- New Computing Paradigm for AI: Processing-in-Memory (PIM) Architecture, by Nam Sung Kim - Oct 15, 2021.
As larger deep neural networks are trained on the latest and fastest chip technologies, an important challenge remains that bottlenecks performance -- and it is not compute power. You can try to calculate a DNN as fast as possible, but there is data -- and it has to move. Data pipelines on the chip are expensive and new solutions must be developed to advance capabilities.
- How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method, by David B Rosen (PhD) - Oct 15, 2021.
Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?
- Deploying Your First Machine Learning API, by Abid Ali Awan - Oct 14, 2021.
Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- The 20 Python Packages You Need For Machine Learning and Data Science, by Sandro Luck - Oct 14, 2021.
Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
- What is Clustering and How Does it Work?, by Satoru Hayasaka - Oct 14, 2021.
Let us examine how clusters with different properties are produced by different clustering algorithms. In particular, we give an overview of three clustering methods: k-Means clustering, hierarchical clustering, and DBSCAN.
- How to Ace Data Science Interview by Working on Portfolio Projects, by Abid Ali Awan - Oct 13, 2021.
Recruiters of Data Science professionals around the world focus on portfolio projects rather than resumes and LinkedIn profiles. So, learning early how to contribute and share your work on GitHub, Deepnote, and Kaggle can help you perform your best during data science interviews.
- Building Multimodal Models: Using the widedeep Pytorch package, by Rajiv Shah - Oct 13, 2021.
This article gets you started on the open-source widedeep PyTorch framework developed by Javier Rodriguez Zaurin.
- Create Synthetic Time-series with Anomaly Signatures in Python, by Tirthajyoti Sarkar - Oct 12, 2021.
A simple and intuitive way to create synthetic (artificial) time-series data with customized anomalies — particularly suited to industrial applications.
- Step by Step Building a Vacancy Tracker Using Tableau, by Dotun Opasina - Oct 12, 2021.
Step-by-step explanations of vacancies valued in tens of millions of dollars in the small town of Fitchburg, Massachusetts.
- AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch, by Kevin Vu - Oct 11, 2021.
AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.
- Scaling human oversight of AI systems for difficult tasks – OpenAI approach, by OpenAI - Oct 11, 2021.
The foundational idea of Artificial Intelligence is that it should demonstrate human-level intelligence. So, unless a model can perform as a human might do, its intended purpose is missed. Here, recent OpenAI research into full-length book summarization focuses on generating results that make sense to humans with state-of-the-art results that leverage scalable AI-enhanced human-in-the-loop feedback.
- Query Your Pandas DataFrames with SQL, by Matthew Mayo - Oct 11, 2021.
Learn how to query your Pandas DataFrames using the standard SQL SELECT statement, seamlessly from within your Python code.
- 8 Must-Have Git Commands for Data Scientists, by Soner Yildirim - Oct 8, 2021.
Git is a must-have skill for data scientists. Maintaining your development work within a version control system is absolutely necessary to have a collaborative and productive working environment with your colleagues. This guide will quickly start you off in the right direction for contributing to an existing project at your organization.
- Dealing with Data Leakage, by Susan Currie Sivek, Ph.D. - Oct 8, 2021.
Target leakage and data leakage represent challenging problems in machine learning. Be prepared to recognize and avoid these potentially messy problems.
- The Evolution of Tokenization – Byte Pair Encoding in NLP, by Harshit Tyagi - Oct 7, 2021.
Though we have SOTA algorithms for tokenization, it's always a good practice to understand the evolution trail and learning how have we reached here. Read this introduction to Byte Pair Encoding.
- How to do “Limitless” Math in Python, by Tirthajyoti Sarkar - Oct 7, 2021.
How to perform arbitrary-precision computation and much more math (and fast too) than what is possible with the built-in math library in Python.
- Four Different Pipes for R with magrittr, by Gregory Janesch - Oct 6, 2021.
The magrittr package supplies the pipe operator (%>%), but it turns out that the package actually contains four pipe operators in total. Let's go into them a bit.
- 38 Free Courses on Coursera for Data Science, by Aqsa Zafar - Oct 6, 2021.
There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.
- My AI Plays Piano for Me, by Kathrin Melcher - Oct 6, 2021.
Training an RNN with a Combined Loss Function.
- Data science SQL interview questions from top tech firms, by Nate Rosidi - Oct 5, 2021.
As a data scientist, there is one thing you really need to understand and know how to handle: data. With SQL being a foundational technical approach for working with data, it should not be surprising that the top tech companies will ask about your SQL skills during an interview. Here, we cover the key concepts tested so you can best prepare for your next data science interview.
- The Architecture Behind DeepMind’s Model for Near Real Time Weather Forecasts, by Jesus Rodriguez - Oct 5, 2021.
Deep Generative Model of Rain (DGMR) is the newest creation from DeepMind which can predict precipitation in short term intervals.
- Parallelizing Python Code, by Borycki & Galarnyk - Oct 4, 2021.
This article reviews some common options for parallelizing Python code, including process-based parallelism, specialized libraries, ipython parallel, and Ray.
- How to Build Strong Data Science Portfolio as a Beginner, by Abid Ali Awan - Oct 4, 2021.
After learning the basics of data science, you can start to work on real-world problems. But how do you showcase your work? In this article, we are going to learn a unique way to create a data science portfolio.
- Introduction to PyTorch Lightning, by Kevin Vu - Oct 4, 2021.
PyTorch Lightning is a high-level programming layer built on top of PyTorch. It makes building and training models faster, easier, and more reliable.
- Teaching AI to Classify Time-series Patterns with Synthetic Data, by Tirthajyoti Sarkar - Oct 1, 2021.
How to build and train an AI model to identify various common anomaly patterns in time-series data.
- How to Auto-Detect the Date/Datetime Columns and Set Their Datatype When Reading a CSV File in Pandas, by David B Rosen (PhD) - Oct 1, 2021.
When read_csv( ) reads e.g. “2021-03-04” and “2021-03-04 21:37:01.123” as mere “object” datatypes, often you can simply auto-convert them all at once to true datetime datatypes.