Search results for "deep learning"

3151 documents found out of 7186 total.

• The 20 Python Packages You Need For Machine Learning and Data Science">The 20 Python Packages You Need For Machine Learning and Data Science

  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.

  https://www.kdnuggets.com/2021/10/20-python-packages.html

• Here’s Why You Need Python Skills as a Machine Learning Engineer">Here’s Why You Need Python Skills as a Machine Learning Engineer

  If you want to learn how to apply Python programming skills in the context of AI applications, the UC San Diego Extension Machine Learning Engineering Bootcamp can help. Read on to find out more about how machine learning engineers use Python, and why the language dominates today’s machine learning landscape.

  https://www.kdnuggets.com/2021/10/bootcamp-python-skills-machine-learning-engineer.html

• Cartoon: How Deep Is That Data Lake?

  New KDnuggets Cartoon looks at some of the problems data engineers may encounter when trying to measure data lakes.

  https://www.kdnuggets.com/2021/10/cartoon-data-lake.html

• 20 Machine Learning Projects That Will Get You Hired">20 Machine Learning Projects That Will Get You Hired

  If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.

  https://www.kdnuggets.com/2021/09/20-machine-learning-projects-hired.html

• Nine Tools I Wish I Mastered Before My PhD in Machine Learning">Nine Tools I Wish I Mastered Before My PhD in Machine Learning

  Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.

  https://www.kdnuggets.com/2021/09/nine-tools-mastered-before-phd-machine-learning.html

• How to Find Weaknesses in your Machine Learning Models">How to Find Weaknesses in your Machine Learning Models

  FreaAI: a new method from researchers at IBM.

  https://www.kdnuggets.com/2021/09/weaknesses-machine-learning-models.html

• Introduction to Automated Machine Learning

  AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.

  https://www.kdnuggets.com/2021/09/introduction-automated-machine-learning.html

• An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab

  Get an Introduction to Reinforcement Learning by attempting to balance a virtual CartPole with OpenAI Gym, RLlib, and Google Colab.

  https://www.kdnuggets.com/2021/09/intro-reinforcement-learning-openai-gym-rllib-colab.html

• Top 18 Low-Code and No-Code Machine Learning Platforms">Top 18 Low-Code and No-Code Machine Learning Platforms

  Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.

  https://www.kdnuggets.com/2021/09/top-18-low-code-no-code-machine-learning-platforms.html

• Math 2.0: The Fundamental Importance of Machine Learning

  Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.

  https://www.kdnuggets.com/2021/09/math-fundamental-importance-machine-learning.html

• How Machine Learning Leverages Linear Algebra to Solve Data Problems

  Why you should learn the fundamentals of linear algebra.

  https://www.kdnuggets.com/2021/09/machine-learning-leverages-linear-algebra-solve-data-problems.html

• How to solve machine learning problems in the real world

  Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.

  https://www.kdnuggets.com/2021/09/solve-machine-learning-problems-real-world.html

• Learning Data Science and Machine Learning: First Steps After The Roadmap">Learning Data Science and Machine Learning: First Steps After The Roadmap

  Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.

  https://www.kdnuggets.com/2021/08/learn-data-science-machine-learning.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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities">Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities

  We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.

  https://www.kdnuggets.com/2020/09/online-certificates-ai-data-science-machine-learning-top.html

• Autograd: The Best Machine Learning Library You’re Not Using?">Autograd: The Best Machine Learning Library You’re Not Using?

  If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.

  https://www.kdnuggets.com/2020/09/autograd-best-machine-learning-library-not-using.html

• Top August Stories: Know What Employers are Expecting for a Data Scientist Role in 2020; If I had to start learning Data Science again, how would I do it?

  Also: Netflix's Polynote is a New Open Source Framework to Build Better Data Science Notebooks; Must-read NLP and Deep Learning articles for Data Scientists.

  https://www.kdnuggets.com/2020/09/top-stories-2020-aug.html

• 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out">8 AI/Machine Learning Projects To Make Your Portfolio Stand Out

  If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.

  https://www.kdnuggets.com/2020/09/8-ml-ai-projects-stand-out.html

• How to Evaluate the Performance of Your Machine Learning Model">How to Evaluate the Performance of Your Machine Learning Model

  You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.

  https://www.kdnuggets.com/2020/09/performance-machine-learning-model.html

• Breaking Privacy in Federated Learning

  Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.

  https://www.kdnuggets.com/2020/08/breaking-privacy-federated-learning.html

• A Deep Dive Into the Transformer Architecture – The Development of Transformer Models

  Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.

  https://www.kdnuggets.com/2020/08/transformer-architecture-development-transformer-models.html

• Introduction to Federated Learning">Introduction to Federated Learning

  Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.

  https://www.kdnuggets.com/2020/08/introduction-federated-learning.html

• Top Google AI, Machine Learning Tools for Everyone">Top Google AI, Machine Learning Tools for Everyone

  Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.

  https://www.kdnuggets.com/2020/08/top-google-ai-machine-learning-tools.html

• Batch Normalization in Deep Neural Networks

  Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.

  https://www.kdnuggets.com/2020/08/batch-normalization-deep-neural-networks.html

• Awesome Machine Learning and AI Courses">Awesome Machine Learning and AI Courses

  Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.

  https://www.kdnuggets.com/2020/07/awesome-machine-learning-ai-courses.html

• A Tour of End-to-End Machine Learning Platforms

  An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  https://www.kdnuggets.com/2020/07/tour-end-to-end-machine-learning-platforms.html

• What I learned from looking at 200 machine learning tools

  While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.

  https://www.kdnuggets.com/2020/07/200-machine-learning-tools.html

• Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook

  The second edition of Data Mining and Machine Learning: Fundamental Concepts and Algorithms is available to read freely online, and includes a new part on regression with chapters on linear regression, logistic regression, neural networks, deep learning and regression assessment.

  https://www.kdnuggets.com/2020/07/data-mining-machine-learning-free-ebook.html

• Wrapping Machine Learning Techniques Within AI-JACK Library in R">Wrapping Machine Learning Techniques Within AI-JACK Library in R

  The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.

  https://www.kdnuggets.com/2020/07/wrapping-machine-learning-techniques-ai-jack-library-r.html

• The Bitter Lesson of Machine Learning">The Bitter Lesson of Machine Learning

  Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.

  https://www.kdnuggets.com/2020/07/bitter-lesson-machine-learning.html

• Some Things Uber Learned from Running Machine Learning at Scale

  Uber machine learning runtime Michelangelo has been in operation for a few years. What has the Uber team learned?

  https://www.kdnuggets.com/2020/07/some-things-uber-learned-machine-learning-scale.html

• 5th International Summer School 2020 on Resource-aware Machine Learning (REAML)

  The Resource-aware Machine Learning summer school provides lectures on the latest research in machine learning, with the twist on resource consumption and how these can be reduced. This year it will be held online between 31st of August and 4th of September, and is free of charge. Register now.

  https://www.kdnuggets.com/2020/07/tu-dortmund-summer-school-2020-reaml.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• An Introduction to Statistical Learning: The Free eBook

  This week's free eBook is a classic of data science, An Introduction to Statistical Learning, with Applications in R. If interested in picking up elementary statistical learning concepts, and learning how to implement them in R, this book is for you.

  https://www.kdnuggets.com/2020/06/introduction-statistical-learning-free-ebook.html

• Machine Learning Engineer vs Data Scientist (Is Data Science Over?)

  What has been happening to the definition of Data Scientist over the past 5 years? Does it still exist or has it morphed into a new version of its old self? Learn more about the recent trends in job descriptions and salaries for data scientists, ML engineers, and others to best understand the best fit for your career trajectory and interests.

  https://www.kdnuggets.com/2020/06/machine-learning-engineer-vs-data-scientist.html

• Tools to Spot Deepfakes and AI-Generated Text

  The technologies that generate deepfake content is at the forefront of manipulating humans. While the research developing these algorithms is fascinating and will lead to powerful tools that enhance the way people create and work, in the wrong hands, these same tools drive misinformation at a scale we can't yet imagine. Stopping these bad actors using awesome tools is in your hands.

  https://www.kdnuggets.com/2020/06/dont-click-this-how-spot-deepfakes.html

• Graph Machine Learning in Genomic Prediction

  This work explores how genetic relationships can be exploited alongside genomic information to predict genetic traits with the aid of graph machine learning algorithms.

  https://www.kdnuggets.com/2020/06/graph-machine-learning-genomic-prediction.html

• Crop Disease Detection Using Machine Learning and Computer Vision

  Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.

  https://www.kdnuggets.com/2020/06/crop-disease-detection-computer-vision.html

• Best Machine Learning Youtube Videos Under 10 Minutes

  The Youtube videos on this list cover concepts such as what machine learning is, the basics of natural language processing, how computer vision works, and machine learning in video games.

  https://www.kdnuggets.com/2020/06/best-machine-learning-youtube-videos-under-10-minutes.html

• Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning">Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning

  The new framework allow developers with minimum experience to create and train machine learning models.

  https://www.kdnuggets.com/2020/06/uber-ludwig-open-source-framework-machine-learning.html

• Understanding Machine Learning: The Free eBook">Understanding Machine Learning: The Free eBook

  Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.

  https://www.kdnuggets.com/2020/06/understanding-machine-learning-free-ebook.html