You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?
A growing consensus of researchers contend that new algorithms are needed to transform narrow AI to AGI. Brain Simulator II is free software for new algorithm development targeted at AGI that you can experiment with and participate in its development.
Interactive visualizations are an effective method for understanding the COVID-19 pandemic. This article presents a repository filled with just such insightful interactions.
So in this article, we will interpret, analyze the COVID-19 DNA sequence data and try to get as many insights regarding the proteins that made it up. Later will compare COVID-19 DNA with MERS and SARS and we’ll understand the relationship among them.
The COVID-19 pandemic has affected everything, and building predictions during this time is difficult. Data science teams need to update their models to prepare for the recovery, and know how to properly train 2020 data models to learn from the coronavirus anomaly.
With the capability to analyze huge amounts of data, including medical information, human behavior patterns, and environmental conditions, big data tools can be invaluable in dealing with deadly outbreaks.
Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.
How can you keep your focus and drive during a global crisis? Take on a 90-day learning challenge for data science and check out this list of books and courses to follow.
Check out this repository of more than 100 freely-accessible NLP notebooks, curated from around the internet, and ready to launch in Colab with a single click.
Data related positions are considered the hottest in the job market during the last couple of years. While everyone wants to join the party and enter this fascinating field, it is essential to first get an understanding. In this quick guide, I’ll do my best to dispel the confusion by crystalizing the essence of the different positions.
If you are already applying your Data Science skills or getting ready to contribute to analyzing COVID-19 data, then be sure to take sufficient time to appreciate the context of the numbers to focus on what's most important as we collaborate on this global battle.
This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.
If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.
An open source low-code machine learning library in Python. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.
Apache Spark runs fast, offers robust, distributed, fault-tolerant data objects, and integrates beautifully with the world of machine learning and graph analytics. Learn more here.
OpenAI research shows a phenomenon that challenges both traditional statistical learning theory and conventional wisdom in machine learning practitioners.
Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.
This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills.
In this article, we’ll walk through the detailed and helpful continuous integration (CI) that supports us in keeping StellarGraph’s demos current and informative.
The Pandas library for Python is a game-changer for data preparation. But, when the data gets big, really big, then your computer needs more help to efficiency handle all that data. Learn more about how to use Dask and follow a demo to scale up your Pandas to work with Big Data.
Understanding the real business processes of a company through analysis of its information systems can guide digital transformations. Here, the top 10 process mining software companies are reviewed that can assist businesses in process optimizations through unique insights of business systems.
In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.
The goal of this essay is to discuss meaningful machine learning progress in the real-world application of drug discovery. There’s even a solid chance of the deep learning approach to drug discovery changing lives for the better doing meaningful good in the world.
Personal journeys in Data Science can vary greatly between individuals. Some are just getting starting and wading into this vast ocean of opportunity, and others have been involved during its decades-long evolution as a professional field. This review of a longer journey can provide a broader perspective of how you might fit into this interesting career.
When first learning data science, you will inevitably find yourself looking for more datasets to practice with. Here, we recommend the 3 best sites to find datasets to spark your next data science project.
torchlayers aims to do what Keras did for TensorFlow, providing a higher-level model-building API and some handy defaults and add-ons useful for crafting PyTorch neural networks.
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.
With your machine learning model in Python just working, it's time to optimize it for performance. Follow this guide to setup automated tuning using any optimization library in three steps.
In this piece, we’ll highlight some of the tips and tricks mentioned during this year’s TF summit. Specifically, these tips will help you in getting the best out of Google’s Colab.
Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?
Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.
How exactly are smart algorithms able to engage and communicate with us like humans? The answer lies in Question Answering systems that are built on a foundation of Machine Learning and Natural Language Processing. Let's build one here.
Are you interested in knowing more about epidemiology, the field which studies the spread and distribution of diseases? This article collects some free courses which are intended to help you do just that.
This post will address the issues that can arise when Pandas slicing is used improperly. If you see the warning that reads "A value is trying to be set on a copy of a slice from a DataFrame", this post is for you.
As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.
The article addresses a simple data analytics problem, comparing a Python and Pandas solution to an R solution (using plyr, dplyr, and data.table), as well as kdb+ and BigQuery solutions. Performance improvement tricks for these solutions are then covered, as are parallel/cluster computing approaches and their limitations.
Read this concise summary of KNN, a supervised and pattern classification learning algorithm which helps us find which class the new input belongs to when k nearest neighbours are chosen and distance is calculated between them.
From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.
Five Cool Python Libraries for Data Science
Coronavirus COVID-19 Genome Analysis using Biopython
Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs
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