Aug
2020 Sep Tutorials, Overviews
All (88) | Events (1) | News, Education (12) | Opinions (17) | Top Stories, Tweets (10) | Tutorials, Overviews (48)
- AI in Healthcare: A review of innovative startups
- Sep 30, 2020.
The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?
- Machine Learning Model Deployment
- Sep 30, 2020.
Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.
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The Best Free Data Science eBooks: 2020 Update
- Sep 30, 2020.
The author has updated their list of best free data science books for 2020. Read on to see what books you should grab. - Missing Value Imputation – A Review
- Sep 29, 2020.
Detecting and handling missing values in the correct way is important, as they can impact the results of the analysis, and there are algorithms that can’t handle them. So what is the correct way?
- International alternatives to Kaggle for Data Science / Machine Learning competitions
- Sep 29, 2020.
While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.
- How AI is Driving Innovation in Astronomy
- Sep 29, 2020.
In this blog, we look at a disruptive AI program - Morpheus - developed by Researchers at UC Santa Cruz that can analyze astronomical image data and classify galaxies and stars with surgical precision. If you're reading this with "starry" eyes, we bet we've got you hooked.
- Looking Inside The Blackbox: How To Trick A Neural Network
- Sep 28, 2020.
In this tutorial, I’ll show you how to use gradient ascent to figure out how to misclassify an input.
- Geographical Plots with Python
- Sep 28, 2020.
When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.
- The Online Courses You Must Take to be a Better Data Scientist
- Sep 28, 2020.
These select courses have proved to be precious online resources which helped make the author a better data scientist today.
- Making Python Programs Blazingly Fast
- Sep 25, 2020.
Let’s look at the performance of our Python programs and see how to make them up to 30% faster!
- Create and Deploy your First Flask App using Python and Heroku
- Sep 25, 2020.
Flask is a straightforward and lightweight web application framework for Python applications. This guide walks you through how to write an application using Flask with a deployment on Heroku.
- Causal Inference: The Free eBook
- Sep 25, 2020.
Here's another free eBook for those looking to up their skills. If you are seeking a resource that exhaustively treats the topic of causal inference, this book has you covered.
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Introduction to Time Series Analysis in Python
- Sep 24, 2020.
Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data. - The Most Complete Guide to PyTorch for Data Scientists
- Sep 24, 2020.
All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.
- LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale
- Sep 23, 2020.
The reference architecture is powering mission critical machine learning workflows within LinkedIn.
- How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy
- Sep 23, 2020.
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.
- Artificial Intelligence for Precision Medicine and Better Healthcare
- Sep 23, 2020.
In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.
- Machine Learning from Scratch: Free Online Textbook
- Sep 22, 2020.
If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.
- Statistical and Visual Exploratory Data Analysis with One Line of Code
- Sep 21, 2020.
If EDA is not executed correctly, it can cause us to start modeling with “unclean” data. See how to use Pandas Profiling to perform EDA with a single line of code.
- Automating Every Aspect of Your Python Project
- Sep 18, 2020.
Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
- What is Simpson’s Paradox and How to Automatically Detect it
- Sep 18, 2020.
Looking at data one way can tell one story, but sometimes looking at it another way will tell the opposite story. Understanding this paradox and why it happens is essential, and new tools are available to help automatically detect this tricky issue in your datasets.
- The Insiders’ Guide to Generative and Discriminative Machine Learning Models
- Sep 18, 2020.
In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.
- Implementing a Deep Learning Library from Scratch in Python
- Sep 17, 2020.
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
- Can Neural Networks Show Imagination? DeepMind Thinks They Can
- Sep 16, 2020.
DeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.
- Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities
- Sep 16, 2020.
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
- Autograd: The Best Machine Learning Library You’re Not Using?
- Sep 16, 2020.
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.
- DIY Election Fraud Analysis Using Benford’s Law
- Sep 15, 2020.
In this article, we will talk about a Do-It-Yourself approach towards election analysis and coming to a conclusion whether the elections were conducted fairly or not.
- Visualization Of COVID-19 New Cases Over Time In Python
- Sep 15, 2020.
Inspired by another concise data visualization, the author of this article has crafted and shared the code for a heatmap which visualizes the COVID-19 pandemic in the United States over time.
- Lessons From My First Kaggle Competition
- Sep 14, 2020.
How I chose my first Kaggle competition to enter and what I learned from doing it.
- Deep Learning’s Most Important Ideas
- Sep 14, 2020.
In the field of deep learning, there continues to be a deluge of research and new papers published daily. Many well-adopted ideas that have stood the test of time provide the foundation for much of this new work. To better understand modern deep learning, these techniques cover the basic necessary knowledge, especially as a starting point if you are new to the field.
- Statistics with Julia: The Free eBook
- Sep 14, 2020.
This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there.
- Understanding Bias-Variance Trade-Off in 3 Minutes
- Sep 11, 2020.
This article is the write-up of a Machine Learning Lighting Talk, intuitively explaining an important data science concept in 3 minutes.
- Feature Engineering for Numerical Data
- Sep 11, 2020.
Data feeds machine learning models, and the more the better, right? Well, sometimes numerical data isn't quite right for ingestion, so a variety of methods, detailed in this article, are available to transform raw numbers into something a bit more palatable.
- An Introduction to NLP and 5 Tips for Raising Your Game
- Sep 11, 2020.
This article is a collection of things the author would like to have known when they started out in NLP. Perhaps it will be useful for you.
- AI Papers to Read in 2020
- Sep 10, 2020.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
- Free From MIT: Intro to Computer Science and Programming in Python
- Sep 9, 2020.
This free introductory computer science and programming course is available via MIT's Open Courseware platform. It's a great resource for mastering the fundamentals of one of data science's major requirements.
- 4 Tools to Speed Up Your Data Science Writing
- Sep 9, 2020.
This article covers how you can achieve your writing goals with these 4 tools.
- 4 Tricks to Effectively Use JSON in Python
- Sep 8, 2020.
Working with JSON in Python is a breeze, this will get you started right away.
- Creating Powerful Animated Visualizations in Tableau
- Sep 7, 2020.
In this post we explore animated data visualization in Tableau,one of the tool's powerful features for making visualizations appealing and interactive.
- A Deep Learning Dream: Accuracy and Interpretability in a Single Model
- Sep 7, 2020.
IBM Research believes that you can improve the accuracy of interpretable models with knowledge learned in pre-trained models.
- Data Scientists think data is their #1 problem. Here’s why they’re wrong.
- Sep 4, 2020.
We tend to think it's all about the data. However, for real data science projects at real organizations in real life, there are more fundamental aspects to consider to do data science right.
- Design of Experiments in Data Science
- Sep 3, 2020.
Read this overview of the process of designing experiments for collecting data.
- How to Evaluate the Performance of Your Machine Learning Model
- Sep 3, 2020.
You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.
- 10 Things You Didn’t Know About Scikit-Learn
- Sep 3, 2020.
Check out these 10 things you didn’t know about Scikit-Learn... until now.
- Computer Vision Recipes: Best Practices and Examples
- Sep 2, 2020.
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
- Which methods should be used for solving linear regression?
- Sep 2, 2020.
As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.
- PyCaret 2.1 is here: What’s new?
- Sep 1, 2020.
PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive. Read about what's new in PyCaret 2.1.
- Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms
- Sep 1, 2020.
The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.
Learn Deep Learning with this Free Course from Yann LeCun
TabPy: Combining Python and Tableau