Month: September 2020

Coming from my active watch on the conversational assistant market, I give you some statistics and information on the almost 550 solutions I studied. As a conclusion, I will give you my view about the future. Source: photo by Patrick Meyer The event still seems to feel everything in the minds of those who followed … Read more An overview of the global market for chatbot solutions in 2020

Better Network Visualization in Python with Hive Plots Hiveplotlib is a new, open-source Python package for generating Hive Plots. Introducing hiveplotlib— a new, open-source Python package for generating Hive Plots. Originally developed by Martin Krzywinski, Hive Plots generate well-defined figures that allow for interpretable, visual explorations of network data. The hiveplotlib repository is visible to … Read more Introducing Hiveplotlib

Over the past decade, the adoption of electronic health record (EHR) systems in hospitals has become widespread. This transformation is due to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which allocated $30 million in incentives for hospitals and physician practices to adopt EHR systems. This digital explosion in big … Read more How GANs Can Improve Healthcare Analytics

Toon Images Convolutional Neural Network (CNN) architectures can be pretty general purpose for vision tasks. In this article, I’ll relay my experience in using the same network architecture for 18 different classification tasks. The classification tasks include facial features such as length of the chin (3 gradations), type of hair (111 types), and hair color … Read more Training the same CNN to do 18 different things and visualizing what it learned.

Efficiently exploring the parameter-search through Bayesian Optimization with skopt in Python. TL;DR: my hyperparameters are always better than yours. Explore vast canyons of the problem space efficiently — Photo by Fineas Anton on Unsplash In this post, we will build a machine learning pipeline using multiple optimizers and use the power of Bayesian Optimization to … Read more Optimizing Hyperparameters the right Way

Determining the valuation of an early-stage Startup is in most cases very challenging due limited historical data, little to no existing revenues, market uncertainty and many more. Traditional valuation techniques, such as Discounted Cash Flow (DCF) or Multiples (CCA), therefore often lead to inappropriate results. On the other hand, alternative valuation methods remain subject to … Read more Machine Learning Models For Improved Startup Valuation. | by flo.tausend

Result: ~74% Accuracy This is exactly similar to our previous classification model. There are some changes based on the nature of the problem though. Lets briefly discuss the same. Data Preprocessing You have images, but models work on matrices! Right! So, this a step that will be needed in almost all CNN-based models. Most of … Read more Cats vs Dogs —Your second end-to-end CNN Classifier in 5 minutes

Breaking down a fundamental equation in signal processing Related article: In Wave Forms, we looked at what waves are, how to visualize them, and how to deal with null data. In this article, I aim to develop an intuition on what the Fourier Transformation is, why it is useful when studying audio, show mathematical proofs … Read more Learning from Audio: Fourier Transformations

Considerations in Favor of Post-Graduate Degrees Specifically for Data Science Introduction (Any statements made in this article are my own and not representative of any other entity, person, or organization) Data Science, as much as it can be defined as a field, is a growing field. With its unique mixture of computer science, statistics, scientific … Read more Defending the Data Science Masters

Kubeflow is an open-source project, dedicated to making deployments of ML projects simpler, portable and scalable. From the documentation: The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed … Read more Jupyter is Ready for Production; As Is

Categorical Data into Numerical Data The truth labels are categorical data: any particular image can be categorized into one of these groups: dog, cat, horse or cheetah. The computer however does not understand this kind of data and therefore we need to convert them into numerical data. There are two ways to do so: Integer … Read more Softmax Activation Function — How It Actually Works

The right graphs can be tremendously revealing — if you know what to look for As one of the contributors to the CDC’s Covid-19 “Ensemble” forecast model, I update a set of state and national graphs several times a week on my Covid-19 Spin Free Data Center. I include the charts that I personally find … Read more The Graphs You Need to Understand the Covid-19 Pandemic

Learn to create an input pipeline for images to efficiently use CPU and GPU resources to process the image dataset and reduce the training time for a deep learning model. In this post, you will learn How are the CPU and GPU resources used in a naive approach during model training? How efficiently use the … Read more How to Reduce Training Time for a Deep Learning Model using tf.data

This Owl API uses various word2vec models and advanced text clustering techniques to create a better granularity comparing to the industry standards. In fact, it uses the largest word2vec English model created by spaCy (i.e., en-core-web-lg) for the general context and uses one of the word2vec models created at Stanford University (i.e., glove-wiki-gigaword-300) for the … Read more How to Compute Word Similarity — A Comparative Analysis

Let’s begin with a set of geospatial data points: We usually do not compute Euclidean distance directly from latitude and longitude. Instead, they are projected to a geographical appropriate coordinate system where x and y share the same unit. I will elaborate on this in a future post but just note that One degree latitude … Read more Efficient Euclidean distance computation in pandas

On today’s harsh global economic conditions, traditional indicators and techniques can have poor performances (to say the least). In this tutorial we’ll search for useful information on news and transform it to a numerical format using NLP to train a Machine Learning model which will predict the rise or fall of any given Cryptocurrency (using … Read more Predict Any Cryptocurrency Applying NLP using Global News

A lot of definitions and formulations of entropy are available. What in general is true is that entropy is used to measure information, surprise, or uncertainty regarding experiments’ possible outcomes. In particular, Shannon entropy is the one that is used most frequently in statistics and machine learning. For this reason, it’s the focus of our … Read more Entropy Application in the Stock Market

Query files like CSV, JSON, and Parquet without moving it from its location Image by Author (Azure Synapse Workspace) About SQL On-Demand Creating the Data Source Gaining Access to the Azure Storage Querying Files in Azure Storage through Azure Synapse Workspace YouTube Video for Visual Description of Steps Below In my last post, I mentioned … Read more SQL On-Demand: An easier way to Query Data

Google Data Analyst Interview Questions Image from Unsplash Introduction Google LLC. is an American tech giant that offers industry-based solutions. As a company that prides itself in “providing access to the world’s information in one click”, Google offers a long list of products and services, including a huge hardware portfolio, an internet search engine, web … Read more The Google Data Analyst Interview

OBJECT DETECTION ON CLICK Simple tutorial for detecting microcontrollers on data from Kaggle competition Photo by Jonas Svidras from Pexels Standart libraries as TensorFlow or PyTorch don’t provide any simple way to train your custom Object Detection models. Most of the time you need to install a big library as Detectron 2 or Tensorflow Object … Read more Detecting microcontrollers with CNN

Lifting the curses of time and cardinality. Machine learning is research intensive. It contains significantly higher degrees of uncertainty compared to classic programming. This has a significant impact on product management and product development. Image via Shutterstock under license to Nicolas Maquaire. Developing an intelligent product with good performance is very difficult. In addition, the … Read more Best practices for Reinforcement Learning

It’s all about reading between the lines Photo by Erico Marcelino on Unsplash It is well known that data scientists spend much more time on data preparation tasks (data collection, EDA and features engineering) than on Machine Learning modelling. Although many of us might have complained about this fact, I think that underestimating the importance … Read more The Invisible Traps of Data

Basic Idea How RL Works. [Image by Author] For now, I’ll explain how RL works in high-level. Not to worry, because, in the future posts, we’ll get back to this and learn the details! The subscript t refers to the time step we are currently in. At the first time step (t=0), the agent receives … Read more Invitation to All Aspiring Reinforcement Learning Practitioner

Peer-reviewed study says you’ll soon consult Dr. Bot for a second opinion Image Credit: upklyak New research finds that causal machine learning models are not only more accurate than previous AI-based symptom checkers for patient diagnosis but, in many cases, can now exceed the diagnosis accuracy of human doctors. That’s mainly due to the methods … Read more ‘Sherlock Holmes’ AI Diagnoses Disease Better Than Your Doctor, Study Finds