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Railroad tracks and mountains! Why not! Photo by Pixabay from Pexels A real-time look at Sentiment, Positioning Analysis, and Trend Analysis of the US Stock Market. I have been meaning to write this post for a while now. Basically this is a follow up to: A Guide to Sentiment, Positioning Analysis, and Trend Analysis of … Read moreCurrent Stock Market Thoughts

We all know how clustering works? You can think of Connected Components in very layman’s terms as a sort of a hard clustering algorithm which finds clusters/islands in related/connected data. As a concrete example: Say you have data about roads joining any two cities in the world. And you need to find out all the … Read more4 Graph Algorithms on Steroids for data Scientists with cuGraph

UFC Heavyweight, Brock Lesnar: 100% All-American muscle (and “supplements”) MMA fans and pundits love to debate which fighters should be considered the all-time greats. Many times however, these rankings are often a subjective mix of fighter records, individual biases, analyses from Reddit keyboard-warriors, and gut feelings. Is there a more data-driven way to assemble fighter … Read moreRanking The Best UFC Fighters Using PageRank and Neo4j

Bounding box regression could be your first wonderful step in the world of object detection. Wanna get started with Object Detection? Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me! ). We’ll now learn a basic concept in Object Detection called Bounding Box … Read moreGetting Started With Bounding Box Regression In TensorFlow

A general introduction to NLP and its basic pipeline using Kaggle Competition This post is my attempt to give an overview of basic concepts which might help noobs. For better understanding, I will be following a completed Kaggle competition Quora Insincere Questions Classification. Here, we are provided 1.31 million questions with labels and 376 thousand … Read moreIntroduction to Natural Language Processing for Noobs

Simpson’s paradox is a fascinating phenomena that illustrates the importance of causality in reasoning. If you haven’t already read it, you should check out The Book of Why — it’s one of the most important books of this decade. I think Simpson’s paradox is a great introduction to the value of causal reasoning. In this … Read moreSimpson’s Paradox

A less discussed, yet important, topic when it comes to the development of Machine Learning pipeline Many companies are using the power machine learning to provide prediction, recommendation, or classification on both front-end and back-end of their applications. A report from The Verge states that “Eventually, pretty much everything will have [machine learning] somewhere inside.” … Read moreBeginner Guide to Machine Learning Pipeline Monitoring

Loading in and preprocessing a data-set in ML.NET is quite different than when working with other machine learning packages/frameworks because it requires us to explicitly state the structure of our data. To do so we create a file called ModelInput.cs inside a folder called DataModels. Inside this file, we will state all the columns of … Read moreIntroduction to Machine Learning in C# with ML.NET

DIY Practical guide on Transformer. Hands-on proven PyTorch code for Intent Classification in NLU with BERT fine-tuned. This article introduces everything you need in order to take off with BERT. We provide a step-by-step guide on how to fine-tune Bidirectional Encoder Representations from Transformers (BERT) for Natural Language Understanding and benchmark it with LSTM. Chatbots, … Read moreBERT for dummies —  Step by Step Tutorial

When you are building a Supervised Machine Learning model, you want to make sure that it covers as diverse a set of data and real-world demographics as possible. The methods for ensuring that you have diverse training data for your model are a type of Active Learning called Diversity Sampling. The four types of Diversity … Read moreDiversity Sampling Cheatsheet

This post addresses the appropriate way to split data into a training set, validation set, and test set, and how to use each of these sets to their maximum potential. It also discusses concepts specific to medical data with the motivation that the basic unit of medical data is the patient, not the example. If … Read moreBest Use of Train/Val/Test Splits with Tips for Medical Data

A Peek Behind the Curtains of Bitcoin’s Price Volatility On Halloween 2008, during the last leg of the Global Financial Crisis, a person or group by the pseudonym Satoshi Nakamoto published the Bitcoin whitepaper. A few months later, on January 9, 2009, Bitcoin was released to the world and anyone could download a copy of … Read moreWhat Is Bitcoin Used For?

How to take your model from unremarkable to amazing simply by cleaning and preprocessing your data If you have a model that has acceptable results but isn’t amazing, take a look at your data! Taking the time to clean and preprocess your data the right way can make your model a star. Photo by Burst … Read moreThe Ultimate Beginner’s Guide to Data Scraping, Cleaning, and Visualization

For the past couple of years, researchers and companies have been trying to make deep learning more accessible to non-experts by providing access to pre-trained computer vision or machine translation models. Using a pre-trained model for another task is known as transfer learning, but it still requires sufficient expertise to fine-tune the model on another … Read moreNeural Architecture Search — Limitations and Extensions

Now that we have the data ready and the network that we want to train defined, it is time to build a basic training pipeline. First is defining a torch dataset and iterate through it using a DataLoader from torch.utils.data import Dataset, DataLoaderfrom torchvision import transforms, utilsclass FormsDataset(Dataset): def __init__(self, images, masks, num_classes: int, transforms=None):self.images … Read moreTrain a lines segmentation model using Pytorch

Image generated using Seurat Single-cell RNA sequencing (scRNA-seq) has offered a comprehensive and unbiased approach to profile immune cells including T cells with a single-cell resolution using next‑generation sequencing. More recently, exciting technologies such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) have been developed to extend scRNA-seq by jointly measuring multiple molecular … Read moreIntegrative analysis of single-cell multi-omics data using deep learning

I came across these terms while matching company names with their corresponding business names. However, due to confidentiality reasons I will walk you through them using some more common example. Before diving deep into CountVectorizer and HashingTF, lets first broadly understand what they do. CountVectorizer and HashingTF estimators are used to generate term frequency vectors. … Read moreCountVectorizer|HashingTF

By using my crawler as an example, snapshot above shows the output of my crawler and most importantly, you can download in CSV format by just clicking the Download CSV button. For the item_desc field, it is showing undefined because my item_desc is in JSON format, but Crawlab is not supporting the output JSON field … Read moreCrawlab — The Ultimate Live Dashboard For Web Crawler

Extend TD(λ) on Q function with Sarsa(λ) In last posts, we have learnt the idea of TD(λ) with eligibility trace, which is a combination of n-step TD method, and have applied it on random walk example. In this post, let’s extend the idea of lambda to more general use cases — instead of learning a … Read moreReinforcement Learning — TD(λ) Introduction(3)

Philosophical presuppositions of AI Symbolic AI The early successful paradigm of AI, which was considered as road to general intelligence Allen Newell, Herbert A. Simon — Pioneers in Symbolic AI The work in AI started by projects like the General Problem Solver and other rule-based reasoning systems like Logic Theorist became the foundation for almost … Read moreRise and Fall of Symbolic AI

Add face detection feature to your app using this simple code Face detection is one of the most common applications of Artificial Intelligence. From camera applications in smartphones to Facebook’s tag suggestions, the use of face detection in applications is increasing every single day. Face detection is the ability of a computer program to identify … Read moreImplement Face Detection in Less Than 3 Minutes Using Python

Uncluttering the NLP predictive modeling scene The takeaway: 5 minutes from now you will become a pro, At engineering features from natural language datasets to build a classic ML model At building embeddings that learn cool representations from complex natural language datasets to build a DNN model. NLP as a field is moving at warp … Read moreAll you need to know about NLP based predictive modeling in one diagram

Full stack development is one of the most trending topics in the market today. People have put up a lot of queries on the social media platforms regarding full stack web development. But why do these questions arise and why do you actually need a full stack developer? This is something that you will find … Read moreDecoding Basics: An Ultimate Guide To Full Stack Developers

Cluster job roles in FEC data to create animated bar races visualizing contribution by occupation Polls get plenty of attention from week to week, but the news generally lacks deep analysis of the FEC filings. The filings are rich with self-identifying donor information including job position, industry, and location. In the 2020 primary almost all … Read moreClustering FEC Quarterly Campaign Contributions

Better Interpretability Leads to Better Adoption Is your highly-trained model easy to understand? A sophisticated machine learning algorithms usually can produce accurate predictions, but its notorious “black box” nature does not help adoption at all. Think about this: If you ask me to swallow a black pill without telling me what’s in it, I certainly … Read moreExplain Your Model with the SHAP Values

We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model. Plenty has been written about deep learning frameworks such as Keras and PyTorch, and how powerful yet simple to use they are for constructing and playing with wonderful deep learning … Read moreA single function to streamline image classification with Keras

I needed to convert this dataset into a more defined structure to be able to do something useful with it. Now, my idea was to use python to clean the data and use R to render it into a visual form. The output of python program became the input to R program. So, what output … Read moreA designer’s guide to visualize a text dataset

“Never give up. Never stop believing. Never stop fighting!” As a developer with a career spanning across 2 decades, there are two glowing events, which are permanently etched within my memory. · As part of the worst-performing team which was sacked (including me). · As part of the best performing team which won the president’s … Read more5 Powerful Habits of the Most Productive Teams.

International Football Results From 1872 to 2019 This tutorial article details how the Python Pandas library can be used to explore a data-set efficiently. Specifically, this example will use the data-set, International football results from 1872 to 2019, which is available from the Kaggle website. Chiefly, this tutorial will explore simple visualizations and how they can be … Read moreSimple Football Data-set Exploration with Pandas

Market Trends Recently, the Middle East has seen a swarm of investments pooling in from domestic as well as international markets to boost their efforts in innovation and technological advancements. When Amazon accessed a regional online shopping site ‘Souq.com’ for around $580m, it became a milestone for the technology sector and digital economy in the … Read moreData Science and Machine Learning Adoption in Middle East Countries | Clevered

Next word prediction is one of the applications of NLP Natural Language Processing (NLP) is an increasingly important sub-domain of Machine Learning that deals with deriving general understanding through developing various language models from various large corpora of text. In this article, we go over 5 common types of NLP problems and their relevant models: … Read moreNatural Language Processing: A Crash Course!

Before attempting any modeling, I pre-processed the data using NLTK’s WordNet Lemmatizer to reduce the words to their base forms. I then tokenized the data using scikit-learn’s CountVectorizer, which generates a basic count of words in the text in a bag-of-words approach. This approach created a sparse matrix of approximately 4,600 unigrams and bigrams. Some … Read moreWhat Makes the News?

Implementation of Yolo v3 Model The Yolov3 is the state of the art object detection model, making it a fast and accurate real-time object detection model. Ever wondered where is the crux of Yolov3 model? The secret lies in the Yolo Layer of the Yolo Model. The Yolov3 Architecture takes in a 416×416 image process … Read moreCalculating Loss of Yolo (v3)Layer

What lies beneath : like the water in this peaceful river, your database can conceal any number of problems. (photo: Author) Before jumping in , understand the true state of your data. Many organizations store their data in SQL compliant databases. It is far from unheard of for those databases to have grown organically for … Read moreSimple Data Profiling with SQL

Focus Area I: Defining purpose of knowledge for the enterprise Challenge: More information, less understanding Possible solution: Mission-performance-value knowledge assessment We are living in a highly information intense environment today. However, for enterprises, not all forms of information and knowledge are relevant. At the highest level, the purpose of knowledge should be derived (or understood) … Read moreIndustry 4.0: Evolution to knowledge-based enterprises

Step One: Design First, solidify your purpose and the most important take-away for your audience. To avoid wasting time rebuilding your diagram or building an ineffective Sankey diagram, here are some questions I would recommend asking yourself before you start: Are you using this Sankey for exploratory data analysis? Are you using it to tell … Read moreThe What, Why, and How of Sankey Diagrams

Originally published at OpenGenus IQ. Assume you are given a task to fill a bag with 10 Kg of sand. You fill it up till the measuring machine gives you a perfect reading of 10 Kg or you take out the sand if the reading exceeds 10kg. Just like that weighing machine, if your predictions … Read moreImportance of Loss Function in Machine Learning