2020 Oct Tutorials, Overviews
All (88) | Events (1) | News, Education (12) | Opinions (20) | Top Stories, Tweets (9) | Tutorials, Overviews (46)
- How to Make Sense of the Reinforcement Learning Agents? - Oct 30, 2020.
In this blog post, you’ll learn what to keep track of to inspect/debug your agent learning trajectory. I’ll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting and you’ve heard about at least some of the most common RL algorithms and environments.
-
Building Neural Networks with PyTorch in Google Colab - Oct 30, 2020.
Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that. - Dealing with Imbalanced Data in Machine Learning - Oct 29, 2020.
This article presents tools & techniques for handling data when it's imbalanced.
- Explaining the Explainable AI: A 2-Stage Approach - Oct 29, 2020.
Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. Even more so, explaining any type of understanding of AI models to humans is yet another challenging layer that must be addressed if we are to develop a complete approach to Explainable AI.
- An Introduction to AI, updated - Oct 28, 2020.
We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.
- Stop Running Jupyter Notebooks From Your Command Line - Oct 28, 2020.
Instead, run your Jupyter Notebook as a stand alone web app.
- Roadmap to Computer Vision - Oct 26, 2020.
Read this introduction to the main steps which compose a computer vision system, starting from how images are pre-processed, features extracted and predictions are made.
- Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak - Oct 23, 2020.
If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.
- DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.
- Behavior Analysis with Machine Learning and R: The free eBook - Oct 22, 2020.
Check out this new free ebook to learn how to leverage the power of machine learning to analyze behavioral patterns from sensor data and electronic records using R.
- Which flavor of BERT should you use for your QA task? - Oct 22, 2020.
Check out this guide to choosing and benchmarking BERT models for question answering.
- 10 Underrated Python Skills - Oct 21, 2020.
Tips for feature analysis, hyperparameter tuning, data visualization and more.
- Deploying Streamlit Apps Using Streamlit Sharing - Oct 20, 2020.
Read this sneak peek into Streamlit’s new deployment platform.
- Data Science in the Cloud with Dask - Oct 20, 2020.
Scaling large data analyses for data science and machine learning is growing in importance. Dask and Coiled are making it easy and fast for folks to do just that. Read on to find out how.
- Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
This article covers using scikit-learn to obtain the optimal number of features for your machine learning project.
- How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
- Roadmap to Natural Language Processing (NLP) - Oct 19, 2020.
Check out this introduction to some of the most common techniques and models used in Natural Language Processing (NLP).
- Optimizing the Levenshtein Distance for Measuring Text Similarity - Oct 16, 2020.
For speeding up the calculation of the Levenshtein distance, this tutorial works on calculating using a vector rather than a matrix, which saves a lot of time. We’ll be coding in Java for this implementation.
- Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.
- Fast Gradient Boosting with CatBoost - Oct 16, 2020.
In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.
-
fastcore: An Underrated Python Library - Oct 15, 2020.
A unique python library that extends the python programming language and provides utilities that enhance productivity. - How to ace the data science coding challenge - Oct 15, 2020.
Preparing to interview for a Data Scientist position takes preparation and practice, and then it could all boil down to a final review of your skills. Based on personal experience, these tips on how to approach such a review will help you excel in the coding challenge project for your next interview.
- Text Mining with R: The Free eBook - Oct 15, 2020.
This freely-available book will show you how to perform text analytics in R, using packages from the tidyverse.
- Free From MIT: Intro to Computational Thinking and Data Science - Oct 14, 2020.
This free course from MIT will help in your transition to thinking computationally, and ultimately solving complex data science problems.
- Goodhart’s Law for Data Science and what happens when a measure becomes a target? - Oct 14, 2020.
When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law.
- Getting Started with PyTorch - Oct 14, 2020.
A practical walkthrough on how to use PyTorch for data analysis and inference.
- The Future of Fake News - Oct 13, 2020.
Let's talk about misleading communications in the digital era.
- Software Engineering Tips and Best Practices for Data Science - Oct 13, 2020.
Bringing your work as a Data Scientist into the real-world means transforming your experiments, test, and detailed analysis into great code that can be deployed as efficient and effective software solutions. You must learn how to enable your machine learning algorithms to integrate with IT systems by taking them out of your notebooks and delivering them to the business by following software engineering standards.
- Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
The new release makes Ludwig one of the most complete open source AutoML stacks in the market.
- 5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
Our focus for this piece is to establish the best practices that make an ML project successful.
- How to be a 10x data scientist - Oct 12, 2020.
If you are a Data Scientist looking to make it to the next level, then there are many opportunities to up your game and your efficiency to stand out from the others. Some of these recommendations that you can follow are straightforward, and others are rarely followed, but they will all pay back in dividends of time and effectiveness for your career.
- Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases.
- Annotated Machine Learning Research Papers - Oct 9, 2020.
Check out this collection of annotated machine learning research papers, and no longer fear their reading.
- How I Levelled Up My Data Science Skills In 8 Months - Oct 9, 2020.
Read how the author used their time to level up a variety of their data science skills over a short period of time, and learn how you could do the same.
- Strategies of Docker Images Optimization - Oct 8, 2020.
Large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems.
- How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems - Oct 8, 2020.
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.
- Free Introductory Machine Learning Course From Amazon - Oct 7, 2020.
Amazon's Machine Learning University offers an introductory course titled Accelerated Machine Learning, which is a good starting place for those looking for a foundation in generalized practical ML.
- A step-by-step guide for creating an authentic data science portfolio project - Oct 7, 2020.
Especially if you are starting out launching yourself as a Data Scientist, you will want to first demonstrate your skills through interesting data science project ideas that you can implement and share. This step-by-step guide shows you how to do go through this process, with an original example that explores Germany’s biggest frequent flyer forum, Vielfliegertreff.
- 10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
- A Guide to Preparing OpenCV for Android - Oct 6, 2020.
This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
- Your Guide to Linear Regression Models - Oct 5, 2020.
This article explains linear regression and how to program linear regression models in Python.
- Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code - Oct 5, 2020.
Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.
- Getting Started in AI Research - Oct 5, 2020.
A guide on how to contribute to confirming the reproducibility of some of the most recent papers and join open-search research.
- Data Protection Techniques Needed to Guarantee Privacy - Oct 2, 2020.
This article takes a look at the concepts of data privacy and personal data. It presents several privacy protection techniques and explains how they contribute to preserving the privacy of individuals.
- 10 Days With “Deep Learning for Coders” - Oct 1, 2020.
Read about the author's experience with the course and the book from fast.ai.
- Understanding Transformers, the Data Science Way - Oct 1, 2020.
Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
Building Neural Networks with PyTorch in Google Colab
How to Explain Key Machine Learning Algorithms at an Interview
fastcore: An Underrated Python Library
Sep
