- Google Unveils TAPAS, a BERT-Based Neural Network for Querying Tables Using Natural Language - May 19, 2020.
The new neural network extends BERT to interact with tabular datasets.
- Google Open Sources SimCLR, A Framework for Self-Supervised and Semi-Supervised Image Training - Apr 27, 2020.
The new framework uses contrastive learning to improve image analysis in unlabeled datasets.
- The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks - Apr 24, 2020.
Check out this repository of more than 100 freely-accessible NLP notebooks, curated from around the internet, and ready to launch in Colab with a single click.
- TensorFlow Dev Summit 2020: Top 10 Tricks for TensorFlow and Google Colab Users - Apr 8, 2020.
In this piece, we’ll highlight some of the tips and tricks mentioned during this year’s TF summit. Specifically, these tips will help you in getting the best out of Google’s Colab.
- Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models - Mar 23, 2020.
TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures.
- Google Open Sources TFCO to Help Build Fair Machine Learning Models - Mar 12, 2020.
A new optimization framework helps to incorporate fairness constraints in machine learning models.
- 5 Google Colaboratory Tips - Mar 2, 2020.
Are you looking for some tips for using Google Colab for your projects? This article presents five you may find useful.
- Francois Chollet on the Future of Keras and Reinforce Conference - Feb 25, 2020.
Ahead of Reinforce Conference in Budapest, we asked Francois Chollet, the creator of Keras, about Keras future, proposed developments, PyTorch, energy efficiency, and more. Listen to him in person in Budapest, April 6-7, and use code KDNuggets to save 15% on conference tickets.
- Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
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.
- Prepare for a Long Battle against Deepfakes - Feb 21, 2020.
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.
- Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything - Feb 17, 2020.
Meena is one of the major milestones in the history of NLU. How did Google build it?
- Google Dataset Search Provides Access to 25 Million Datasets - Jan 29, 2020.
Google's dataset search is out of beta, and provides centralized access to 25 million datasets.
- KDnuggets™ News 19:n49, Dec 27: What is a Data Scientist Worth? New Explainable AI from Google - Dec 27, 2019.
What is a Data Scientist Worth?; Google's New Explainable AI Service; The Most In Demand Tech Skills for Data Scientists; The 4 fastest ways NOT to get hired as a data scientist; and KDnuggets Cartoon which was included in a surprising textbook.
- Google’s New Explainable AI Service - Dec 20, 2019.
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.
- Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models - Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
- Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
- Can Neural Networks Develop Attention? Google Thinks they Can - Nov 25, 2019.
Google recently published some work about modeling attention mechanisms in deep neural networks.
- Python, Selenium & Google for Geocoding Automation: Free and Paid - Nov 21, 2019.
This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API.
- How to Extract Google Maps Coordinates - Nov 11, 2019.
In this article, I will show you how to quickly extract Google Maps coordinates with a simple and easy method.
- About Google’s Self-Proclaimed Quantum Supremacy and its Impact on Artificial Intelligence - Oct 29, 2019.
Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI.
- Understanding Tensor Processing Units - Jul 30, 2019.
The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.
- This New Google Technique Help Us Understand How Neural Networks are Thinking - Jul 24, 2019.
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
- Practical Speech Recognition with Python: The Basics - Jul 9, 2019.
Do you fear implementing speech recognition in your Python apps? Read this tutorial for a simple approach to getting practical with speech recognition using open source Python libraries.
- How Google uses Reinforcement Learning to Train AI Agents in the Most Popular Sport in the World - Jun 21, 2019.
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.
- Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications - Apr 10, 2019.
We discuss industry trends in Artificial Intelligence with Vijay Ramakrishnan, a machine learning engineer and expert in conversational applications.
- 7 “Gotchas” for Data Engineers New to Google BigQuery - Mar 28, 2019.
Here are some things that might take some getting used to when new to Google BigQuery, along with mitigation strategies where I’ve found them.
- Network with Google, Intel, Facebook, LinkedIn & more - Mar 27, 2019.
The Predictive Analytics Innovation Summit takes place Apr 29 & 30 in San Diego. Secure your place at this must-attend event for data professionals today and deep-dive into a new era of AI and data strategy.
- Top KDnuggets tweets, Mar 06-12: Most impactful AI trends of 2018; Google open-sources GPipe for efficiently training large deep neural networks - Mar 13, 2019.
The rise of ML Engineering; Build your own Robust #DeepLearning Environment in Minutes; Another 10 Free Must-Read Books for Machine Learning and Data Science; Top 5 #MachineLearning Courses for 2019 - from @Coursera and @EdX.
- Reflections on the State of AI: 2018 - Feb 26, 2019.
We provide a detailed overview of the key developments in the AI space, focusing on key players, applications, opportunities, and challenges.
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
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.
- Data Science in the Real World – Meet Netflix, Google and Amazon at DATAx Singapore - Jan 30, 2019.
Machine learning, predictive analytics, IoT, smart cities and fintech are some of the hot topics where you need to know the latest. Get a sneak peak of upcoming DATAx with the DATAx New York festival post event report.
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
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.
- 3 More Google Colab Environment Management Tips - Jan 2, 2019.
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.
- Keras Hyperparameter Tuning in Google Colab Using Hyperas - Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
- KDnuggets™ News 18:n46, Dec 5: AI, Data Science, Analytics 2018 Main Developments, 2019 Key Trends; Deep Learning Cheat Sheets - Dec 5, 2018.
Also: Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools; How to Build a Machine Learning Team When You Are Not Google or Facebook; A Complete Guide to Choosing the Best Machine Learning Course; Handling Imbalanced Datasets in Deep Learning
- How to Build a Machine Learning Team When You Are Not Google or Facebook - Nov 28, 2018.
If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- Join AI experts from Google Brain, Open AI & Uber AI Labs in San Francisco - Nov 1, 2018.
Join us at the Deep Learning Summit, San Francisco, 24 - 25 Jan 2019. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP, and explore how deep learning will impact all industries.
- Join Microsoft, Google and Bilfinger at Deep Learning World – Berlin, 12 November - Sep 17, 2018.
Deep Learning World is coming to Berlin, Germany, Nov 12. Early bird rates until Sep 28. Register now!
- Deploying scikit-learn Models at Scale - Aug 29, 2018.
Find out how to serve your scikit-learn model in an auto-scaling, serverless environment! Today, we’ll take a trained scikit-learn model and deploy it on Cloud ML Engine.
- 9 Things You Should Know About TensorFlow - Aug 22, 2018.
A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.
- How to Set Up a Free Data Science Environment on Google Cloud - Aug 15, 2018.
In this post, we'll walk through how to set up a data science environment on Google Cloud Platform (GCP). Because of the economy of scale that cloud hosting companies provide, individuals or teams can affordably access powerful computers.
- Google’s AutoML: Cutting Through the Hype - Jul 31, 2018.
In today’s post, I want to look specifically at Google’s AutoML, a product which has received a lot of media attention, and address "What is Google's AutoML?" and more.
- Ready your Skills for a Cloud-First World with Google - Jul 20, 2018.
The Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera will help you jumpstart your career, includes hands-on labs, and takes you from a strategic overview to practical skills in building real-world, accurate ML models.
- How Not to Regulate the Data Economy - May 24, 2018.
The GDPR will affect not just tech companies but any company that handles customer data — in other words, every company. And it will affect the use of data throughout the world, not just in Europe...
- Hear the latest AI advancements in robotics & automation from Uber, Hitachi, Google & more - Apr 26, 2018.
The Summits will bring together 550 experts and 60 speakers using AI and deep learning to improve operations in manufacturing, and creating the next generation of intelligent robots. Save 20% with code KDNUGGETS.
- Creating a simple text classifier using Google CoLaboratory - Mar 15, 2018.
Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.
- Get ready for smart apps - Mar 8, 2018.
Mobile platforms are set to benefit from Deep Learning this year, with significant improvements in privacy, offline functionality and much more. But which Android phone should you purchase to maximise these benefits?
- Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch - Feb 20, 2018.
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
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- 3 Essential Google Colaboratory Tips & Tricks - Feb 12, 2018.
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
- Fast.ai Lesson 1 on Google Colab (Free GPU) - Feb 8, 2018.
In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?
- Top KDnuggets tweets, Jan 24-30: Top 10 Algorithms for Machine Learning Newbies; Want to Become a Data Scientist? Try Feynman Technique - Jan 31, 2018.
Also: Chronological List of AI Books To Read - from Goedel, Escher, Bach ... ; Aspiring Data Scientists! Start to learn Statistics with these 6 books.
- Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI - Jan 22, 2018.
A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.
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- View from Google Assistant: Are we becoming reliant on AI? - Dec 26, 2017.
AI is powering a paradigm shift in human machine interaction and conversational UIs like Alexa, Cortana, Google Assistant, and Siri, have the potential to break free from some key limitations of mobile app.
- AlphaGo Zero: The Most Significant Research Advance in AI - Oct 27, 2017.
The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI.
- Machine Learning Translation and the Google Translate Algorithm - Sep 14, 2017.
Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.
- What data has to teach us about deep learning? - Sep 4, 2017.
Budapest is calling Data Scientists and Data engineers to CRUNCH Conference, Oct 18-20. CRUNCH will feature talks from Google, Airbnb, Tesla, LinkedIn, Netflix, Uber, and more. Use code KDnuggetsAtCrunch to save.
- DataScience: Elevate Live Stream, July 27 - Jul 13, 2017.
Register now for the live stream of DataScience: Elevate, a half-day event featuring data science leaders from Google, Netflix, eHarmony, and other global companies.
- Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN - Jun 9, 2017.
Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
- Top 10 Recent AI videos on YouTube - May 10, 2017.
Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.
- Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- Deep Learning – Past, Present, and Future - May 2, 2017.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
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- Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!? - Apr 4, 2017.
A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research
- Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 2 - Mar 29, 2017.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
- What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
Check this out: A topic wise collection of 100+ data science interview questions from top companies.
- Proxy Indicators: beware of spurious claims - Mar 16, 2017.
Beware of online and market research studies which can lead to false or spurious claims. We examine several notable examples including Google Street View and Argentina inflation.
- Google Got a Lot of Data About You - Mar 9, 2017.
This article will dive into six types of data that most big tech companies, and especially Google, gather about consumers.
- Top KDnuggets tweets, Mar 01-07: Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image - Mar 8, 2017.
Also Deep Forest: Towards An Alternative to Deep #NeuralNetworks; An Overview of #Python #DeepLearning Frameworks; The Gentlest Introduction to Tensorflow - Part 2.
- Top /r/MachineLearning Posts, February: Oxford Deep NLP Course; Data Visualization for Scikit-learn Results - Mar 6, 2017.
Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release
- New Poll: Do you support Trump Immigration Ban? - Feb 9, 2017.
Express your opinion about Trump Immigration ban and find out what does Google Autocomplete offers when you search for "Trump Immigration".
- Going to War with the Giants: Automated Machine Learning with MLJAR - Jan 19, 2017.
The performance of automated machine learning tool MLJAR on Kaggle competition data is presented in comparison with those from other predictive APIs from Amazon, Google, PredicSis and BigML.
- A Concise Overview of Recent Advances in Vehicle Technologies - Jan 11, 2017.
2016 was a big year for electric and driverless cars. Get a quick review with relevant videos on some of the events of interest in the field during the past year.
- Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment - Jan 11, 2017.
Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment; Huggable Image Classifier; xkcd: Linear Regression; AlphaGO WINS!; TensorFlow Fizzbuzz
- Revenue per Employee: golden ratio, or red herring? - Jan 4, 2017.
There is growing support for revenue per employee as one of the most underrated metrics available for assessing business performance in a crowded marketplace.
- Forecast Your Future With Analytics Insights From Google, Bloomberg & others - Dec 13, 2016.
What are the top AI & Machine Learning trends for 2017? Join the Predictive Analytics Innovation Summit in San Diego on Feb 22 & 23, 2017, to find out everything you need to know about Real-time Machine Learning algorithms, developing strong data-driven cultures and more!
- Top /r/MachineLearning Posts, November: StarCraft II for AI Research; Google AI Experiments Website; Google in Montreal - Dec 5, 2016.
DeepMind and Blizzard to release StarCraft II as an AI research environment; Google AI Experiments Website; Google opens new Montreal-based AI research lab; Lip Reading Sentences in the Wild; Clean implementations of machine learning algorithms
- The Foundations of Algorithmic Bias - Nov 16, 2016.
We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.
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- Up to Speed on Deep Learning: August Update, Part 2 - Sep 23, 2016.
This is the second part of an overview of deep learning stories that made news in August. Look to see if you have missed anything.
- Top /r/MachineLearning Posts, August: Google Brain AMA, Image Completion with TensorFlow, Japanese Cucumber Farming - Sep 5, 2016.
Google Brain AMA; Image Completion with Deep Learning in TensorFlow; Japanese Cucumber Farming; Andrew Ng's machine learning class in Python; Google Brain datasets for robotics research
- Up to Speed on Deep Learning: July Update - Aug 29, 2016.
Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.
- Top /r/MachineLearning Posts, July: Google Brain AMA, Geoff Hinton Awarded IEEE Medal, Hinton ANN Course Lives! - Aug 2, 2016.
Google Brain AMA; Geoff Hinton Awarded IEEE Medal; Geoff Hinton's ANN Course Lives; Google’s DeepMind Reduces Data Center Cooling Bill; Training an artificial neural network to play Diablo 2
- Data Science of Visiting Famous Movie Locations in San Francisco - Jul 30, 2016.
Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.
- 5 Big Data Projects You Can No Longer Overlook - Jul 21, 2016.
Check out 5 Big Data projects that you are not likely to have seen before, but which may be useful to you, and perhaps even scratch an itch you didn't know you had.
- Why Big Data is in Trouble: They Forgot About Applied Statistics - Jul 18, 2016.
This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago.
- Top KDnuggets tweets, Jun 22-28: #Bayesian #Statistics explained in Simple English; Brexit - Jun 29, 2016.
#Bayesian #Statistics explained to Beginners in Simple English; Amazing analysis of #Brexit with #MachineLearning - it is sad; 18 Useful Mobile Apps for #DataScientist; Sharp divisions between England, #Scotland in #Brexit vote suggest future UK split.
- What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data - Jun 20, 2016.
The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.
- Top /r/MachineLearning Posts, May: TensorFlow Tricks; Machine Learning Tutorials; Google TPUs - Jun 1, 2016.
May on /r/MachineLearning was all about tutorials, TensorFlow, Google hardware, Deep Learning machine installations, and some laughs.
- Top KDnuggets tweets, May 18-24: Google supercharges #MachineLearning, #DeepLearning tasks with TPU (Tensor Processing Unit) - May 25, 2016.
Stanford Crowd Course Initiative: #MachineLearning with #Python course; Practical Guide to Matrix Calculus for #DeepLearning; Build your own #DeepLearning Box < $1.5K
- The Good, Bad & Ugly of TensorFlow - May 24, 2016.
A survey of six months of rapid evolution (+ tips/hacks and code to fix the ugly stuff) using TensorFlow. Get some great advice from the trenches.
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- Stanford Webinar: The Secret to a Perfect Search, May 17 - May 5, 2016.
What makes “googling” the best way to search for something on the Internet? Join Rajan Patel, Stanford Instructor and Google Engineer, as he shares insights into the manipulation of large complex data sets and how you can turn them into valuable, actionable information for your company.
- Data is the New Everything - Mar 17, 2016.
Data gets a lot of mainstream attention these days, and has been compared to all sorts of different things. This is a lighthearted look at some of the top suggestions from Google autocomplete when searching for the phrase "data is the new" something.
- The Rise Of The Robot - Mar 3, 2016.
Atlas, the latest robot from Google's Boston Dynamics a pretty resilient chap. He can trudge through uneven snow, be knocked off his feet and get up again. and do work that can take place in any warehouse. We examine what it means for our future.
- Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement - Mar 2, 2016.
In February on /r/MachineLearning, we get a run-down of the AlphaGo matches, Distributed TensorFlow is released, convolutional neural nets are cleaning Star Wars images, vintage science is on parade, military machine learning is criticized, and the overwhelmed researcher is given advice.
- Distributed TensorFlow Has Arrived - Mar 1, 2016.
Google has open sourced its distributed version of TensorFlow. Get the info on it here, and catch up on some other TensorFlow news at the same time.
- Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn - Feb 12, 2016.
Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?
- AI Supercomputers: Microsoft Oxford, IBM Watson, Google DeepMind, Baidu Minwa - Feb 1, 2016.
In the world of AI, this is the equivalent of the US and USSR competing to put their guy on the moon first. Here is a profile of some of the giants locked into the AI space race.
- Google’s Great Gains in the Grand Game of Go - Feb 1, 2016.
The game of Go has long stumped AI researchers, and, as such, solving it was thought to be years off. That is, until Google solved it earlier this week. Or did it?
- Top /r/MachineLearning Posts, January: Google Masters Go, Deep Learning Laughs, OpenAI AMA - Feb 1, 2016.
In January on /r/MachineLearning: Go gets mastered, deep learning laughs, an OpenAI team AMA, convolutional neural nets colorize black and white photos, and the AI community loses a leader.
- Google Launches Deep Learning with TensorFlow MOOC - Jan 26, 2016.
Google and Udacity have partnered for a new self-paced course on deep learning and TensorFlow, starting immediately.
- Public Knowledge Graph – small guys unite - Jan 21, 2016.
Currently, only global corporations like Google or Facebook can maintain a vast knowledge graph about the world. Little companies which rely on knowing world context need to unite to create a Public Knowledge Graph, or they will fall further behind the big guys.
- Top /r/MachineLearning Posts, December: The Secret Sauce, OpenAI, Google vs. Facebook - Jan 4, 2016.
December on /r/MachineLearning: Is TensorFlow Google's "secret sauce?", AI leaders unite, an extensive curated list of machine learning resources grows, Google vs. Facebook, and Deep Q Pong.
- TensorFlow is Terrific – A Sober Take on Deep Learning Acceleration - Dec 30, 2015.
TensorFlow does not change the world. But it appears to be the best, most convenient deep learning library out there.
- Update: Google TensorFlow Deep Learning Is Improving - Dec 17, 2015.
The recent open sourcing of Google's TensorFlow was a significant event for machine learning. While the original release was lacking in some ways, development continues and improvements are already being made.
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- Top /r/DataScience Posts, November: Open source Plot.ly, Pokemon (?), Social analysis with R - Dec 3, 2015.
November on /r/DataScience: Plot.ly is open sourced, Pokemon and Big Data games, a new social network analysis package for R, insider information on landing a Google Data Scientist job, and a free data science curriculum.
- Top /r/MachineLearning Posts, November: TensorFlow, Deep Convolutional Generative Adversarial Networks, and lolz - Dec 2, 2015.
In November on /r/MachineLearning, we've got a good laugh, a fantastic image-generating convolutional generative adversarial network, and a whole lot of Google TensorFlow.
- TensorFlow Disappoints – Google Deep Learning falls shallow - Nov 16, 2015.
Google recently open-sourced its TensorFlow machine learning library, which aims to bring large-scale, distributed machine learning and deep learning to everyone. But does it deliver?
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- 5 Best Machine Learning APIs for Data Science - Nov 5, 2015.
Machine Learning APIs make it easy for developers to develop predictive applications. Here we review 5 important Machine Learning APIs: IBM Watson, Microsoft Azure Machine Learning, Google Prediction API, Amazon Machine Learning API, and BigML.
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- Top /r/MachineLearning Posts, October: Machine learning video course, neural nets evaluate selfies - Nov 2, 2015.
Machine learning video lectures, deep nets evaluate selfies, Google focusing on machine learning, DeepMind's huge text dataset made available, implement a recurrent neural net, and open source face recognition with Google's FaceNet.
- An Inside View of Language Technologies at Google - Oct 29, 2015.
Learn about language technologies at Google, including projects, technologies, and philosophy, from an interview with a Googler.
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- MLconf Industry Impact Student Research Award Sponsored by Google - Sep 23, 2015.
The award is for current PhD students or recent graduates whose research is deemed to be useful for solving the problems currently faced in industry. Nominations due Oct 25, 2015.
- Top /r/MachineLearning Posts, August: Deep Learning paints in style of many famous painters - Sep 7, 2015.
Deep Learning algorithm generating paintings in the styles of famous artists, Genetic algorithms pioneer John Holland passes away, Beginner Python data analysis tutorial, LSTM networks explained, and Google Thought Vectors.
- Top /r/MachineLearning Posts, July: Visual Intro to Machine Learning, Google new patent controversy, Deep Learning and famous art - Aug 20, 2015.
A Visual Introduction to Machine Learning, Why Google's new patent applications are alarming, Art with Google's Inceptionism code, Google Photo's algorithm gone wrong and a Neural network tutorial made it to the top this month!
- From Big Data to Big Profits: A Lesson from Google’s Nest - Jul 24, 2015.
Google Nest is a very interesting example of how such a seemingly simple item as thermostat, with the addition to Big Data can transform an industry and lead to significant profits.
- Top 10 Machine Learning Videos on YouTube - Jun 23, 2015.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
- Cognitive Computing: Solving the Big Data Problem? - Jun 12, 2015.
With a shortage of data scientists, what are the alternatives for making sense of Big Data? We examine Cognitive Computing, its strengths, and how it can fit into the current Big Data landscape.
- May 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity - Jun 10, 2015.
May 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: Elon Musk, Google buys Timeful, Lexalytics Wizard GUI, Banjo $100M, Unicorn myth and reality.
- Top KDnuggets tweets, May 26 – Jun 1: Step by Step Guide to Learn #DataScience on R - Jun 5, 2015.
Creator of @ApacheSpark @Matei_Zaharia wins @ACM Doctoral Dissertation Award; LeaRning Path: Step by Step Guide to Learn #DataScience with R; Top 20 #Python #MachineLearning #OpenSource Projects; R vs #Python for #DataScience.
- Top KDnuggets tweets, May 12-18: Hadoop demand falls; Andrew Ng Machine Learning class, excellent course notes - May 19, 2015.
#Hadoop demand falls, 54% of enterprises have no plans for it; Download your entire #Google search history; #DataScience for #Dummies: An interview with author Lilian @BigDataGal Pierson; Andrew Ng Machine Learning Coursera class - complete, excellent course notes.
- Most Viewed Data Mining Videos on YouTube - May 18, 2015.
The top Data Mining YouTube videos by those like Google and Revolution Analytics covers topics ranging from statistics in data mining to using R for data mining to data mining in sports.
- CRN Big Data Business Analytics Companies - May 12, 2015.
Data, Analytics, and Intelligence play a key role in CRN Big Data 100 in 2015. New additions include predictive analytics firms Knime, Logi Analytics, Looker, Luminoso, Predixion, RapidMiner, and Salesforce.
- Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis - May 12, 2015.
Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place.
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- Most Viewed Big Data Videos on YouTube - May 9, 2015.
The top Big Data YouTube videos by those like Hortonworks and Kirk D. Borne cover diverse topics including Hadoop, Big Data Trends, Deep Learning, and Big Data Leadership.
- Blockspring: Out-run programmers with your spreadsheet - Apr 4, 2015.
Blockspring for Google Sheets lets you run over 1000 functions from your spreadsheets - create interactive data visualizations, run algorithms, pull data sources, execute db queries, automate tweets and emails, make API calls, and more.
- Top KDnuggets tweets, Mar 19-22: Tensor methods for Machine Learning; Tibco survey: Big Data top use cases - Mar 23, 2015.
Tensor methods for #MachineLearning: fast, accurate, scalable, need open-source libs; #DataScience and Reproducibility: Explaining when the experiment does not work; Google #DeepLearning FaceNet is the best ever for recognizing faces; Tibco survey #BigData top use cases: Customer & Experience Analytics, Risk/Threat.
- RE.WORK Deep Learning Summit, San Francisco, January – videos, presentations - Feb 26, 2015.
Interesting videos and presentations from January Deep Learning Summit include a Fireside Chat with Andrew Ng and Derrick Harris, presentations by Richard Socher (Founder, MetaMind), Quoc Le (Research Scientist, Google), Greg Corrado (Sr Research Scientist at Google), and more.
- Google BigQuery Public Datasets - Feb 20, 2015.
Google BigQuery is not only a fantastic tool to analyze data, but it also has a repository of public data, including GDELT world events database, NYC Taxi rides, GitHub archive, Reddit top posts, and more.
- Big Data, Privacy, and Security – which side are you on? - Feb 18, 2015.
After all the positive promise, the hype, and predictions about Big Data, 2015 started with a debate about privacy and specifically whether or not companies like Google and Facebook should be allowed to encrypt their users data.
- Interview: M.C. Srivas, MapR on Demystifying the Art of Processing Massive Data - Feb 11, 2015.
We discuss the launch and evolution of MapR, achievements, key characteristics of MapR-DB, significance of Apache Drill, MapR use cases and more.
- How Big Data Pieces, Technology, and Animals fit together - Feb 5, 2015.
How Big Data Pieces and animals fit together: MapReduce, HDFS, Apache Spark,, Pregel, Zookeeper, Flume, Hive, Pig, and more, explained by a Quora (and past Facebook) Data Scientist.
- BigML machine learning platform Winter 2015 Release, Feb 11 - Feb 2, 2015.
See the latest in BigML's continuously evolved machine learning platform with its emphasis on consumability, programmability, and scalability. Feb 11 webinar at 9 am PT and 5 pm PT.