Search results for "deep learning"

3151 documents found out of 7186 total.

• Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container

  In this tutorial, we will use a previously-built machine learning pipeline and Flask app to demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service.

  https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html

• Upgrading the Brand Mobile App with Machine Learning

  The tech progress in mobile app development, as well as digital enhancements, have created new chances for brands to allure and retain customers. In bridging the individualization gap, Machine Learning comes to the rescue.

  https://www.kdnuggets.com/2020/06/upgrading-brand-mobile-app-machine-learning.html

• How to make AI/Machine Learning models resilient during COVID-19 crisis

  COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.

  https://www.kdnuggets.com/2020/06/ai-ml-models-resilient-covid-19-crisis.html

• Upcoming Webinars and Online Events in AI, Data Science, Machine Learning: June

  Here are some interesting upcoming webinar, online events and virtual conferences in in AI, Data Science, and Machine Learning.

  https://www.kdnuggets.com/2020/06/upcoming-webinars-online-events-june.html

• Machine Learning Experiment Tracking

  Why is experiment tracking so important for doing real world machine learning?

  https://www.kdnuggets.com/2020/06/machine-learning-experiment-tracking.html

• 5 Machine Learning Papers on Face Recognition

  This article will highlight some of that research and introduce five machine learning papers on face recognition.

  https://www.kdnuggets.com/2020/05/5-machine-learning-papers-face-recognition.html

• Deepmind’s Gaming Streak: The Rise of AI Dominance

  There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.

  https://www.kdnuggets.com/2020/05/deepmind-gaming-ai-dominance.html

• Interactive Machine Learning Experiments

  Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.

  https://www.kdnuggets.com/2020/05/interactive-machine-learning-experiments.html

• Build and deploy your first machine learning web app">Build and deploy your first machine learning web app

  A beginner’s guide to train and deploy machine learning pipelines in Python using PyCaret.

  https://www.kdnuggets.com/2020/05/build-deploy-machine-learning-web-app.html

• What they do not tell you about machine learning

  There's a lot of excitement out there about machine learning jobs. So, it's always good to start off with a healthy dose of reality and proper expectations.

  https://www.kdnuggets.com/2020/05/not-tell-machine-learning.html

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

  There is a lot to learn about automated machine learning theory and practice. This free eBook can get you started the right way.

  https://www.kdnuggets.com/2020/05/automated-machine-learning-free-ebook.html

• AI and Machine Learning for Healthcare">AI and Machine Learning for Healthcare

  Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.

  https://www.kdnuggets.com/2020/05/ai-machine-learning-healthcare.html

• I Designed My Own Machine Learning and AI Degree

  With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.

  https://www.kdnuggets.com/2020/05/designed-machine-learning-ai-degree.html

• The Elements of Statistical Learning: The Free eBook

  Check out this free ebook covering the elements of statistical learning, appropriately titled "The Elements of Statistical Learning."

  https://www.kdnuggets.com/2020/05/elements-statistical-learning-free-ebook.html

• Start Your Machine Learning Career in Quarantine">Start Your Machine Learning Career in Quarantine

  While this quarantine can last two months, make the most of it by starting your career in Machine Learning with this 60-day learning plan.

  https://www.kdnuggets.com/2020/05/machine-learning-career-quarantine.html

• The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models

  The new typed feature schema streamlined the reusability of features across thousands of machine learning models.

  https://www.kdnuggets.com/2020/05/architecture-linkedin-feature-management-machine-learning-models.html

• Will Machine Learning Engineers Exist in 10 Years?

  As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.

  https://www.kdnuggets.com/2020/05/machine-learning-engineers-not-exist-10-years.html

• Hyperparameter Optimization for Machine Learning Models

  Check out this comprehensive guide to model optimization techniques.

  https://www.kdnuggets.com/2020/05/hyperparameter-optimization-machine-learning-models.html

• Beginners Learning Path for Machine Learning">Beginners Learning Path for Machine Learning

  So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

  https://www.kdnuggets.com/2020/05/beginners-learning-path-machine-learning.html

• Microsoft Research Unveils Three Efforts to Advance Deep Generative Models

  Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

  https://www.kdnuggets.com/2020/05/microsoft-research-three-efforts-advance-deep-generative-models.html

• Optimize Response Time of your Machine Learning API In Production

  This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

  https://www.kdnuggets.com/2020/05/optimize-response-time-machine-learning-api-production.html

• 10 Best Machine Learning Textbooks that All Data Scientists Should Read

  Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

  https://www.kdnuggets.com/2020/04/10-best-machine-learning-textbooks-data-scientists.html

• Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition">Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition

  If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

  https://www.kdnuggets.com/2020/04/machine-learning-data-science-books-courses-quarantine.html

• OpenAI Open Sources Microscope and the Lucid Library to Visualize Neurons in Deep Neural Networks

  The new tools shows the potential of data visualizations for understanding features in a neural network.

  https://www.kdnuggets.com/2020/04/openai-open-sources-microscope-lucid-library-neural-networks.html

• State of the Machine Learning and AI Industry

  Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  https://www.kdnuggets.com/2020/04/machine-learning-ai-industry.html

• Federated Learning: An Introduction

  Improving machine learning models and making them more secure by training on decentralized data.

  https://www.kdnuggets.com/2020/04/federated-learning-introduction.html

• Can Java Be Used for Machine Learning and Data Science?">Can Java Be Used for Machine Learning and Data Science?

  While Python and R have become favorites for building these programs, many organizations are turning to Java application development to meet their needs. Read on to see how, and why.

  https://www.kdnuggets.com/2020/04/java-used-machine-learning-data-science.html

• DeepMind Unveils Agent57, the First AI Agents that Outperforms Human Benchmarks in 57 Atari Games

  The new reinforcement learning agent innovates over previous architectures achieving one of the most important milestones in the AI space.

  https://www.kdnuggets.com/2020/04/deepmind-agent57-atari-games.html

• Upcoming Webinars and online events in AI, Data Science, Machine Learning

  Use the time at home productively and learn something new! We bring you a selection of upcoming interesting webinars and online events on AI, Data Science, Machine Learning, and related topics.

  https://www.kdnuggets.com/2020/04/upcoming-webinars-online-events.html

• 10 Must-read Machine Learning Articles (March 2020)">10 Must-read Machine Learning Articles (March 2020)

  This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.

  https://www.kdnuggets.com/2020/04/10-must-read-machine-learning-articles-march-2020.html

• 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency

  Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.

  https://www.kdnuggets.com/2020/04/2-things-reinforcement-learning.html

• Microsoft Research Uses Transfer Learning to Train Real-World Autonomous Drones

  The new research uses policies learned in simulations in real world drone environments.

  https://www.kdnuggets.com/2020/03/microsoft-research-transfer-learning-train-real-world-autonomous-drones.html

• Making sense of ensemble learning techniques

  This article breaks down ensemble learning and how it can be used for problem solving.

  https://www.kdnuggets.com/2020/03/making-sense-ensemble-learning-techniques.html

• Diffusion Map for Manifold Learning, Theory and Implementation

  This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions.

  https://www.kdnuggets.com/2020/03/diffusion-map-manifold-learning-theory-implementation.html

• Top AI Resources – Directory for Remote Learning

  Whether you are just learning Data Science, a current professional, or just interested, it's crucial to keep the mind stimulated and stay current. With conferences, schools, and travel largely canceled because of #coronavirus, these remote resources will help you stay engaged.

  https://www.kdnuggets.com/2020/03/top-ai-resources-remote-learning.html

• Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models

  TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures.

  https://www.kdnuggets.com/2020/03/tensorflow-quantum-framework-quantum-machine-learning-models.html

• Building a Mature Machine Learning Team

  After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market.

  https://www.kdnuggets.com/2020/03/mature-machine-learning-team.html

• The Most Useful Machine Learning Tools of 2020

  This articles outlines 5 sets of tools every lazy full-stack data scientist should use.

  https://www.kdnuggets.com/2020/03/most-useful-machine-learning-tools-2020.html

• Decision Boundary for a Series of Machine Learning Models

  I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions.

  https://www.kdnuggets.com/2020/03/decision-boundary-series-machine-learning-models.html

• Few-Shot Image Classification with Meta-Learning

  Here is how you can teach your model to learn quickly from a few examples.

  https://www.kdnuggets.com/2020/03/few-shot-image-classification-meta-learning.html

• Top KDnuggets tweets, Mar 04-10: 10 Free Must-Read Books for Machine Learning and Data Science

  Also: The three phases of #COVID19 – and how we can make it manageable; 50 Must-Read Free Books For Every Data Scientist in 2020; Binary classification is a core machine learning technique, but is there a better way to evaluate its performance than ROC-AUC?

  https://www.kdnuggets.com/2020/03/top-tweets-mar04-10.html

• Software Interfaces for Machine Learning Deployment

  While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable.

  https://www.kdnuggets.com/2020/03/software-interfaces-machine-learning-deployment.html

• 21 Machine Learning Projects – Datasets Included

  Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today.

  https://www.kdnuggets.com/2020/03/20-machine-learning-datasets-project-ideas.html

• A Crash Course in Game Theory for Machine Learning: Classic and New Ideas

  Game theory is experiencing a renaissance driven by the evolution of AI. What are some classic and new ideas that data scientists should be aware of.

  https://www.kdnuggets.com/2020/03/crash-course-game-theory-machine-learning.html

• Phishytics – Machine Learning for Detecting Phishing Websites

  Since phishing is such a widespread problem in the cybersecurity domain, let us take a look at the application of machine learning for phishing website detection.

  https://www.kdnuggets.com/2020/03/phishytics-machine-learning-detecting-phishing-websites.html

• The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images

  Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.

  https://www.kdnuggets.com/2020/03/the-augmented-scientist-practical-application-machine-learning-classification-images.html

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2)">20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2)

  We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.

  https://www.kdnuggets.com/2020/03/ai-data-science-machine-learning-key-terms-part2.html

• Uber Unveils a New Service for Backtesting Machine Learning Models at Scale

  The transportation giant built a new service and architecture for backtesting forecasting models.

  https://www.kdnuggets.com/2020/03/uber-unveils-service-backtesting-machine-learning-models-scale.html

• Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms">Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms

  The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.

  https://www.kdnuggets.com/2020/02/gartner-mq-2020-data-science-machine-learning.html

• Microsoft Open Sources ZeRO and DeepSpeed: The Technologies Behind the Biggest Language Model in History

  The two efforts enable the training of deep learning models at massive scale.

  https://www.kdnuggets.com/2020/02/microsoft-open-sources-zero-deepspeed-language-model.html

• Prepare for a Long Battle against Deepfakes

  While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.

  https://www.kdnuggets.com/2020/02/long-battle-against-deepfakes.html

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)">20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)

  2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.

  https://www.kdnuggets.com/2020/02/ai-data-science-machine-learning-key-terms-2020.html

• Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything

  Meena is one of the major milestones in the history of NLU. How did Google build it?

  https://www.kdnuggets.com/2020/02/inside-machine-learning-google-build-meena-chatbot.html

• Deep Neural Networks

  We examine the features and applications of a deep neural network.

  https://www.kdnuggets.com/2020/02/deep-neural-networks.html

• What Does it Mean to Deploy a Machine Learning Model?

  You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.

  https://www.kdnuggets.com/2020/02/deploy-machine-learning-model.html

• Sharing your machine learning models through a common API

  DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.

  https://www.kdnuggets.com/2020/02/sharing-machine-learning-models-common-api.html

• Amazon Uses Self-Learning to Teach Alexa to Correct its Own Mistakes

  The digital assistant incorporates a reformulation engine that can learn to correct responses in real time based on customer interactions.

  https://www.kdnuggets.com/2020/02/amazon-uses-self-learning-teach-alexa-correct-mistakes.html

• Intro to Machine Learning and AI based on high school knowledge

  Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.

  https://www.kdnuggets.com/2020/02/intro-machine-learning-ai.html

• Why are Machine Learning Projects so Hard to Manage?

  What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.

  https://www.kdnuggets.com/2020/02/machine-learning-projects-manage.html

• Past 2020 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-2020.html

• Data Validation for Machine Learning">Data Validation for Machine Learning

  While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model.

  https://www.kdnuggets.com/2020/01/data-validation-machine-learning.html

• Top 10 AI, Machine Learning Research Articles to know">Top 10 AI, Machine Learning Research Articles to know

  We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.

  https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html

• Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools

  Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.

  https://www.kdnuggets.com/2020/01/managing-machine-learning-cycles.html

• Semi-supervised learning with Generative Adversarial Networks

  The paper discussed in this post, Semi-supervised learning with Generative Adversarial Networks, utilizes a GAN architecture for multi-label classification.

  https://www.kdnuggets.com/2020/01/semi-supervised-learning-generative-adversarial-networks.html

• Random Forest® — A Powerful Ensemble Learning Algorithm

  The article explains the Random Forest algorithm and how to build and optimize a Random Forest classifier.

  https://www.kdnuggets.com/2020/01/random-forest-powerful-ensemble-learning-algorithm.html

• The Future of Machine Learning

  This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.

  https://www.kdnuggets.com/2020/01/future-machine-learning.html

• Classify A Rare Event Using 5 Machine Learning Algorithms

  Which algorithm works best for unbalanced data? Are there any tradeoffs?

  https://www.kdnuggets.com/2020/01/classify-rare-event-machine-learning-algorithms.html

• Graph Machine Learning Meets UX: An uncharted love affair

  When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.

  https://www.kdnuggets.com/2020/01/graph-machine-learning-ux.html

• Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks

  The new technique can really improve how deep learning models are trained at scale.

  https://www.kdnuggets.com/2020/01/uber-generative-teaching-networks-train-neural-networks.html

• Deepfakes Security Risks

  Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

  https://www.kdnuggets.com/2020/01/deepfakes-security-risks.html

• The Book to Start You on Machine Learning">The Book to Start You on Machine Learning

  This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

  https://www.kdnuggets.com/2020/01/book-start-machine-learning.html

• Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models

  There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.

  https://www.kdnuggets.com/2020/01/generalized-integrated-gradients-explaining-ensemble-models.html

• H2O Framework for Machine Learning

  This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

  https://www.kdnuggets.com/2020/01/h2o-framework-machine-learning.html

• How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4

  In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.

  https://www.kdnuggets.com/2019/12/ultralearn-data-science-deep-understanding-experimentation-part4.html

• 10 Best and Free Machine Learning Courses, Online

  Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

  https://www.kdnuggets.com/2019/12/best-free-machine-learning-courses-online.html

• Microsoft Introduces Icebreaker to Address the Famous Ice-Start Challenge in Machine Learning

  The new technique allows the deployment of machine learning models that operate with minimum training data.

  https://www.kdnuggets.com/2019/12/microsoft-introduces-icebreaker-ice-start-challenge-machine-learning.html

• Scalable graph machine learning: a mountain we can climb?

  Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.

  https://www.kdnuggets.com/2019/12/scalable-graph-machine-learning.html

• DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules

  The new model showed great improvements over the previous AlphaZero agent.

  https://www.kdnuggets.com/2019/12/deepmind-unveils-muzero-agent-chess-shogi-atari-go.html

• 10 Free Top Notch Machine Learning Courses">10 Free Top Notch Machine Learning Courses

  Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.

  https://www.kdnuggets.com/2019/12/10-free-top-notch-courses-machine-learning.html

• Why software engineering processes and tools don’t work for machine learning

  While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.

  https://www.kdnuggets.com/2019/12/comet-software-engineering-machine-learning.html

• Lit BERT: NLP Transfer Learning In 3 Steps

  PyTorch Lightning is a lightweight framework which allows anyone using PyTorch to scale deep learning code easily while making it reproducible. In this tutorial we’ll use Huggingface's implementation of BERT to do a finetuning task in Lightning.

  https://www.kdnuggets.com/2019/11/lit-bert-nlp-transfer-learning-3-steps.html

• Machine Learning 101: The What, Why, and How of Weighting

  Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.

  https://www.kdnuggets.com/2019/11/machine-learning-what-why-how-weighting.html

• Probability Learning: Naive Bayes

  This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.

  https://www.kdnuggets.com/2019/11/probability-learning-naive-bayes.html

• Automated Machine Learning Project Implementation Complexities">Automated Machine Learning Project Implementation Complexities

  To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.

  https://www.kdnuggets.com/2019/11/automl-implementation-complexities.html

• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead">Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

  https://www.kdnuggets.com/2019/11/stop-explaining-black-box-models.html

• The Reinforcement-Learning Methods that Allow AlphaStar to Outcompete Almost All Human Players at StarCraft II

  The new AlphaStar achieved Grandmaster level at StarCraft II overcoming some of the limitations of the previous version. How did it do it?

  https://www.kdnuggets.com/2019/11/reinforcement-learning-methods-alphastar-outcompete-human-players-starcraft.html

• GitHub Repo Raider and the Automation of Machine Learning

  Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.

  https://www.kdnuggets.com/2019/11/github-repo-raider-automated-machine-learning.html

• Testing Your Machine Learning Pipelines

  Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.

  https://www.kdnuggets.com/2019/11/testing-machine-learning-pipelines.html

• Transfer Learning Made Easy: Coding a Powerful Technique

  While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.

  https://www.kdnuggets.com/2019/11/transfer-learning-coding.html

• Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal

  ReAgent is a new framework that streamlines the implementation of reasoning systems.

  https://www.kdnuggets.com/2019/11/facebook-adds-this-new-framework-reinforcement-learning-arsenal.html

• Research Guide: Advanced Loss Functions for Machine Learning Models

  This guide explores research centered on a variety of advanced loss functions for machine learning models.

  https://www.kdnuggets.com/2019/11/research-guide-advanced-loss-functions-machine-learning-models.html

• Probability Learning: Maximum Likelihood

  The maths behind Bayes will be better understood if we first cover the theory and maths underlying another fundamental method of probabilistic machine learning: Maximum Likelihood. This post will be dedicated to explaining it.

  https://www.kdnuggets.com/2019/11/probability-learning-maximum-likelihood.html

• Top Machine Learning Software Tools for Developers">Top Machine Learning Software Tools for Developers

  As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

  https://www.kdnuggets.com/2019/11/top-machine-learning-software-developers.html

• What is Machine Learning on Code?

  Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.

  https://www.kdnuggets.com/2019/11/machine-learning-code-mloncode.html

• Why is Machine Learning Deployment Hard?">Why is Machine Learning Deployment Hard?

  Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

  https://www.kdnuggets.com/2019/10/machine-learning-deployment-hard.html

• How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow

  In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

  https://www.kdnuggets.com/2019/10/extend-scikit-learn-bring-sanity-machine-learning-workflow.html

• How Bayes’ Theorem is Applied in Machine Learning

  Learn how Bayes Theorem is in Machine Learning for classification and regression!

  https://www.kdnuggets.com/2019/10/bayes-theorem-applied-machine-learning.html

• Intro to Adversarial Machine Learning and Generative Adversarial Networks

  In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.

  https://www.kdnuggets.com/2019/10/adversarial-machine-learning-generative-adversarial-networks.html

• Probability Learning: Bayes’ Theorem

  Learn about one of the fundamental theorems of probability with an easy everyday example.

  https://www.kdnuggets.com/2019/10/probability-learning-bayes-theorem.html

• Three Things to Know About Reinforcement Learning

  As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.

  https://www.kdnuggets.com/2019/10/mathworks-reinforcement-learning.html

• Choosing a Machine Learning Model

  Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.

  https://www.kdnuggets.com/2019/10/choosing-machine-learning-model.html

• Recreating Imagination: DeepMind Builds Neural Networks that Spontaneously Replay Past Experiences

  DeepMind researchers created a model to be able to replay past experiences in a way that simulate the mechanisms in the hippocampus.

  https://www.kdnuggets.com/2019/10/recreating-imagination-deepmind-builds-neural-networks-spontaneously-replay-past-experiences.html

• Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning

  While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.

  https://www.kdnuggets.com/2019/09/scikit-learn-synthetic-dataset.html

• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python

  “I want to learn machine learning and artificial intelligence, where do I start?” Here.

  https://www.kdnuggets.com/2019/09/5-beginner-friendly-steps-learn-machine-learning-data-science-python.html

• Explore the world of Bioinformatics with Machine Learning">Explore the world of Bioinformatics with Machine Learning

  The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.

  https://www.kdnuggets.com/2019/09/explore-world-bioinformatics-machine-learning.html

• Cartoon: Unsupervised Machine Learning?">Cartoon: Unsupervised Machine Learning?

  New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?"

  https://www.kdnuggets.com/2019/09/cartoon-unsupervised-machine-learning.html

• Many Heads Are Better Than One: The Case For Ensemble Learning

  While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.

  https://www.kdnuggets.com/2019/09/ensemble-learning.html

• Can graph machine learning identify hate speech in online social networks?

  Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

  https://www.kdnuggets.com/2019/09/graph-machine-learning-hate-speech-social-networks.html

• Common Machine Learning Obstacles

  In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

  https://www.kdnuggets.com/2019/09/mathworks-common-machine-learning-obstacles.html

• Advice on building a machine learning career and reading research papers by Prof. Andrew Ng">Advice on building a machine learning career and reading research papers by Prof. Andrew Ng

  This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.

  https://www.kdnuggets.com/2019/09/advice-building-machine-learning-career-research-papers-andrew-ng.html

• Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing

  Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

  https://www.kdnuggets.com/2019/08/introducing-ai-explainability-360-toolkit-understand-machine-learning-models.html

• How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions

  As machine learning evolves, the need for tools and platforms that automate the lifecycle management of training and testing datasets is becoming increasingly important. Fast growing technology companies like Uber or LinkedIn have been forced to build their own in-house data lifecycle management solutions to power different groups of machine learning models.

  https://www.kdnuggets.com/2019/08/linkedin-uber-lyft-airbnb-netflix-solving-data-management-discovery-machine-learning-solutions.html

• Understanding Cancer using Machine Learning">Understanding Cancer using Machine Learning

  Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis.

  https://www.kdnuggets.com/2019/08/understanding-cancer-machine-learning.html

• Statistical Modelling vs Machine Learning">Statistical Modelling vs Machine Learning

  At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

  https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html

• 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”">6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”

  If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

  https://www.kdnuggets.com/2019/08/key-concepts-andrew-ng-machine-learning-yearning.html

• Knowing Your Neighbours: Machine Learning on Graphs">Knowing Your Neighbours: Machine Learning on Graphs

  Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.

  https://www.kdnuggets.com/2019/08/neighbours-machine-learning-graphs.html

• Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment

  This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside.

  https://www.kdnuggets.com/2019/08/machine-learning-happening-now-survey-organizational-adoption-implementation-investment.html

• Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning">Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning

  Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

  https://www.kdnuggets.com/2019/07/best-podcasts-ai-analytics-data-science-machine-learning.html

• High-Quality AI And Machine Learning Data Labeling At Scale: A Brief Research Report

  Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?

  https://www.kdnuggets.com/2019/07/high-quality-ai-machine-learning-data-labeling-research-report.html

• Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI">Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI

  Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.

  https://www.kdnuggets.com/2019/07/top-certificates-analytics-data-science-machine-learning-ai.html

• 12 Things I Learned During My First Year as a Machine Learning Engineer

  Learn about the day-in-the-life of one machine learning engineer and the important lessons learned for being successful in that role.

  https://www.kdnuggets.com/2019/07/12-things-learned-machine-learning-engineer.html

• Is Bias in Machine Learning all Bad?

  We have been taught over our years of predictive model building that bias will harm our model. Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias.

  https://www.kdnuggets.com/2019/07/bias-machine-learning-bad.html

• Adapters: A Compact and Extensible Transfer Learning Method for NLP

  Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.

  https://www.kdnuggets.com/2019/07/adapters-compact-extensible-transfer-learning-method-nlp.html

• A Summary of DeepMind’s Protein Folding Upset at CASP13">A Summary of DeepMind’s Protein Folding Upset at CASP13

  Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.

  https://www.kdnuggets.com/2019/07/deepmind-protein-folding-upset.html

• Collaborative Evolutionary Reinforcement Learning

  Intel Researchers created a new approach to RL via Collaborative Evolutionary Reinforcement Learning (CERL) that combines policy gradient and evolution methods to optimize, exploit, and explore challenges.

  https://www.kdnuggets.com/2019/07/collaborative-evolutionary-reinforcement-learning.html

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph

  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  https://www.kdnuggets.com/2019/06/data-fabric-machine-learning-building-knowledge-graph.html

• How Google uses Reinforcement Learning to Train AI Agents in the Most Popular Sport in the World

  Researchers from the Google Brain team open sourced Google Research Football, a new environment that leverages reinforcement learning to teach AI agents how to master the most popular sport in the world.

  https://www.kdnuggets.com/2019/06/google-reinforcement-learning-ai-agents-sport.html

• Evolving Deep Neural Networks

  This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural network design.

  https://www.kdnuggets.com/2019/06/evolving-deep-neural-networks.html

• Why Machine Learning is vulnerable to adversarial attacks and how to fix it

  Machine learning can process data imperceptible to humans to produce expected results. These inconceivable patterns are inherent in the data but may make models vulnerable to adversarial attacks. How can developers harness these features to not lose control of AI?

  https://www.kdnuggets.com/2019/06/machine-learning-adversarial-attacks.html

• Overview of Different Approaches to Deploying Machine Learning Models in Production

  Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.html