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The Big Bad NLP Database: Access Nearly 300 Datasets
- Feb 28, 2020.
Check out this database of nearly 300 freely-accessible NLP datasets, curated from around the internet. - Hands on Hyperparameter Tuning with Keras Tuner
- Feb 28, 2020.
Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.
- Decision Tree Intuition: From Concept to Application
- Feb 27, 2020.
While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.
- Introducing fastpages: An easy to use blogging platform with extra features for Jupyter Notebooks
- Feb 27, 2020.
This article introduces the easy to use blogging platform fastpages. fastpages relies on Github pages for hosting, and Github Actions to automate the creation of your blog, and contains extra features for Jupyter Notebooks.
- Data Science Curriculum for self-study
- Feb 26, 2020.
Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.
- Image Recognition and Object Detection in Retail
- Feb 26, 2020.
“According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI.”
- Python and R Courses for Data Science
- Feb 26, 2020.
Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
- Probability Distributions in Data Science
- Feb 26, 2020.
Some machine learning models are designed to work best under some distribution assumptions. Therefore, knowing with which distributions we are working with can help us to identify which models are best to use.
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Free Mathematics Courses for Data Science & Machine Learning
- Feb 25, 2020.
It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond. - Audio Data Analysis Using Deep Learning with Python (Part 2)
- Feb 25, 2020.
This is a followup to the first article in this series. Once you are comfortable with the concepts explained in that article, you can come back and continue with this.
- Microsoft Open Sources ZeRO and DeepSpeed: The Technologies Behind the Biggest Language Model in History
- Feb 24, 2020.
The two efforts enable the training of deep learning models at massive scale.
- Passive Data Collection and Actionable Results: What to Know
- Feb 21, 2020.
There are plenty of ways to get actionable results by using passive data. However, such an outcome will not happen without careful forethought. Data analysts must consider several crucial specifics, including what questions they want and expect the information to answer, and how they'll apply the findings to aid the business.
- How Kubeflow Can Add AI to Your Kubernetes Deployments
- Feb 21, 2020.
As Kubernetes is capable of working with other solutions, it is possible to integrate it with a collection of tools that can almost fully automate your development pipeline. Some of those third-party tools even allow you to integrate AI into Kubernetes. One such tool you can integrate with Kubernetes is Kubeflow. Read more about it here.
- The Forgotten Algorithm
- Feb 20, 2020.
This article explores Monte Carlo Simulation with Streamlit.
- Getting Started with R Programming
- Feb 19, 2020.
An end to end Data Analysis using R, the second most requested programming language in Data Science.
- Audio Data Analysis Using Deep Learning with Python (Part 1)
- Feb 19, 2020.
A brief introduction to audio data processing and genre classification using Neural Networks and python.
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)
- Feb 18, 2020.
2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.
- Using the Fitbit Web API with Python
- Feb 18, 2020.
Fitbit provides a Web API for accessing data from Fitbit activity trackers. Check out this updated tutorial to accessing this Fitbit data using the API with Python.
- Using AI to Identify Wildlife in Camera Trap Images from the Serengeti
- Feb 17, 2020.
With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.
- Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything
- Feb 17, 2020.
Meena is one of the major milestones in the history of NLU. How did Google build it?
- Fourier Transformation for a Data Scientist
- Feb 14, 2020.
The article contains a brief intro into Fourier transformation mathematically and its applications in AI.
- Introduction to Geographical Time Series Prediction with Crime Data in R, SQL, and Tableau
- Feb 14, 2020.
When reviewing geographical data, it can be difficult to prepare the data for an analysis. This article helps by covering importing data into a SQL Server database; cleansing and grouping data into a map grid; adding time data points to the set of grid data and filling in the gaps where no crimes occurred; importing the data into R; running XGBoost model to determine where crimes will occur on a specific day
- Adversarial Validation Overview
- Feb 13, 2020.
Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.
- Practical Hyperparameter Optimization
- Feb 13, 2020.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
- Easy Image Dataset Augmentation with TensorFlow
- Feb 13, 2020.
What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.
- Sharing your machine learning models through a common API
- Feb 12, 2020.
DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.
- Illustrating the Reformer
- Feb 12, 2020.
In this post, we will try to dive into the Reformer model and try to understand it with some visual guides.
- How to learn data science on your own: a practical guide
- Feb 11, 2020.
While much focus today is on the rise in working from home and the challenges experienced, not as much is said about learning from home. For those lone wolfs studying Data Science in a self-directed way, a range of issues can get in the way of your goal. Learn about these common problems to prepare to focus yourself all the way to your educational goals.
- Basics of Audio File Processing in R
- Feb 11, 2020.
This post provides basic information on audio processing using R as the programming language. It also walks through and understands some basics of sound and digital audio.
- Recommender System Metrics: Comparing Apples, Oranges and Bananas
- Feb 11, 2020.
This article will discuss a sometimes-overlooked aspect of what distinguishes recommender systems from other machine learning tasks: added uncertainties of measuring them.
- Observability for Data Engineering
- Feb 10, 2020.
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
- Intent Recognition with BERT using Keras and TensorFlow 2
- Feb 10, 2020.
TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!
- Amazon Uses Self-Learning to Teach Alexa to Correct its Own Mistakes
- Feb 10, 2020.
The digital assistant incorporates a reformulation engine that can learn to correct responses in real time based on customer interactions.
- Large Scale Adversarial Representation Learning
- Feb 7, 2020.
GANs can be used for unsupervised learning where a generator maps latent samples to generate data, but this framework does not include an inverse mapping from data to latent representation. BiGAN adds an encoder E to the standard generator-discriminator GAN architecture — the encoder takes input data x and outputs a latent representation z of the input.
- Understanding Density-based Clustering
- Feb 6, 2020.
HDBSCAN is a robust clustering algorithm that is very useful for data exploration, and this comprehensive introduction provides an overview of its fundamental ideas from a high-level view above the trees to down in the weeds.
- Getting up and Running with Python: Installing Anaconda on Windows
- Feb 6, 2020.
This tutorial covers how to download and install Anaconda on Windows; how to test your installation; how to fix common installation issues; and what to do after installing Anaconda.
- Intro to Machine Learning and AI based on high school knowledge
- Feb 5, 2020.
Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.
- Create Your Own Computer Vision Sandbox
- Feb 5, 2020.
This post covers a wide array of computer vision tasks, from automated data collection to CNN model building.
- Optimal Estimation Algorithms: Kalman and Particle Filters
- Feb 5, 2020.
An introduction to the Kalman and Particle Filters and their applications in fields such as Robotics and Reinforcement Learning.
- Audio File Processing: ECG Audio Using Python
- Feb 4, 2020.
In this post, we will look into an application of audio file processing, for a good cause — Analysis of ECG Heart beat and write code in python.
- Serverless Machine Learning with R on Cloud Run
- Feb 4, 2020.
Expedite the deployment of your machine models using serverless cloud infrastructure. In this tutorial, we explore creating and deploying a model which scraps real time Twitter data and returns interactive visualization using R.
- Microsoft Open Sources Jericho to Train Reinforcement Learning Using Linguistic Games
- Feb 3, 2020.
The new framework provides an OpenAI-like environment for language-based games.
- 12-Hour Machine Learning Challenge: Build & deploy an app with Streamlit and DevOps tools
- Feb 3, 2020.
This article will present the knowledge, process, tools, and frameworks required for completing a 12-hour ML challenge. I hope you can find it useful for your personal or professional projects.
