Search results for deep learning

Found 5344 documents, 14038 searched:

• KDnuggets™ News 21:n31, Aug 18: The Difference Between Data Scientists and ML Engineers; MLOPs And Machine Learning RoadMap

  What is the difference between Data Scientists and ML Engineers? How does MLOPs fit into Machine Learning RoadMap? How to Train a BERT Model From Scratch? What is so great about Intro to Statistical Learning, 2nd Edition? Find the answers to these questions and more in this issue.

  https://www.kdnuggets.com/2021/n31.html

• Model Drift in Machine Learning – How To Handle It In Big Data

  Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?

  https://www.kdnuggets.com/2021/08/model-drift-machine-learning-big-data.html

• Introduction to Statistical Learning Second Edition

  The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.

  https://www.kdnuggets.com/2021/08/introduction-statistical-learning-v2.html

• DeepMind’s New Super Model: Perceiver IO is a Transformer that can Handle Any Dataset

  The new transformer-based architecture can process audio, video and images using a single model.

  https://www.kdnuggets.com/2021/08/deepmind-new-super-model-perceiver-io-transformer.html

• How DeepMind Trains Agents to Play Any Game Without Intervention

  A new paper proposes a new architecture and training environment for generally capable agents.

  https://www.kdnuggets.com/2021/08/deepmind-trains-agents-play-without-intervention.html

• 10 Machine Learning Model Training Mistakes

  These common ML model training mistakes are easy to overlook but costly to redeem.

  https://www.kdnuggets.com/2021/07/10-machine-learning-model-training-mistakes.html

• Building Machine Learning Pipelines using Snowflake and Dask

  In this post, I want to share some of the tools that I have been exploring recently and show you how I use them and how they helped improve the efficiency of my workflow. The two I will talk about in particular are Snowflake and Dask. Two very different tools but ones that complement each other well especially as part of the ML Lifecycle.

  https://www.kdnuggets.com/2021/07/building-machine-learning-pipelines-snowflake-dask.html

• KDnuggets™ News 21:n28, Jul 28: Design patterns in machine learning; The Best NLP Course is Free

  What are the Design patterns for Machine Learning and why you should know them? For more advanced readers, how to use Kafka Connect to create an open source data pipeline for processing real-time data; The state-of-the-art NLP course is freely available; Python Data Structures Compared; Update your Machine Learning skills this summer.

  https://www.kdnuggets.com/2021/n28.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• Design patterns in machine learning">Design patterns in machine learning

  Can we abstract best practices to real design patterns yet?

  https://www.kdnuggets.com/2021/07/design-patterns-machine-learning.html

• How Much Memory is your Machine Learning Code Consuming?

  Learn how to quickly check the memory footprint of your machine learning function/module with one line of command. Generate a nice report too.

  https://www.kdnuggets.com/2021/07/memory-machine-learning-code-consuming.html

• How to Create Unbiased Machine Learning Models

  In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.

  https://www.kdnuggets.com/2021/07/create-unbiased-machine-learning-models.html

• Pushing No-Code Machine Learning to the Edge

  Discover the power of no-code machine learning, and what it can accomplish when pushed to edge devices.

  https://www.kdnuggets.com/2021/07/pushing-no-code-machine-learning-edge.html

• A Learning Path To Becoming a Data Scientist">A Learning Path To Becoming a Data Scientist

  Becoming a professional data scientist may not be as easy as "1... 2... 3...", but these 10 steps can be your self-learning roadmap to kickstarting your future in the exciting and ever-expanding field of data science.

  https://www.kdnuggets.com/2021/07/learning-path-data-scientist.html

• Learning Data Science Through Social Media

  Want your social media algorithms to show you actual algorithms? Spare a moment during your social media scrolling to learn a bit of data science. Here are suggestions for at-a-glance access to good ideas and tips on your favorite platforms.

  https://www.kdnuggets.com/2021/07/learning-data-science-through-social-media.html

• Amazing Low-Code Machine Learning Capabilities with New Ludwig Update

  Integration with Ray, MLflow and TabNet are among the top features of this release.

  https://www.kdnuggets.com/2021/06/ludwig-update-includes-low-code-machine-learning-capabilities.html

• Dashboards for Interpreting & Comparing Machine Learning Models

  This article discusses using Interpret to create dashboards for machine learning models.

  https://www.kdnuggets.com/2021/06/dashboards-interpreting-comparing-machine-learning-models.html

• Facebook Launches One of the Toughest Reinforcement Learning Challenges in History

  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.

  https://www.kdnuggets.com/2021/06/facebook-launches-toughest-reinforcement-learning-challenges.html

• 9 Deadly Sins of Machine Learning Dataset Selection

  Avoid endless pain in model debugging by focusing on datasets upfront.

  https://www.kdnuggets.com/2021/06/9-deadly-sins-ml-dataset-selection.html

• KDnuggets™ News 21:n21, Jun 9: 5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning

  5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning; Will There Be a Shortage of Data Science Jobs in the Next 5 Years?; How to Make Python Code Run Incredibly Fast; Stop (and Start) Hiring Data Scientists

  https://www.kdnuggets.com/2021/n21.html

• Top Stories, May 31 – Jun 6: A Guide On How To Become A Data Scientist (Step By Step Approach); How I Doubled My Income with Data Science and Machine Learning

  Also: 5 Tasks To Automate With Python; How I Doubled My Income with Data Science and Machine Learning; Will There Be a Shortage of Data Science Jobs in the Next 5 Years?; How to Make Python Code Run Incredibly Fast

  https://www.kdnuggets.com/2021/06/top-news-week-0531-0606.html

• 10 Deadly Sins of Machine Learning Model Training

  These mistakes are easy to overlook but costly to redeem.

  https://www.kdnuggets.com/2021/06/10-deadly-sins-machine-learning-model-training.html

• Machine Learning Model Interpretation

  Read this overview of using Skater to build machine learning visualizations.

  https://www.kdnuggets.com/2021/06/machine-learning-model-interpretation.html

• Data Validation in Machine Learning is Imperative, Not Optional

  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  https://www.kdnuggets.com/2021/05/data-validation-machine-learning-imperative.html

• How to Determine if Your Machine Learning Model is Overtrained">How to Determine if Your Machine Learning Model is Overtrained

  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.

  https://www.kdnuggets.com/2021/05/how-determine-machine-learning-model-overtrained.html

• Top Stories, May 10-16: Essential Linear Algebra for Data Science and Machine Learning

  Also: Data Preparation in SQL, with Cheat Sheet!; Best Python Books for Beginners and Advanced Programmers; Similarity Metrics in NLP; The NoSQL Know-It-All Compendium

  https://www.kdnuggets.com/2021/05/top-news-week-0510-0516.html

• Binary Classification with Automated Machine Learning

  Check out how to use the open-source MLJAR auto-ML to build accurate models faster.

  https://www.kdnuggets.com/2021/05/binary-classification-automated-machine-learning.html

• What makes a winning entry in a Machine Learning competition?

  So you want to show your grit in a Kaggle-style competition? Many, many others have the same idea, including domain experts and non-experts, and academic and corporate teams. What does it take for your bright ideas and skills to come out on top of thousands of competitors?

  https://www.kdnuggets.com/2021/05/winning-machine-learning-competition.html

• The Machine Learning Research Championed by the Biggest AI Labs in the World

  How Google, Microsoft, Facebook, DeepMind, OpenAI, Amazon and IBM think about the future of AI.

  https://www.kdnuggets.com/2021/05/machine-learning-research-biggest-ai-labs.html

• Top Stories, Apr 26 – May 2: Data Scientist vs Machine Learning Engineer – what are their skills?

  Also: Data Science Books You Should Start Reading in 2021; Data science is not about data – applying Dijkstra principle to data science; How to ace A/B Testing Data Science Interviews; Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1

  https://www.kdnuggets.com/2021/05/top-news-week-0426-0502.html

• Best Podcasts for Machine Learning

  Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.

  https://www.kdnuggets.com/2021/04/best-podcasts-machine-learning.html

• Data Scientist vs Machine Learning Engineer – what are their skills?">Data Scientist vs Machine Learning Engineer – what are their skills?

  As two very popular tech roles for 2021, the Data Scientist and Machine Learning Engineer can overlap or be entirely distinct, depending on the organization you work for. However, general differences between these positions require certain skill sets that you must be prepared for when applying for jobs.

  https://www.kdnuggets.com/2021/04/data-scientist-machine-learning-engineer-skills.html

• How Uber manages Machine Learning Experiments with Comet.ml

  At Uber, where ML is fundamental to most products, a mechanism to manage offline experiments easily is needed to improve developer velocity. To solve for this, Uber AI was looking for a solution that will potentially complement and extend its in-house experiment management and collaboration capabilities.

  https://www.kdnuggets.com/2021/04/comet-uber-machine-learning-experiments.html

• Free From Stanford: Machine Learning with Graphs

  Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher teaches their topic of expertise. Accessible materials include slides, videos, and more.

  https://www.kdnuggets.com/2021/04/free-stanford-machine-learning-graphs.html

• 6 Mistakes To Avoid While Training Your Machine Learning Model

  While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.

  https://www.kdnuggets.com/2021/04/cogitotech-6-mistakes-avoid-training-machine-learning.html

• KDnuggets™ News 21:n14, Apr 14: A/B Testing: Common Questions and Answers in Data Science Interviews; Interpretable Machine Learning: The Free eBook

  Common Questions and Answers on A/B testing in Data Science Interviews; Interpretable Machine Learning: The Free eBook; Why machine learning struggles with causality; Deep Learning Recommendation Models: A Deep Dive; and more.

  https://www.kdnuggets.com/2021/n14.html

• 10 Real-Life Applications of Reinforcement Learning

  In this article, we’ll look at some of the real-world applications of reinforcement learning.

  https://www.kdnuggets.com/2021/04/10-real-life-applications-reinforcement-learning.html

• Deepfakes are now mainstream. What’s next?

  Deepfakes have become mainstream. Here we take a closer look at recent news about deepfakes, and what it all might mean for the future.

  https://www.kdnuggets.com/2021/04/deepfakes-mainstream-next.html

• Why machine learning struggles with causality

  If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.

  https://www.kdnuggets.com/2021/04/machine-learning-struggles-causality.html

• KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable

  Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1

  https://www.kdnuggets.com/2021/n13.html

• Top Stories, Mar 29 – Apr 4: Top 10 Python Libraries Data Scientists should know in 2021; Shapash: Making Machine Learning Models Understandable

  Also: The 8 Most Common Data Scientists; Easy AutoML in Python; How to Succeed in Becoming a Freelance Data Scientist; The 8 Most Common Data Scientists

  https://www.kdnuggets.com/2021/04/top-news-week-0329-0404.html

• The Best Machine Learning Frameworks & Extensions for TensorFlow

  Check out this curated list of useful frameworks and extensions for TensorFlow.

  https://www.kdnuggets.com/2021/04/best-machine-learning-frameworks-extensions-tensorflow.html

• Shapash: Making Machine Learning Models Understandable">Shapash: Making Machine Learning Models Understandable

  Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.

  https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html

• Top YouTube Machine Learning Channels

  These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  https://www.kdnuggets.com/2021/03/top-youtube-machine-learning-channels.html

• The Best Machine Learning Frameworks & Extensions for Scikit-learn">The Best Machine Learning Frameworks & Extensions for Scikit-learn

  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  https://www.kdnuggets.com/2021/03/best-machine-learning-frameworks-extensions-scikit-learn.html

• Learning from machine learning mistakes

  Read this article and discover how to find weak spots of a regression model.

  https://www.kdnuggets.com/2021/03/learning-from-machine-learning-mistakes.html

• Automating Machine Learning Model Optimization

  This articles presents an overview of using Bayesian Tuning and Bandits for machine learning.

  https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html

• 10 Amazing Machine Learning Projects of 2020">10 Amazing Machine Learning Projects of 2020

  So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.

  https://www.kdnuggets.com/2021/03/10-amazing-machine-learning-projects-2020.html

• DeepMind’s AlphaFold & the Protein Folding Problem

  Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.

  https://www.kdnuggets.com/2021/03/deepmind-alphafold-protein-folding-problem.html

• Getting Started with Distributed Machine Learning with PyTorch and Ray

  Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.

  https://www.kdnuggets.com/2021/03/getting-started-distributed-machine-learning-pytorch-ray.html

• Top Stories, Feb 22-28: We Don’t Need Data Scientists, We Need Data Engineers; Data Science Learning Roadmap for 2021

  Also: Powerful Exploratory Data Analysis in just two lines of code; Machine Learning Systems Design: A Free Stanford Course; Telling a Great Data Story: A Visualization Decision Tree

  https://www.kdnuggets.com/2021/03/top-news-week-0222-0228.html

• Data Science Learning Roadmap for 2021">Data Science Learning Roadmap for 2021

  Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.

  https://www.kdnuggets.com/2021/02/data-science-learning-roadmap-2021.html

• Machine Learning Systems Design: A Free Stanford Course">Machine Learning Systems Design: A Free Stanford Course

  This freely-available course from Stanford should give you a toolkit for designing machine learning systems.

  https://www.kdnuggets.com/2021/02/machine-learning-systems-design-free-stanford-course.html

• Why Do Machine Learning Projects Fail?

  At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.

  https://www.kdnuggets.com/2021/02/why-machine-learning-projects-fail.html

• Approaching (Almost) Any Machine Learning Problem">Approaching (Almost) Any Machine Learning Problem

  This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.

  https://www.kdnuggets.com/2021/02/approaching-almost-any-machine-learning-problem.html

• IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks

  Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.

  https://www.kdnuggets.com/2021/02/ibm-continual-learning-avoid-amnesia-problem-neural-networks.html

• Machine Learning – it’s all about assumptions

  Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.

  https://www.kdnuggets.com/2021/02/machine-learning-assumptions.html

• A Critical Comparison of Machine Learning Platforms in an Evolving Market

  There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.

  https://www.kdnuggets.com/2021/02/critical-comparison-machine-learning-platforms-evolving-market.html

• My machine learning model does not learn. What should I do?

  This article presents 7 hints on how to get out of the quicksand.

  https://www.kdnuggets.com/2021/02/machine-learning-model-not-learn.html

• Microsoft Explores Three Key Mysteries of Ensemble Learning

  A new paper studies three key puzzling characteristics of deep learning ensembles and some potential explanations.

  https://www.kdnuggets.com/2021/02/microsoft-explores-three-key-mysteries-ensemble-learning.html

• 2011: DanNet triggers deep CNN revolution

  In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.

  https://www.kdnuggets.com/2021/02/dannet-triggers-deep-cnn-revolution.html

• Past 2021 Meetings / Online Events on AI, Analytics, Big Data, Data Science, and Machine Learning

  Past | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec Read more »

  https://www.kdnuggets.com/meetings/past-meetings-2021.html

• KDnuggets™ News 21:n05, Feb 3: How to Get a Job as a Data Scientist; Popular Machine Learning Interview Questions, part 2

  Learn how to get a job as Data Scientist; it will help if you study popular machine learning interview questions; Beyond the Nash Equilibrium: DeepMind Clever Strategy to Solve Asymmetric Games; Understanding Bayes Theorem; and more.

  https://www.kdnuggets.com/2021/n05.html

• Top Stories, Jan 25-31: Want to Be a Data Scientist? Don’t Start With Machine Learning; The Ultimate Scikit-Learn Machine Learning Cheatsheet

  Also: How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off; How to Get a Job as a Data Scientist; Data Engineering — the Cousin of Data Science, is Troublesome; What to Learn to Become a Data Scientist in 2021

  https://www.kdnuggets.com/2021/02/top-news-week-0125-0131.html

• Beyond the Nash Equilibrium: DeepMind Clever Strategy to Solve Asymmetric Games

  The method expands the concept of a Nash equilibrium by decomposing an asymmetric game into multiple symmetric games.

  https://www.kdnuggets.com/2021/02/beyond-nash-equilibrium-deepmind-solve-asymmetric-games.html

• Machine learning adversarial attacks are a ticking time bomb

  Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.

  https://www.kdnuggets.com/2021/01/machine-learning-adversarial-attacks.html

• Top 5 Reasons Why Machine Learning Projects Fail

  The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.

  https://www.kdnuggets.com/2021/01/top-5-reasons-why-machine-learning-projects-fail.html

• Machine learning is going real-time

  Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?

  https://www.kdnuggets.com/2021/01/machine-learning-real-time.html

• Popular Machine Learning Interview Questions, part 2

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions-part2.html

• Want to Be a Data Scientist? Don’t Start With Machine Learning">Want to Be a Data Scientist? Don’t Start With Machine Learning

  Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.

  https://www.kdnuggets.com/2021/01/data-scientist-dont-start-machine-learning.html

• Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning

  Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?

  https://www.kdnuggets.com/2021/01/github-career-growth-ai-machine-learning.html

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions.html

• Graph Representation Learning: The Free eBook

  This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models.

  https://www.kdnuggets.com/2021/01/graph-representation-learning-book-free-ebook.html

• Top Stories, Jan 11-17: K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; My Data Science Learning Journey So Far

  Also: Essential Math for Data Science: Information Theory; Cleaner Data Analysis with Pandas Using Pipes; The Four Jobs of the Data Scientist

  https://www.kdnuggets.com/2021/01/top-news-week-0111-0117.html

• Unsupervised Learning for Predictive Maintenance using Auto-Encoders

  This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; ​​​​Approaches for machine diagnosis; Machine diagnosis using machine learning

  https://www.kdnuggets.com/2021/01/unsupervised-learning-predictive-maintenance-auto-encoders.html

• My Data Science Learning Journey So Far">My Data Science Learning Journey So Far

  These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.

  https://www.kdnuggets.com/2021/01/data-science-learning-journey.html

• KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners

  Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers

  https://www.kdnuggets.com/2021/n02.html

• Top Stories, Jan 04-10: Best Python IDEs and Code Editors You Should Know; All Machine Learning Algorithms You Should Know in 2021

  Also: DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created; 10 Underappreciated Python Packages for Machine Learning Practitioners; Six Tips on Building a Data Science Team at a Small Company

  https://www.kdnuggets.com/2021/01/top-news-week-0104-0110.html

• 10 Underappreciated Python Packages for Machine Learning Practitioners">10 Underappreciated Python Packages for Machine Learning Practitioners

  Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

  https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html

• CatalyzeX: A must-have browser extension for machine learning engineers and researchers

  CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).

  https://www.kdnuggets.com/2021/01/catalyzex-browser-extension-machine-learning.html

• KDnuggets™ News 21:n01, Jan 6: All machine learning algorithms you should know in 2021; Monte Carlo integration in Python; MuZero – the most important ML system ever created?

  The first issue in 2021 brings you a great blog about Monte Carlo Integration - in Python; An overview of main Machine Learning algorithms you need to know in 2021; SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations - how to; MuZero - may be the most important Machine Learning system ever created; and much more!

  https://www.kdnuggets.com/2021/n01.html

• 15 Free Data Science, Machine Learning & Statistics eBooks for 2021">15 Free Data Science, Machine Learning & Statistics eBooks for 2021

  We present a curated list of 15 free eBooks compiled in a single location to close out the year.

  https://www.kdnuggets.com/2020/12/15-free-data-science-machine-learning-statistics-ebooks-2021.html

• Top Stories, Dec 14-20: Crack SQL Interviews; State of Data Science and Machine Learning 2020: 3 Key Findings

  Also: A Rising Library Beating Pandas in Performance; 20 Core Data Science Concepts for Beginners; How to Create Custom Real-time Plots in Deep Learning; 10 Python Skills They Don’t Teach in Bootcamp

  https://www.kdnuggets.com/2020/12/top-news-week-1214-1220.html

• 5 strategies for enterprise machine learning for 2021

  While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.

  https://www.kdnuggets.com/2020/12/5-strategies-enterprise-machine-learning-2021.html

• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance

  A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.

  https://www.kdnuggets.com/2020/12/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance.html

• Top 2020 Stories: 24 Best (and Free) Books To Understand Machine Learning; If I had to start learning Data Science again, how would I do it?

  Also: Know What Employers are Expecting for a Data Scientist Role in 2020; Top Python Libraries for Data Science, Data Visualization & Machine Learning.

  https://www.kdnuggets.com/2020/12/top-stories-2020.html

• Top KDnuggets tweets, Dec 09-15: Main 2020 Developments, Key 2021 Trends in #AI #DataScience #MachineLearning DL Technology from experts

  Also: Data Science and Machine Learning: The Free eBook; CatBoost vs. Light GBM vs. XGBoost; 10 Python Skills They Don’t Teach in Bootcamp; MIT @techreview read the paper that forced @TimnitGebru out of Google. It presents the history of #NLP and an overview of four main #risks of large language models - here are the details

  https://www.kdnuggets.com/2020/12/top-tweets-dec09-15.html

• Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning

  We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?

  https://www.kdnuggets.com/2020/12/industry-2021-predictions-ai-data-science-machine-learning.html

• Data Science and Machine Learning: The Free eBook

  Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.

  https://www.kdnuggets.com/2020/12/data-science-machine-learning-free-ebook.html

• Facebook Open Sources ReBeL, a New Reinforcement Learning Agent

  The new model tries to recreate the reinforcement learning and search methods used by AlphaZero in imperfect information scenarios.

  https://www.kdnuggets.com/2020/12/facebook-open-sources-rebel-new-reinforcement-learning-agent.html

• A Journey from Software to Machine Learning Engineer

  In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.

  https://www.kdnuggets.com/2020/12/journey-from-software-machine-learning-engineer.html

• Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology">Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology

  Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  https://www.kdnuggets.com/2020/12/developments-trends-ai-data-science-machine-learning-technology.html

• Top November Stories: Top Python Libraries for Data Science, Data Visualization & Machine Learning; The Best Data Science Certification You’ve Never Heard Of

  Also: TabPy: Combining Python and Tableau; How to Acquire the Most Wanted Data Science Skills.

  https://www.kdnuggets.com/2020/12/top-stories-2020-nov.html

• Pruning Machine Learning Models in TensorFlow

  Read this overview to learn how to make your models smaller via pruning.

  https://www.kdnuggets.com/2020/12/pruning-machine-learning-models-tensorflow.html

• Simple & Intuitive Ensemble Learning in R

  Read about metaEnsembleR, an R package for heterogeneous ensemble meta-learning (classification and regression) that is fully-automated.

  https://www.kdnuggets.com/2020/12/simple-intuitive-meta-learning-r.html

• Roadmaps to becoming a Full-Stack AI Developer, Data Scientist, Machine Learning Engineer, and more

  As the fields related to AI and Data Science expand, they are becoming complex with more options and specializations to consider. If you are beginning your journey toward becoming an expert in Artificial Intelligence, this roadmap will guide you to find your path along what to learn next while steering clear of the latest hype.

  https://www.kdnuggets.com/2020/12/roadmaps-ai-developer-data-scientist-machine-learning-engineer.html

• Top Stories, Nov 23-29: TabPy: Combining Python and Tableau; The Rise of the Machine Learning Engineer

  Also: Learn Deep Learning with this Free Course from Yann LeCun; Know-How to Learn Machine Learning Algorithms Effectively; 15 Exciting AI Project Ideas for Beginners; How to Get Into Data Science Without a Degree

  https://www.kdnuggets.com/2020/11/top-news-week-1123-1129.html

• Is Your Machine Learning Model Likely to Fail?

  Read about these 5 missteps to avoid in your planning process.

  https://www.kdnuggets.com/2020/11/machine-learning-model-fail.html

• How to Know if a Neural Network is Right for Your Machine Learning Initiative

  It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.

  https://www.kdnuggets.com/2020/11/neural-network-right-machine-learning-initiative.html

• Know-How to Learn Machine Learning Algorithms Effectively

  The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.

  https://www.kdnuggets.com/2020/11/learn-machine-learning-algorithms-effectively.html

• 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use

  If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  https://www.kdnuggets.com/2020/11/5-useful-machine-learning-tools.html

• When Machine Learning Knows Too Much About You

  If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.

  https://www.kdnuggets.com/2020/11/machine-learning-knows-too-much-about-you.html

• My Data Science Online Learning Journey on Coursera

  Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.

  https://www.kdnuggets.com/2020/11/data-science-online-learning-journey-coursera.html

• Doing the impossible? Machine learning with less than one example

  Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.

  https://www.kdnuggets.com/2020/11/machine-learning-less-than-one-example.html

• Top Stories, Nov 2-8: Top Python Libraries for Data Science, Data Visualization & Machine Learning; The Best Data Science Certification You’ve Never Heard Of

  Also: Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read; Pandas on Steroids: End to End Data Science in Python with Dask; Essential data science skills that no one talks about; DIY Election Fraud Analysis Using Benford's Law

  https://www.kdnuggets.com/2020/11/top-news-week-1102-1108.html

• Top KDnuggets tweets, Oct 28 – Nov 03: 11 Branches of #MachineLearning and 63 Important Machine Learning Algorithms; Top #Python Libraries for #DataScience, #DataVisualization, #MachineLearning

  Building Neural Networks with PyTorch in Google Colab; The Roadmap of Mathematics for Deep Learning; Top #Python Libraries for Data Science, Data Visualization, Machine Learning; 11 Branches of #MachineLearning and 63 Important Machine Learning Algorithms.

  https://www.kdnuggets.com/2020/11/top-tweets-oct28-nov03.html

• Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know

  The terms “interpretability,” “explainability” and “black box” are tossed about a lot in the context of machine learning, but what do they really mean, and why do they matter?

  https://www.kdnuggets.com/2020/11/interpretability-explainability-machine-learning.html

• KDnuggets™ News 20:n42, Nov 4: Top Python Libraries for Data Science, Data Visualization & Machine Learning; Mastering Time Series Analysis

  Top Python Libraries for Data Science, Data Visualization, Machine Learning; Mastering Time Series Analysis with Help From the Experts; Explaining the Explainable AI: A 2-Stage Approach; The Missing Teams For Data Scientists; and more.

  https://www.kdnuggets.com/2020/n42.html

• Data scientist or machine learning engineer? Which is a better career option?

  In order to build automated data processing systems, we require professionals like Machine Learning Engineers and Data Scientists. But which of these is a better career option right now? Read on to find out.

  https://www.kdnuggets.com/2020/11/greatlearning-data-scientist-machine-learning-engineer.html

• How to Make Sense of the Reinforcement Learning Agents?

  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.

  https://www.kdnuggets.com/2020/10/make-sense-reinforcement-learning-agents.html

• Dealing with Imbalanced Data in Machine Learning

  This article presents tools & techniques for handling data when it's imbalanced.

  https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html

• Exploring the Significance of Machine Learning for Algorithmic Trading with Stefan Jansen

  The immense expansion of digital data has increased the demand for proficiency in trading strategies that use machine learning (ML). Learn more from author Stefan Jansen, and get his latest book on the subject from Packt Publishing.

  https://www.kdnuggets.com/2020/10/packt-significance-machine-learning-algorithmic-trading.html

• Top Stories, Oct 19-25: How to Explain Key Machine Learning Algorithms at an Interview; Roadmap to Natural Language Processing

  Also: Roadmap to Natural Language Processing (NLP); 5 Must-Read Data Science Papers (and How to Use Them); DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models; Good-bye Big Data. Hello, Massive Data!

  https://www.kdnuggets.com/2020/10/top-news-week-1019-1025.html

• Behavior Analysis with Machine Learning and R: The free eBook

  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.

  https://www.kdnuggets.com/2020/10/behavior-analysis-machine-learning-r-free-ebook.html

• Machine Learning’s Greatest Omission: Business Leadership

  Eric Siegel's business-oriented, vendor-neutral machine learning course is designed to fulfill vital unmet learner needs, delivering material critical for both techies and business leaders.

  https://www.kdnuggets.com/2020/10/machine-learning-omission-business-leadership.html

• Top September Stories: Free From MIT: Intro to Computer Science and Programming in Python; Best Online MS in AI, Analytics, Data Science, Machine Learning

  Also: Introduction to Time Series Analysis in Python; Automating Every Aspect of Your Python Project

  https://www.kdnuggets.com/2020/10/top-stories-2020-sep.html

• Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform

  The new release makes Ludwig one of the most complete open source AutoML stacks in the market.

  https://www.kdnuggets.com/2020/10/uber-open-source-ludwig-code-free-machine-learning-platform.html

• Top Stories, Oct 5-11: A step-by-step guide for creating an authentic data science portfolio project; 10 Best Machine Learning Courses in 2020

  Also: Free Introductory Machine Learning Course From Amazon; How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems; A step-by-step guide for creating an authentic data science portfolio project; Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

  https://www.kdnuggets.com/2020/10/top-news-week-1005-1011.html

• Annotated Machine Learning Research Papers">Annotated Machine Learning Research Papers

  Check out this collection of annotated machine learning research papers, and no longer fear their reading.

  https://www.kdnuggets.com/2020/10/annotated-machine-learning-research-papers.html

• Free Introductory Machine Learning Course From Amazon">Free Introductory Machine Learning Course From Amazon

  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.

  https://www.kdnuggets.com/2020/10/machine-learning-free-course-amazon.html

• 5 Challenges to Scaling Machine Learning Models

  ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.

  https://www.kdnuggets.com/2020/10/5-challenges-scaling-machine-learning-models.html

• 10 Best Machine Learning Courses in 2020">10 Best Machine Learning Courses in 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.

  https://www.kdnuggets.com/2020/10/10-best-machine-learning-courses-2020.html

• International alternatives to Kaggle for Data Science / Machine Learning competitions

  While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.

  https://www.kdnuggets.com/2020/09/international-alternatives-kaggle-data-science-competitions.html

• Top Stories, Sep 21-27: Introduction to Time Series Analysis in Python; Machine Learning from Scratch: Free Online Textbook

  Also: How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy; I'm a Data Scientist, Not Just The Tiny Hands that Crunch your Data; New Poll: What Python IDE / Editor you used the most in 2020?; The Most Complete Guide to PyTorch for Data Scientists

  https://www.kdnuggets.com/2020/09/top-news-week-0921-0927.html

• Top KDnuggets tweets, Sep 16-22: An overview of 63 #MachineLearning algorithms

  Also: Online Certificates/Courses in #AI, #BusinessAnalytics, #DataScience, #MachineLearning from Top Universities; 24 Best (and #Free) #Books To Understand #MachineLearning; New Poll: What Python IDE / Editor you used the most in 2020?; Mathematics for #MachineLearning: The #Free eBook

  https://www.kdnuggets.com/2020/09/top-tweets-sep16-22.html

• LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale

  The reference architecture is powering mission critical machine learning workflows within LinkedIn.

  https://www.kdnuggets.com/2020/09/linkedin-pro-ml-architecture-best-practices-building-machine-learning-scale.html

• Machine Learning from Scratch: Free Online Textbook">Machine Learning from Scratch: Free Online Textbook

  If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.

  https://www.kdnuggets.com/2020/09/machine-learning-from-scratch-free-online-textbook.html

• What an Argentine Writer and a Hungarian Mathematician Can Teach Us About Machine Learning Overfitting

  This article presents some beautiful ideas about intelligence and how they related to modern machine learning.

  https://www.kdnuggets.com/2020/09/what-argentine-writer-hungarian-mathematician-machine-learning-overfitting.html

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models

  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  https://www.kdnuggets.com/2020/09/insiders-guide-generative-discriminative-machine-learning-models.html

• Can Neural Networks Show Imagination? DeepMind Thinks They Can

  DeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.

  https://www.kdnuggets.com/2020/09/deepmind-neural-networks-show-imagination.html