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Latest:
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Efficiency in Deep Learning, Part 1 - Jun 18, 2021.
Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer. -
Dashboards for Interpreting & Comparing Machine Learning Models - Jun 17, 2021.
This article discusses using Interpret to create dashboards for machine learning models. -
The Best Way to Learn Practical NLP? - Jun 16, 2021.
Hugging Face has just released a course on using its libraries and ecosystem for practical NLP, and it appears to be very comprehensive. Have a look for yourself. -
An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021.
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier. -
A Graph-based Text Similarity Method with Named Entity Information in NLP - Jun 16, 2021.
In this article, the author summarizes the 2017 paper "A Graph-based Text Similarity Measure That Employs Named Entity Information" as per their understanding. Better understand the concepts by reading along. -
7 Data Security Best Practices for 2021 - Jun 15, 2021.
Here are seven data security best practices to adopt this year. -
Beginners Guide to Debugging TensorFlow Models - Jun 15, 2021.
If you are new to working with a deep learning framework, such as TensorFlow, there are a variety of typical errors beginners face when building and training models. Here, we explore and solve some of the most common errors to help you develop a better intuition for debugging in TensorFlow. -
Facebook Launches One of the Toughest Reinforcement Learning Challenges in History - Jun 15, 2021.
The FAIR team just launched the NetHack Challenge as part of the upcoming NeurIPS 2021 competition. The objective is to test new RL ideas using a one of the toughest game environments in the world. -
Get Interactive Plots Directly With Pandas - Jun 14, 2021.
Telling a story with data is a core function for any Data Scientist, and creating data visualizations that are simultaneously illuminating and appealing can be challenging. This tutorial reviews how to create Plotly and Bokeh plots directly through Pandas plotting syntax, which will help you convert static visualizations into interactive counterparts -- and take your analysis to the next level. -
Building a Knowledge Graph for Job Search Using BERT - Jun 14, 2021.
A guide on how to create knowledge graphs using NER and Relation Extraction. -
Top 10 Data Science Projects for Beginners - Jun 11, 2021.
Check out these projects for ideas to strengthen your skills and build a portfolio that stands out. -
Five types of thinking for a high performing data scientist - Jun 11, 2021.
The way you think about a problem and the conceptual process you go through to find a solution may be guided by your personal skills or the type of problem at hand. Many mental models exist representing a variety of thinking patterns -- and as a Data Scientist, appreciating different approaches can help you more effectively model data in the business world and communicate your results to the decision-makers. -
9 Deadly Sins of Machine Learning Dataset Selection - Jun 11, 2021.
Avoid endless pain in model debugging by focusing on datasets upfront. -
The Essential Guide to Transformers, the Key to Modern SOTA AI - Jun 10, 2021.
You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another? -
Feature Selection – All You Ever Wanted To Know - Jun 10, 2021.
Although your data set may contain a lot of information about many different features, selecting only the "best" of these to be considered by a machine learning model can mean the difference between a model that performs well--with better performance, higher accuracy, and more computational efficiency--and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore. -
How to Generate Automated PDF Documents with Python - Jun 10, 2021.
Discover how to leverage automation to create dazzling PDF documents effortlessly. -
The 7 Best Open Source AI Libraries You May Not Have Heard Of - Jun 9, 2021.
AI researchers today have many exciting options for working with specialized tools. Although starting original projects from scratch is often not necessary, knowing which existing library to leverage remains a challenge. This list of generally unknown yet awesome, open-source libraries offers an interesting collection to consider for state-of-the-art research that spans from automatic machine learning to differentiable quantum circuits. -
This Data Visualization is the First Step for Effective Feature Selection - Jun 8, 2021.
Understanding the most important features to use is crucial for developing a model that performs well. Knowing which features to consider requires experimentation, and proper visualization of your data can help clarify your initial selections. The scatter pairplot is a great place to start. -
The only Jupyter Notebooks extension you truly need - Jun 8, 2021.
Now you don’t need to restart the kernel after editing the code in your custom imports. -
5 Data Science Open-source Projects You Should Consider Contributing to - Jun 7, 2021.
As you prepare to interview for a position in data science or are looking to jump to the next level, now is the time to enhance your skills and your resume with by working on rea, open-source projects. Here, we suggest a great selection of projects you can contribute to and help build something awesome, so, all you need to do choose one and tackle it head on. -
How to Fine-Tune BERT Transformer with spaCy 3 - Jun 7, 2021.
A step-by-step guide on how to create a knowledge graph using NER and Relation Extraction. -
PyCaret 101: An introduction for beginners - Jun 7, 2021.
This article is a great overview of how to get started with PyCaret for all your machine learning projects. -
5 Tasks To Automate With Python - Jun 4, 2021.
Here are 5 tasks you can automate with Python, and how to do it. -
Beyond Brainless AI with a Feature Store - Jun 4, 2021.
AI-powered products that are limited to the data available within its application are like jellyfish: its autonomic system makes it functional, but it lacks a brain. However, you can evolve your models with data enriched "brains" through the help of a feature store. -
10 Deadly Sins of Machine Learning Model Training - Jun 4, 2021.
These mistakes are easy to overlook but costly to redeem. -
How a Data Scientist Should Communicate with Stakeholders - Jun 3, 2021.
Effective and collaborative communication with stakeholders is a skill that can help you survive in your role as a Data Scientist at your organization. Learn how to master this interaction, and you will perform your job better, see improved outcomes from your projects, and grow in your capabilities and career. -
Machine Learning Model Interpretation - Jun 2, 2021.
Read this overview of using Skater to build machine learning visualizations. -
Stop (and Start) Hiring Data Scientists - Jun 2, 2021.
Large companies are losing many data scientists to smaller companies, so what should executives and managers do? These three “stop & start” tactics can improve talent retention, and help define a new way of recruiting and working for the Data Science field. -
How to Make Python Code Run Incredibly Fast - Jun 2, 2021.
In this article, I have explained some tips and tricks to optimize and speed up Python code. -
How to Create and Deploy a Simple Sentiment Analysis App via API - Jun 1, 2021.
In this article we will create a simple sentiment analysis app using the HuggingFace Transformers library, and deploy it using FastAPI.
May:
- Make Pandas 3 Times Faster with PyPolars
- Top 4 Data Extraction Tools
- Supercharge Your Machine Learning Experiments with PyCaret and Gradio
- Essential Math for Data Science: Basis and Change of Basis
- 4 Tips for Dataset Curation for NLP Projects
- Great New Resource for Natural Language Processing Research and Applications
- AI Books you should read in 2021
- Topic Modeling with Streamlit
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Top Programming Languages and Their Uses
, by Claire D. Costa The landscape of programming languages is rich and expanding, which can make it tricky to focus on just one or another for your career. We highlight some of the most popular languages that are modern, widely used, and come with loads of packages or libraries that will help you be more productive and efficient in your work. - Essential Machine Learning Algorithms: A Beginner’s Guide
- Write and train your own custom machine learning models using PyCaret
- How to Deal with Categorical Data for Machine Learning
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A Guide On How To Become A Data Scientist (Step By Step Approach)
, by Aditya Agarwal Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist. - Data Validation in Machine Learning is Imperative, Not Optional
- How to pitch to VCs, explained: The Deck We Used to Raise Capital For Our Open-Source ELT Platform
- Building RESTful APIs using Flask
- DataOps: 5 things that you need to know
- Awesome list of datasets in 100+ categories
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How to Determine if Your Machine Learning Model is Overtrained
, by Charles Martin WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems. - Differentiable Programming from Scratch
- Animated Bar Chart Races in Python
- The Most In Demand Skills for Data Engineers in 2021
- Easy MLOps with PyCaret + MLflow
- Machine Translation in a Nutshell
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Vaex: Pandas but 1000x faster
, by Ahmad Anis If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas.
- Binary Classification with Automated Machine Learning
- Best Python Books for Beginners and Advanced Programmers
- The next-generation of AutoML frameworks
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DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning
, by Jesus Rodriguez In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
- The NoSQL Know-It-All Compendium
- 6 side hustles for an aspiring data scientist
- The Explainable Boosting Machine
- Super Charge Python with Pandas on GPUs Using Saturn Cloud
- Machine Learning Pipeline Optimization with TPOT
- Confidence Intervals for XGBoost
- Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists
- Similarity Metrics in NLP
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Essential Linear Algebra for Data Science and Machine Learning
, by Benjamin Obi Tayo Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.
- Ensemble Methods Explained in Plain English: Bagging
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Applying Python’s Explode Function to Pandas DataFrames
, by Michael Mosesov Read this applied Python method to solve the issue of accessing column by date/ year using the Pandas library and functions lambda(), list(), map() & explode().
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Data Preparation in SQL, with Cheat Sheet!
, by Stan Pugsley If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
- A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know
- What is Neural Search?
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Rebuilding My 7 Python Projects
, by Kaustubh Gupta This is how I rebuilt My Python Projects: Data Science, Web Development & Android Apps.
- Deploy a Dockerized FastAPI App to Google Cloud Platform
- Disentangling AI, Machine Learning, and Deep Learning
- A simple static visualization can often be the best approach
- How To Generate Meaningful Sentences Using a T5 Transformer
- XGBoost Explained: DIY XGBoost Library in Less Than 200 Lines of Python
