Search results for "deep learning"

3151 documents found out of 7186 total.

• 3 Main Approaches to Machine Learning Models

  Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.

  https://www.kdnuggets.com/2019/06/main-approaches-machine-learning-models.html

• What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem">What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem

  We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.

  https://www.kdnuggets.com/2019/06/top-data-science-machine-learning-tools.html

• 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition"> 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition

  This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!

  https://www.kdnuggets.com/2019/06/7-steps-mastering-intermediate-machine-learning-python.html

• Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis">Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis

  Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.

  https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html

• Why organizations fail in scaling AI and Machine Learning

  We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.

  https://www.kdnuggets.com/2019/05/why-organizations-fail-scaling-ai-machine-learning.html

• AI in the Family: how to teach machine learning to your kids

  AI is all the rage with today’s programmers, but what about the next generation? Machine learning can be introduced to young ones just now learning about code, and you can help spark their interest.

  https://www.kdnuggets.com/2019/05/ai-machine-learning-kids.html

• End-to-End Machine Learning: Making videos from images

  Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.

  https://www.kdnuggets.com/2019/05/making-videos-from-images.html

• How do you teach physics to machine learning models?

  How to integrate physics-based models (these are math-based methods that explain the world around us) into machine learning models to reduce its computational complexity.

  https://www.kdnuggets.com/2019/05/physics-machine-learning-models.html

• The Data Fabric for Machine Learning – Part 1">The Data Fabric for Machine Learning – Part 1

  How the new advances in semantics and the data fabric can help us be better at Machine Learning

  https://www.kdnuggets.com/2019/05/data-fabric-machine-learning-part-1.html

• Building Recommender systems with Azure Machine Learning service

  Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.

  https://www.kdnuggets.com/2019/05/recommender-systems-azure-machine-learning.html

• Machine Learning in Agriculture: Applications and Techniques">Machine Learning in Agriculture: Applications and Techniques

  Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.

  https://www.kdnuggets.com/2019/05/machine-learning-agriculture-applications-techniques.html

• “Please, explain.” Interpretability of machine learning models

  Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.

  https://www.kdnuggets.com/2019/05/interpretability-machine-learning-models.html

• 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?">2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

  Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.

  https://www.kdnuggets.com/2019/05/new-poll-software-analytics-data-science-machine-learning.html

• The 3 Biggest Mistakes on Learning Data Science">The 3 Biggest Mistakes on Learning Data Science

  Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.

  https://www.kdnuggets.com/2019/05/biggest-mistakes-learning-data-science.html

• How to Automate Tasks on GitHub With Machine Learning for Fun and Profit

  Check this tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

  https://www.kdnuggets.com/2019/05/automate-tasks-github-machine-learning-fun-profit.html

• Top Data Science and Machine Learning Methods Used in 2018, 2019">Top Data Science and Machine Learning Methods Used in 2018, 2019

  Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.

  https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

• All you need to know about text preprocessing for NLP and Machine Learning

  We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.

  https://www.kdnuggets.com/2019/04/text-preprocessing-nlp-machine-learning.html

• Another 10 Free Must-See Courses for Machine Learning and Data Science">Another 10 Free Must-See Courses for Machine Learning and Data Science

  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  https://www.kdnuggets.com/2019/04/another-10-free-must-see-courses-machine-learning-data-science.html

• Training a Champion: Building Deep Neural Nets for Big Data Analytics

  Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.

  https://www.kdnuggets.com/2019/04/sisense-deep-neural-nets-big-data-analytics.html

• Deep Compression: Optimization Techniques for Inference & Efficiency

  We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.

  https://www.kdnuggets.com/2019/03/deep-compression-optimization-techniques-inference-efficiency.html

• Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision

  In this blog, I’ll walk you through a personal project in which I cheaply built a classifier to detect anti-semitic tweets, with no public dataset available, by combining weak supervision and transfer learning.

  https://www.kdnuggets.com/2019/03/building-nlp-classifiers-cheaply-transfer-learning-weak-supervision.html

• My favorite mind-blowing Machine Learning/AI breakthroughs">My favorite mind-blowing Machine Learning/AI breakthroughs

  We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.

  https://www.kdnuggets.com/2019/03/favorite-ml-ai-breakthroughs.html

• 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning">19 Inspiring Women in AI, Big Data, Data Science, Machine Learning

  For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.

  https://www.kdnuggets.com/2019/03/women-ai-big-data-science-machine-learning.html

• Another 10 Free Must-Read Books for Machine Learning and Data Science">Another 10 Free Must-Read Books for Machine Learning and Data Science

  Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.

  https://www.kdnuggets.com/2019/03/another-10-free-must-read-books-for-machine-learning-and-data-science.html

• What are Some “Advanced” AI and Machine Learning Online Courses?

  Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?

  https://www.kdnuggets.com/2019/02/some-advanced-ai-machine-learning-online-courses.html

• State of the art in AI and Machine Learning – highlights of papers with code

  We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.

  https://www.kdnuggets.com/2019/02/paperswithcode-ai-machine-learning-highlights.html

• Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms">Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms

  We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.

  https://www.kdnuggets.com/2019/02/gartner-2019-mq-data-science-machine-learning-changes.html

• Past 2019 Meetings / Conferences on AI, Analytics, Big Data, Data Science, and Machine Learning

  Past | 2019 Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Read more »

  https://www.kdnuggets.com/meetings/past-meetings-2019.html

• Five Ways Your Safety Depends on Machine Learning

  Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

  https://www.kdnuggets.com/2019/02/dr-data-five-ways-safety-depends-machine-learning.html

• Machine Learning Security

  We take a look at how malicious actors can break machine learning models and what some of the best practices are when it comes to stopping them.

  https://www.kdnuggets.com/2019/01/machine-learning-security.html

• Automated Machine Learning in Python

  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  https://www.kdnuggets.com/2019/01/automated-machine-learning-python.html

• Comparing Machine Learning Models: Statistical vs. Practical Significance

  Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.

  https://www.kdnuggets.com/2019/01/comparing-machine-learning-models-statistical-vs-practical-significance.html

• The Hundred-Page Machine Learning Book

  This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.

  https://www.kdnuggets.com/2019/01/hundred-page-machine-learning-book.html

• Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples

  We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.

  https://www.kdnuggets.com/2019/01/data-scientist-dilemma-cold-start-machine-learning.html

• How to build an API for a machine learning model in 5 minutes using Flask">How to build an API for a machine learning model in 5 minutes using Flask

  Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.

  https://www.kdnuggets.com/2019/01/build-api-machine-learning-model-using-flask.html

• The 6 Most Useful Machine Learning Projects of 2018

  Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.

  https://www.kdnuggets.com/2019/01/6-most-useful-machine-learning-projects-2018.html

• Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated

  Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  https://www.kdnuggets.com/2019/01/active-blogs-ai-analytics-data-science.html

• End To End Guide For Machine Learning Projects">End To End Guide For Machine Learning Projects

  Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

  https://www.kdnuggets.com/2019/01/end-to-end-guide-machine-learning-project.html

• 4 Myths of Big Data and 4 Ways to Improve with Deep Data

  There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.

  https://www.kdnuggets.com/2019/01/4-myths-big-data-deep-data.html

• Supervised Learning: Model Popularity from Past to Present

  An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?

  https://www.kdnuggets.com/2018/12/supervised-learning-model-popularity-from-past-present.html

• The Essence of Machine Learning">The Essence of Machine Learning

  And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.

  https://www.kdnuggets.com/2018/12/essence-machine-learning.html

• A Case For Explainable AI & Machine Learning

  In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.

  https://www.kdnuggets.com/2018/12/explainable-ai-machine-learning.html

• A Guide to Decision Trees for Machine Learning and Data Science">A Guide to Decision Trees for Machine Learning and Data Science

  What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

  https://www.kdnuggets.com/2018/12/guide-decision-trees-machine-learning-data-science.html

• Feature Engineering for Machine Learning: 10 Examples

  A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.

  https://www.kdnuggets.com/2018/12/feature-engineering-explained.html

• Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI

  We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.

  https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html

• 10 More Must-See Free Courses for Machine Learning and Data Science">10 More Must-See Free Courses for Machine Learning and Data Science

  Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.

  https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html

• Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019">Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019

  This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!

  https://www.kdnuggets.com/2018/12/predictions-industry-2019.html

• Top Stories of 2018: 9 Must-have skills you need to become a Data Scientist, updated; Python eats away at R: Top Software for Analytics, Data Science, Machine Learning

  Also 5 Data Science Projects That Will Get You Hired in 2018; Top 20 Python AI and Machine Learning Open Source Projects; Neural network AI is simple. So... Stop pretending you are a genius.

  https://www.kdnuggets.com/2018/12/top-stories-2018.html

• Machine Learning & AI Main Developments in 2018 and Key Trends for 2019">Machine Learning & AI Main Developments in 2018 and Key Trends for 2019

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.

  https://www.kdnuggets.com/2018/12/predictions-machine-learning-ai-2019.html

• Learning Machine Learning vs Learning Data Science">Learning Machine Learning vs Learning Data Science

  We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.

  https://www.kdnuggets.com/2018/12/learning-machine-learning-data-science.html

• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more

  A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.

  https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html

• The Machine Learning Project Checklist">The Machine Learning Project Checklist

  In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."

  https://www.kdnuggets.com/2018/12/machine-learning-project-checklist.html

• Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools">Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools

  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  https://www.kdnuggets.com/2018/12/machine-learning-data-visualization-deep-learning-tools.html

• Interpretability is crucial for trusting AI and machine learning

  We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.

  https://www.kdnuggets.com/2018/11/interpretability-trust-ai-machine-learning.html

• Bringing Machine Learning Research to Product Commercialization

  In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.

  https://www.kdnuggets.com/2018/11/bringing-machine-learning-research-product-commercialization.html

• 10 Free Must-See Courses for Machine Learning and Data Science">10 Free Must-See Courses for Machine Learning and Data Science

  Check out a collection of free machine learning and data science courses to kick off your winter learning season.

  https://www.kdnuggets.com/2018/11/10-free-must-see-courses-machine-learning-data-science.html

• Implementing Automated Machine Learning Systems with Open Source Tools

  What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.

  https://www.kdnuggets.com/2018/10/implementing-automated-machine-learning-open-source-path.html

• Introduction to Active Learning

  An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.

  https://www.kdnuggets.com/2018/10/introduction-active-learning.html

• Machine Reading Comprehension: Learning to Ask & Answer

  Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.

  https://www.kdnuggets.com/2018/10/machine-reading-comprehension-learning-ask-answer.html

• Top 8 Python Machine Learning Libraries">Top 8 Python Machine Learning Libraries

  Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  https://www.kdnuggets.com/2018/10/top-python-machine-learning-libraries.html

• A Concise Explanation of Learning Algorithms with the Mitchell Paradigm

  A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.

  https://www.kdnuggets.com/2018/10/mitchell-paradigm-concise-explanation-learning-algorithms.html

• Top Stories, Sep 24-30: Machine Learning Cheat Sheets; Learning the Mathematics of Machine Learning

  Also: Math for Machine Learning; Introducing Path Analysis Using R; Introduction to Deep Learning; Essential Math for Data Science: Why and How; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study

  https://www.kdnuggets.com/2018/10/top-news-week-0924-0930.html

• More Effective Transfer Learning for NLP

  Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.

  https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html

• When Bayes, Ockham, and Shannon come together to define machine learning

  A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.

  https://www.kdnuggets.com/2018/09/when-bayes-ockham-shannon-come-together-define-machine-learning.html

• 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study">6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study

  Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

  https://www.kdnuggets.com/2018/09/6-steps-write-machine-learning-algorithm.html

• Machine Learning Cheat Sheets">Machine Learning Cheat Sheets

  Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.

  https://www.kdnuggets.com/2018/09/machine-learning-cheat-sheets.html

• Machine Learning for Text Classification Using SpaCy in Python

  In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.

  https://www.kdnuggets.com/2018/09/machine-learning-text-classification-using-spacy-python.html

• Why Automated Feature Engineering Will Change the Way You Do Machine Learning

  Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.

  https://www.kdnuggets.com/2018/08/automated-feature-engineering-will-change-machine-learning.html

• Cartoon: Machine Learning takes a vacation

  August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.

  https://www.kdnuggets.com/2018/08/cartoon-machine-learning-vacation.html

• Reinforcement Learning: The Business Use Case, Part 2

  In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.

  https://www.kdnuggets.com/2018/08/reinforcement-learning-business-use-case-part-2.html

• Reinforcement Learning: The Business Use Case, Part 1

  At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.

  https://www.kdnuggets.com/2018/08/reinforcement-learning-business-use-case-part-1.html

• Intuitive Ensemble Learning Guide with Gradient Boosting

  This tutorial discusses the importance of ensemble learning with gradient boosting as a study case.

  https://www.kdnuggets.com/2018/07/intuitive-ensemble-learning-guide-gradient-boosting.html

• 9 Reasons why your machine learning project will fail

  This article explains in detail some of the issues that you may face during your machine learning project.

  https://www.kdnuggets.com/2018/07/why-machine-learning-project-fail.html

• fast.ai Machine Learning Course Notes

  This posts is a collection of a set of fantastic notes on the fast.ai machine learning MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  https://www.kdnuggets.com/2018/07/suenaga-fast-ai-machine-learning-notes.html

• Deep Quantile Regression

  Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.

  https://www.kdnuggets.com/2018/07/deep-quantile-regression.html

• Explaining Reinforcement Learning: Active vs Passive

  We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.

  https://www.kdnuggets.com/2018/06/explaining-reinforcement-learning-active-passive.html

• How to Execute R and Python in SQL Server with Machine Learning Services

  Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.

  https://www.kdnuggets.com/2018/06/microsoft-azure-machine-learning-r-python-sql-server.html

• Detecting Sarcasm with Deep Convolutional Neural Networks">Detecting Sarcasm with Deep Convolutional Neural Networks

  Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.

  https://www.kdnuggets.com/2018/06/detecting-sarcasm-deep-convolutional-neural-networks.html

• IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

  I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  https://www.kdnuggets.com/2018/06/zimbres-iot-aws-machine-learning-dashboard.html

• 5 Machine Learning Projects You Should Not Overlook, June 2018">5 Machine Learning Projects You Should Not Overlook, June 2018

  Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!

  https://www.kdnuggets.com/2018/06/5-machine-learning-projects-overlook-jun-2018.html

• Top May Stories: Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018; Data Science vs Machine Learning vs Data Analytics vs Business Analytics

  Also: Boost your data science skills. Learn linear algebra. 10 More Free Must-Read Books for Machine Learning and Data Science.

  https://www.kdnuggets.com/2018/06/top-stories-2018-may.html

• The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R?">The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R?

  We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

  https://www.kdnuggets.com/2018/06/ecosystem-data-science-python-victory.html

• Resources For Women In Data Science and Machine Learning

  A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.

  https://www.kdnuggets.com/2018/06/resources-women-data-science-machine-learning.html

• 10 More Free Must-Read Books for Machine Learning and Data Science">10 More Free Must-Read Books for Machine Learning and Data Science

  Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.

  https://www.kdnuggets.com/2018/05/10-more-free-must-read-books-for-machine-learning-and-data-science.html

• Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis">Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis

  Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.

  https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html

• How to Organize Data Labeling for Machine Learning: Approaches and Tools

  The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.

  https://www.kdnuggets.com/2018/05/data-labeling-machine-learning.html

• Top Stories, May 7-13: 2018 KDnuggets Analytics, Data Mining, Data Science, Machine Learning Software Poll; WTF is a Tensor?!?

  5 Reasons "Logistic Regression" should be the first thing you learn when becoming a Data Scientist; PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; Detecting Breast Cancer with Deep Learning; To SQL or not To SQL: that is the question!

  https://www.kdnuggets.com/2018/05/top-news-week-0507-0513.html

• The Executive Guide to Data Science and Machine Learning

  This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.

  https://www.kdnuggets.com/2018/05/executive-guide-data-science-machine-learning.html

• 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning”

  Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.

  https://www.kdnuggets.com/2018/05/7-useful-suggestions-machine-learning-yearning.html

• Top Data Science, Machine Learning Courses from Udemy – May 2018

  Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.

  https://www.kdnuggets.com/2018/05/udemy-top-data-science-machine-learning-courses.html

• 2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?">2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

  Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

  https://www.kdnuggets.com/2018/05/new-poll-software-analytics-data-mining-data-science-machine-learning.html

• 50+ Useful Machine Learning & Prediction APIs, 2018 Edition">50+ Useful Machine Learning & Prediction APIs, 2018 Edition

  Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.

  https://www.kdnuggets.com/2018/05/50-useful-machine-learning-prediction-apis-2018-edition.html

• What should be focus areas for Machine Learning / AI in 2018?

  This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.

  https://www.kdnuggets.com/2018/04/focus-areas-ml-ai-2018.html

• Top Stories, Apr 16-22: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; Python Regular Expressions Cheat Sheet

  Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning

  https://www.kdnuggets.com/2018/04/top-news-week-0416-0422.html

• 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning">7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning

  It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.

  https://www.kdnuggets.com/2018/04/7-books-mathematical-foundations-data-science.html

• Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks

  The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.

  https://www.kdnuggets.com/2018/04/robust-word2vec-models-gensim.html

• Are High Level APIs Dumbing Down Machine Learning?

  Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?

  https://www.kdnuggets.com/2018/04/high-level-apis-dumbing-down-machine-learning.html

• Don’t learn Machine Learning in 24 hours

  When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.

  https://www.kdnuggets.com/2018/04/dont-learn-machine-learning-24-hours.html

• Onboarding Your Machine Learning Program

  Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.

  https://www.kdnuggets.com/2018/04/onboarding-machine-learning.html

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

• Machine Learning for Text

  This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.

  https://www.kdnuggets.com/2018/04/machine-learning-text.html

• 5 Things You Need to Know about Reinforcement Learning

  With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.

  https://www.kdnuggets.com/2018/03/5-things-reinforcement-learning.html

• Exploring DeepFakes">Exploring DeepFakes

  In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.

  https://www.kdnuggets.com/2018/03/exploring-deepfakes.html

• Multiscale Methods and Machine Learning

  We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.

  https://www.kdnuggets.com/2018/03/multiscale-methods-machine-learning.html

• Will GDPR Make Machine Learning Illegal?">Will GDPR Make Machine Learning Illegal?

  Does GDPR require Machine Learning algorithms to explain their output? Probably not, but experts disagree and there is enough ambiguity to keep lawyers busy.

  https://www.kdnuggets.com/2018/03/gdpr-machine-learning-illegal.html

• How to do Machine Learning Efficiently

  I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this.

  https://www.kdnuggets.com/2018/03/machine-learning-efficiently.html

• 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning">18 Inspiring Women In AI, Big Data, Data Science, Machine Learning

  For the 2018 international women's day, we profile 18 inspiring women who lead the field in AI, Analytics, Big Data , Data science, and Machine Learning areas.

  https://www.kdnuggets.com/2018/03/inspiring-women-ai-big-data-science.html

• Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms">Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms

  We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.

  https://www.kdnuggets.com/2018/02/gartner-2018-mq-data-science-machine-learning-changes.html

• Top 20 Python AI and Machine Learning Open Source Projects">Top 20 Python AI and Machine Learning Open Source Projects

  We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.

  https://www.kdnuggets.com/2018/02/top-20-python-ai-machine-learning-open-source-projects.html

• Cartoon: Machine Learning Problems in 2118

  For Valentine's day, new KDnuggets cartoon looks at some problems Machine Learning can face in 2118.

  https://www.kdnuggets.com/2018/02/cartoon-valentine-machine-learning.html

• A Basic Recipe for Machine Learning">A Basic Recipe for Machine Learning

  One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.

  https://www.kdnuggets.com/2018/02/basic-recipe-machine-learning.html

• Which Machine Learning Algorithm be used in year 2118?

  So what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.

  https://www.kdnuggets.com/2018/02/machine-learning-algorithm-2118.html

• Deep Feature Synthesis: How Automated Feature Engineering Works

  Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.

  https://www.kdnuggets.com/2018/02/deep-feature-synthesis-automated-feature-engineering.html

• 5 Fantastic Practical Machine Learning Resources">5 Fantastic Practical Machine Learning Resources

  This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.

  https://www.kdnuggets.com/2018/02/5-fantastic-practical-machine-learning-resources.html

• The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

• Automated Text Classification Using Machine Learning

  In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.

  https://www.kdnuggets.com/2018/01/automated-text-classification-machine-learning.html

• Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI">Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI

  A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.

  https://www.kdnuggets.com/2018/01/mlaas-amazon-microsoft-azure-google-cloud-ai.html

• Is Learning Rate Useful in Artificial Neural Networks?

  This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.

  https://www.kdnuggets.com/2018/01/learning-rate-useful-neural-network.html

• Top 10 TED Talks for Data Scientists and Machine Learning Engineers">Top 10 TED Talks for Data Scientists and Machine Learning Engineers

  A comprehensive and diverse compilation of TED talks to understand the big picture of AI and Machine Learning.

  https://www.kdnuggets.com/2018/01/top-10-ted-talks-data-scientists-machine-learning.html

• The Art of Learning Data Science">The Art of Learning Data Science

  A beginner’s account of getting into comfort zone of learning Data Science.

  https://www.kdnuggets.com/2018/01/art-learning-data-science.html

• Top Stories of 2017: 10 Free Must-Read Books for Machine Learning and Data Science; Python overtakes R, becomes the leader in Data Science, Machine Learning platforms

  Also Top 10 Machine Learning Algorithms for Beginners; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets.

  https://www.kdnuggets.com/2017/12/top-stories-2017.html

• How to Improve Machine Learning Algorithms? Lessons from Andrew Ng, part 2

  The second chapter of ML lessons from Ng’s experience. This one will only be talking about Human Level Performance & Avoidable Bias.

  https://www.kdnuggets.com/2017/12/improve-machine-learning-algorithm-lessons-andrew-ng-part2.html

• Getting Started with TensorFlow: A Machine Learning Tutorial

  A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.

  https://www.kdnuggets.com/2017/12/getting-started-tensorflow.html

• Building an Audio Classifier using Deep Neural Networks

  Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.

  https://www.kdnuggets.com/2017/12/audio-classifier-deep-neural-networks.html

• Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018">Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/machine-learning-ai-main-developments-2017-key-trends-2018.html

• How to Improve Machine Learning Performance? Lessons from Andrew Ng

  5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.

  https://www.kdnuggets.com/2017/12/improve-machine-learning-performance-lessons-andrew-ng.html

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  https://www.kdnuggets.com/2017/12/data-science-machine-learning-main-developments-trends.html

• When reinforcement learning should not be used?

  While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.

  https://www.kdnuggets.com/2017/12/when-reinforcement-learning-not-used.html

• Exclusive: Interview with Rich Sutton, the Father of Reinforcement Learning

  My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.

  https://www.kdnuggets.com/2017/12/interview-rich-sutton-reinforcement-learning.html

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html