelerations are commonly associated with Deep Learning. GPUs power Convolutional Neural Networks for Computer Vision and Transformers for Natural Language Processing. They do this through parallel computation, making them much faster for certain tasks compared to CPUs. RAPIDS is expanding the utilization of GPUs by bringing traditional Machine Learning and Data Science algorithms, such … Read more600X t-SNE speedup with RAPIDS

R for Industrial Engineers Creating Process Maps using R packages Image by Matteson Ellis available at FCPAmericas A problem well stated is a problem half solved. – Charles Franklin Kettering Process mapping represents a great tool for retrieving information about a process during the Define phase of the DMAIC (Define, Measure, Analyze, Improve, Control) cycle. … Read moreProcess Mapping with R

Shapley values are used in machine learning to explain the predictions of a complex predictive model, aka “black box”. In this post, I will use Shapley values to identify YouTube comment key terms that explain why a comment was predicted as either spam or legitimate by a predictive model. The “coalitions” of specific key terms … Read moreHey Model, Why Do You Say This Is Spam?

CLOUD AUTOMATIC MACHINE LEARNING TOOLS (PART 1) Start-to-finish ML demo using German Credit Data Motivation A new buzzword I hear often is “democratize AI for the masses”. Usually what follows is a suggested Cloud Machine Learning tool. The umbrella term for these tools seems to be AML, or Automatic Machine Learning. As a Data Scientist … Read moreLessons Learned Using Google Cloud BigQuery ML

Results In both examples we use a very small and simple dataset, just to demonstrate the process. data = [{‘feature1’: 1., ‘feature2’: 0., ‘feature3’: 0.3, ‘feature4’: 0.01},{‘feature1’: 10., ‘feature2’: 3., ‘feature3’: 0.9, ‘feature4’: 0.1},{‘feature1’: 101., ‘feature2’: 13., ‘feature3’: 0.9, ‘feature4’: 0.91},{‘feature1’: 111., ‘feature2’: 11., ‘feature3’: 1.2, ‘feature4’: 1.91},] Both algorithms conclude that the first sample … Read moreIsolation Forest and Spark

Cleaning Tools Data cleaning is a subject that is lightly touched in your brick&mortar or on-line classes. However, in your work as a Data Engineer or Data Scientist you will spend a great deal of your time getting ready (pre-processing) your data so that it can be input into your model. Data cleaning is critical … Read moreUncommon Data Cleaners for your Real-World Machine or Deep Learning Project

In-depth tutorial for an open-source GAN research tool Visualization of feature map 158 at a layer with resolution 64×64 This is a tutorial/technical blog for a research tool I’ve been working on as a personal project. While a significant portion of the blog assumes you have access to the tool while reading it, I attempted … Read moreAnimating gAnime with StyleGAN: The Tool

How do we interpret the shape of a p-value distribution? Example 1: Uniform P-value Distribution Suppose the null hypothesis says a random variable follows a normal distribution with mean 0 and variance 1. As depicted above, the p-value distribution will closely resemble a uniform distribution if the sample follows the null distribution. This is because … Read moreHow to Test Your Hypothesis Using P-Value Uniformity Test

As Industry 4.0 continues to generate media attention, many companies are struggling with the realities of AI implementation. Indeed, the benefits of predictive maintenance such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. Needless to say that the implementation of ML-based solutions can lead to major … Read moreHow to Implement Machine Learning For Predictive Maintenance