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- Fighting Overfitting in Deep Learning
- Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
- 10 Best and Free Machine Learning Courses, Online
- Dec 26, 2019.
Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.
- Random Forest® vs Neural Networks for Predicting Customer Churn
- Dec 26, 2019.
Let us see how random forest competes with neural networks for solving a real world business problem.
- Market Basket Analysis: A Tutorial
- Dec 24, 2019.
This article is about Market Basket Analysis & the Apriori algorithm that works behind it.
- What is Data Catalog and Why You Should Care?
- Dec 23, 2019.
Learn why data catalogs could be just the thing you need to meet the challenges of data and metadata management and collaboration.
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Google’s New Explainable AI Service
- Dec 20, 2019.
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction. - The Most In Demand Tech Skills for Data Scientists
- Dec 20, 2019.
By the end of this article you’ll know which technologies are becoming more popular with employers and which are becoming less popular.
- Alternative Cloud Hosted Data Science Environments
- Dec 19, 2019.
Over the years new alternative providers have risen to provided a solitary data science environment hosted on the cloud for data scientist to analyze, host and share their work.
- Interpretability part 3: opening the black box with LIME and SHAP
- Dec 19, 2019.
The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.
- How to Convert an RGB Image to Grayscale
- Dec 18, 2019.
This post is about working with a mixture of color and grayscale images and needing to transform them into a uniform format - all grayscale. We'll be working in Python using the Pillow, Numpy, and Matplotlib packages.
- Pedestrian Detection Using Non Maximum Suppression Algorithm
- Dec 17, 2019.
Read this overview of a complete pipeline for detecting pedestrians on the road.
- Let’s Build an Intelligent Chatbot
- Dec 17, 2019.
Check out this step by step approach to building an intelligent chatbot in Python.
- The Ultimate Guide to Model Retraining
- Dec 16, 2019.
Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.
- Build Pipelines with Pandas Using pdpipe
- Dec 13, 2019.
We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
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Plotnine: Python Alternative to ggplot2
- Dec 12, 2019.
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python. - What just happened in the world of AI?
- Dec 12, 2019.
The speed at which AI made advancements and news during 2019 makes it imperative now to step back and place these events into order and perspective. It's important to separate the interest that any one advancement initially attracts, from its actual gravity and its consequential influence on the field. This review unfolds the parallel threads of these AI stories over this year and isolates their significance.
- Python Dictionary and Dictionary Methods
- Dec 12, 2019.
Check out this introduction to creating, accessing, and updating dictionaries in Python.
- Deploying a pretrained GPT-2 model on AWS
- Dec 12, 2019.
This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.
- Interpretability: Cracking open the black box, Part 2
- Dec 11, 2019.
The second part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers post-hoc interpretation that is useful when the model is not transparent.
- Intro to Grafana: Installation, Configuration, and Building the First Dashboard
- Dec 10, 2019.
One of the biggest highlights of Grafana is the ability to bring several data sources together in one dashboard with adding rows that will host individual panels. Let's look at installing, configuring, and creating our first dashboard using Grafana.
- 5 Great New Features in Latest Scikit-learn Release
- Dec 10, 2019.
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
- 10 Free Top Notch Machine Learning Courses
- Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- 5 Techniques to Prevent Overfitting in Neural Networks
- Dec 6, 2019.
In this article, I will present five techniques to prevent overfitting while training neural networks.
- Enabling the Deep Learning Revolution
- Dec 5, 2019.
Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.
- The Rise of User-Generated Data Labeling
- Dec 4, 2019.
Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!
- Popular Deep Learning Courses of 2019
- Dec 3, 2019.
With deep learning and AI on the forefront of the latest applications and demands for new business directions, additional education is paramount for current machine learning engineers and data scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills.
- Vega-Lite: A grammar of interactive graphics
- Dec 3, 2019.
Vega and Vega-lite follow in a long line of work that can trace its roots back to Wilkinson’s ‘The Grammar of Graphics.’ Since then VegaLite has come into existence, bringing high-level specification of interactive visualisations to the Vega-Lite world.
- Data Science Curriculum Roadmap
- Dec 3, 2019.
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.
COVID-19 Visualized: The power of effective visualizations for pandemic storytelling