Why Big Data is in Trouble: They Forgot About Applied Statistics
Is Data Science Ripe for a Massive Merger & Acquisition / Consolidation?
A Guide to Deep Learning by YN2
Predictions for Deep Learning in 2017
Top 30 Data Science Articles of the Year
28 Jupyter Notebook tips, tricks and shortcuts
Practical Deep Learning For Coders, Part 1
Saturday, December 31, 2016
Sunday, December 18, 2016
Recent Big Data and Machine Learning Info Digest 2016/12/18
The major advancements in Deep Learning in 2016
Three Major Benefits of Big Data
Comprehensive learning path – Data Science in Python
Apache Spark: A Unified Engine for Big Data Processing
Why Deep Learning is Radically Different from Machine Learning
The Most Boring/Valuable Data Science Advice
Three Major Benefits of Big Data
Comprehensive learning path – Data Science in Python
Apache Spark: A Unified Engine for Big Data Processing
Why Deep Learning is Radically Different from Machine Learning
The Most Boring/Valuable Data Science Advice
Labels:
2016,
Big Data,
Data Science,
Deep Learning,
Machine Learning,
Python,
Spark
Saturday, December 10, 2016
Recent Big Data and Machine Learning Info Digest 2016/12/10
Deep Learning Summer School, Montreal 2016
iSee: Using deep learning to remove eyeglasses from faces
Apache Spark and Amazon S3 — Gotchas and best practices
A Day in the Life of a Data Engineer
Pandas Tutorial: Data analysis with Python: Part 2
This AI Boom Will Also Bust
Big Data Extraction Tools For Good Decision-making
Hortonworks: MapR Shows a Better Way, Says Cowen
iSee: Using deep learning to remove eyeglasses from faces
Apache Spark and Amazon S3 — Gotchas and best practices
A Day in the Life of a Data Engineer
Pandas Tutorial: Data analysis with Python: Part 2
This AI Boom Will Also Bust
Big Data Extraction Tools For Good Decision-making
Hortonworks: MapR Shows a Better Way, Says Cowen
Labels:
AI,
Amazon,
Barrons,
Big Data,
Data Engineering,
Deep Learning,
eyeglasses,
Hortonworks,
MapR,
Montreal,
Pandas,
Python,
Spark
Sunday, December 4, 2016
Recent Big Data and Machine Learning Info Digest 2016/12/04
The Top 7 Big Data Trends for 2017
Why Self-Service Prep Is a Killer App for Big Data
First 12 chapters of the Machine Learning Yearning book draft from Andrew Ng
An Interactive Tutorial on Numerical Optimization
Probabilistic Programming
Computers could soon be our best developers
Why Self-Service Prep Is a Killer App for Big Data
First 12 chapters of the Machine Learning Yearning book draft from Andrew Ng
An Interactive Tutorial on Numerical Optimization
Probabilistic Programming
Computers could soon be our best developers
Labels:
AI,
Andrew Ng,
Ben Frederickson,
Big Data,
Cornell,
Machine Learning,
Trend
Saturday, November 26, 2016
Recent Big Data and Machine Learning Info Digest 2016/11/26
40 Ways Deep Learning is Eating the World
Do You Really Have Big Data, Or Just Too Much Data?
Hadoop Integration Benchmark (report)
Big Data Programming Languages: What Are The Differences Between Python, R, and Julia?
The Simple Economics of Machine Intelligence
How Predictive AI Will Change Shopping
Key Facebook Engineer Departs To Start Deep Learning Hardware Company
Do You Really Have Big Data, Or Just Too Much Data?
Hadoop Integration Benchmark (report)
Big Data Programming Languages: What Are The Differences Between Python, R, and Julia?
The Simple Economics of Machine Intelligence
How Predictive AI Will Change Shopping
Key Facebook Engineer Departs To Start Deep Learning Hardware Company
Labels:
AI,
Benchmark,
Big Data,
Deep Learning,
Facebook,
Hadoop,
Hardware,
Julia,
Language,
Machine Learning,
Python,
R,
Shopping
Sunday, November 13, 2016
Recent Big Data and Machine Learning Info Digest after US Election 2016/11/13
Statistics and pollsters blowing the election forecast
Donald Trump's mind readers try to win him voters
Trump, Failure of Prediction, and Lessons for Data Scientists
Why FiveThirtyEight Gave Trump A Better Chance Than Almost Anyone Else
What Just Happened? by Sam Wang
Donald Trump's mind readers try to win him voters
Trump, Failure of Prediction, and Lessons for Data Scientists
Why FiveThirtyEight Gave Trump A Better Chance Than Almost Anyone Else
What Just Happened? by Sam Wang
Labels:
Big Data,
Data Scientist,
Election2016,
Nate Silver,
Sam Wang,
Trump
Sunday, November 6, 2016
Recent Big Data and Machine Learning Info Digest about US Election 2016/11/06
Has a computer or program predicted the result of the US Presidential elections 2016?
An artificial intelligence system that correctly predicted the last 3 elections says Trump will win
A professor who has correctly predicted elections for decades says Trump will win
Who will win the presidency? by FiveThirtyEight
Princeton Election Consortium by Sam Wang
Predicting US 2016 Presidential Election
Queensland Professor claims algorithm will accurately predict US election results
An artificial intelligence system that correctly predicted the last 3 elections says Trump will win
A professor who has correctly predicted elections for decades says Trump will win
Who will win the presidency? by FiveThirtyEight
Princeton Election Consortium by Sam Wang
Predicting US 2016 Presidential Election
Queensland Professor claims algorithm will accurately predict US election results
Labels:
AI,
Election2016,
FiveThirtyEight,
Hillary,
Queensland,
Sam Wang,
Trump
Saturday, October 29, 2016
Saturday, October 22, 2016
Recent Big Data and Machine Learning Info Digest 2016/10/22
Explaining Data Science to High School Students
Big data: Why the boom is already over
Banking, manufacturing industries will fuel demand for big data products: IDC
Kaggle Ranking #1: The Data Science (and more) of Predicting Consumer Debt Default
Preparing for the Future of Artificial Intelligence (White House)
Ask HN: How to get started with machine learning?
Big data: Why the boom is already over
Banking, manufacturing industries will fuel demand for big data products: IDC
Kaggle Ranking #1: The Data Science (and more) of Predicting Consumer Debt Default
Preparing for the Future of Artificial Intelligence (White House)
Ask HN: How to get started with machine learning?
Labels:
AI,
Big Data,
Data Science,
Hacker News,
IDC,
Machine Learning,
White House,
ZDnet
Friday, October 14, 2016
Recent Big Data and Machine Learning Info Digest 2016/10/14
Deep Reinforcement Learning: Pong from Pixels
Data Mining in Python: A Guide
Top-down learning path: Machine Learning for Software Engineers
Can we open the black box of AI?
Gartner Survey Reveals Investment in Big Data Is Up but Fewer Organizations Plan to Invest
The broken promise of open-source Big Data software – and what might fix it
Data Mining in Python: A Guide
Top-down learning path: Machine Learning for Software Engineers
Can we open the black box of AI?
Gartner Survey Reveals Investment in Big Data Is Up but Fewer Organizations Plan to Invest
The broken promise of open-source Big Data software – and what might fix it
Labels:
AI,
Andrej Karpathy,
Big Data,
Data Mining,
Deep Learning,
Gartner,
Machine Learning,
Nature,
Open Source,
Python,
Tutorial
Friday, October 7, 2016
Recent Big Data and Machine Learning Info Digest 2016/10/07
Machine Learning in a Year
Staying on Top of The Game with Modern Big Data
Why Palantir is Silicon Valley’s most questionable unicorn
What is the difference between AI, Machine Learning, NLP, and Deep Learning?
Why Deep Learning Is Suddenly Changing Your Life
Staying on Top of The Game with Modern Big Data
Why Palantir is Silicon Valley’s most questionable unicorn
What is the difference between AI, Machine Learning, NLP, and Deep Learning?
Why Deep Learning Is Suddenly Changing Your Life
Labels:
AI,
Big Data,
Deep Learning,
Machine Learning,
NLP,
Palantir
Saturday, October 1, 2016
Recent Big Data and Machine Learning Info Digest 2016/10/01
What are the major bottlenecks in making deep learning systems more effective (as of 2016)?
A Neural Network for Machine Translation, at Production Scale
Amazon's new GPU-cloud wants to chew through your AI and big data projects
Sentimental Analysis of the First Presidential Debate of 2016 Using Machine Learning
A Beginner's Guide to Apache Flink – 12 Key Terms, Explained
Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
A Neural Network for Machine Translation, at Production Scale
Amazon's new GPU-cloud wants to chew through your AI and big data projects
Sentimental Analysis of the First Presidential Debate of 2016 Using Machine Learning
A Beginner's Guide to Apache Flink – 12 Key Terms, Explained
Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
Labels:
AI,
Amazon,
Big Data,
Cloud,
Deep Learning,
GPU,
Hillary,
Machine Learning,
Presidential Debate,
Quora,
TensorFlow,
Translation,
Trump,
YouTube
Friday, September 23, 2016
Recent Big Data and Machine Learning Info Digest 2016/09/23
Data Science: A Kaggle Walkthrough – Creating a Model
Deep Learning in a Nutshell: Reinforcement Learning
Why do deep neural nets require so much training data to perform well?
6 Startups Using AI for Algorithmic Trading Strategies
Ten Myths About Machine Learning
How Hillary's Campaign Is (Almost Certainly) Using Big Data
Deep Learning in a Nutshell: Reinforcement Learning
Why do deep neural nets require so much training data to perform well?
6 Startups Using AI for Algorithmic Trading Strategies
Ten Myths About Machine Learning
How Hillary's Campaign Is (Almost Certainly) Using Big Data
Labels:
AI,
Big Data,
Data Science,
Deep Learning,
Hillary,
Kaggle,
Nvidia,
Quora
Saturday, September 17, 2016
Recent Big Data and Machine Learning Info Digest 2016/09/17
Beware of the gaps in Big Data
The 10 Algorithms Machine Learning Engineers Need to Know
How do you define "data science" and "data scientist"?
What is the Difference Between Deep Learning and “Regular” Machine Learning?
What You Know About Deep Learning Is A Lie
Attention and Augmented Recurrent Neural Networks
The 10 Algorithms Machine Learning Engineers Need to Know
How do you define "data science" and "data scientist"?
What is the Difference Between Deep Learning and “Regular” Machine Learning?
What You Know About Deep Learning Is A Lie
Attention and Augmented Recurrent Neural Networks
Labels:
Algorithms,
Big Data,
Data Science,
Data Scientist,
Deep Learning,
DJ Patil,
Jason Brownlee,
Machine Learning,
RNN
Sunday, September 11, 2016
Recent Big Data and Machine Learning Info Digest 2016/09/11
A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
Deep Learning-Take machine learning to the next level (Udacity)
Big Data In Banking: How Citibank Delivers Real Business Benefits With Its Data-First Approach
A Survival Guide to a PhD
New Research — We’re In the Middle of a Data Engineering Talent Shortage
Deep Learning-Take machine learning to the next level (Udacity)
Big Data In Banking: How Citibank Delivers Real Business Benefits With Its Data-First Approach
A Survival Guide to a PhD
New Research — We’re In the Middle of a Data Engineering Talent Shortage
Labels:
Andrej Karpathy,
CNN,
Data Engineering,
Deep Learning,
PhD,
Udacity
Friday, September 2, 2016
Recent Big Data and Machine Learning Info Digest 2016/09/02
The Simple, Elegant Algorithm That Makes Google Maps Possible
Baidu open-sources Python-driven machine learning framework
How a Japanese cucumber farmer is using deep learning and TensorFlow
Kafka in Action: 7 Steps to Real-Time Streaming From RDBMS to Hadoop
Data Ebook Archive
Baidu open-sources Python-driven machine learning framework
How a Japanese cucumber farmer is using deep learning and TensorFlow
Kafka in Action: 7 Steps to Real-Time Streaming From RDBMS to Hadoop
Data Ebook Archive
Labels:
Algorithms,
Baidu,
cucumber,
Deep Learning,
Ebook,
Google,
Hadoop,
Kafka,
Map,
O'Reilly,
Open Source,
Python,
TensorFlow
Saturday, August 27, 2016
Recent Big Data and Machine Learning Info Digest 2016/08/26
Deep Learning for Everyone – and (Almost) Free
What I learned from Deep Learning Summer School 2016
Getting Started With R in RStudio Notebooks
Your Garbage Data Is A Gold Mine
Could data help solve Seattle’s transportation challenges?
DroidOL: Android malware detection based on online machine learning
What I learned from Deep Learning Summer School 2016
Getting Started With R in RStudio Notebooks
Your Garbage Data Is A Gold Mine
Could data help solve Seattle’s transportation challenges?
DroidOL: Android malware detection based on online machine learning
Labels:
Deep Learning,
DroidOL,
Fastcompany,
Machine Learning,
Malware,
R,
RStudio,
Seattle,
Transportation,
Weird Data,
Yoshua Bengio
Sunday, August 21, 2016
Big Data, Machine Learning and Rio 2016
Big Data Doesn’t Rule The Olympics
Big data set to shine at Rio 2016 Olympic Games
How Can Big Data And Analytics Help Athletes Win Olympic Gold In Rio 2016?
Changing the Game: Big Data Helps Bring Home Olympic Gold
CrimeRadar is using machine learning to predict crime in Rio
Was There a Problem With the Rio Pool?
Big data set to shine at Rio 2016 Olympic Games
How Can Big Data And Analytics Help Athletes Win Olympic Gold In Rio 2016?
Changing the Game: Big Data Helps Bring Home Olympic Gold
CrimeRadar is using machine learning to predict crime in Rio
Was There a Problem With the Rio Pool?
Labels:
Big Data,
CrimeRadar,
Machine Learning,
Olympic,
Pool,
Rio 2016,
Swimming
Saturday, August 13, 2016
Recent Big Data and Machine Learning Info Digest 2016/08/13
Uber’s case for incremental processing on Hadoop
2016 Outlook On Artificial Intelligence In The Enterprise
AI’s Language Problem
Using Machine Learning to Predict Out-Of-Sample Performance of Trading Algorithms
Ensuring Data Security In A Cloud Based Knowledge Base
Why Natural Language Processing Will Change Everything
2016 Outlook On Artificial Intelligence In The Enterprise
AI’s Language Problem
Using Machine Learning to Predict Out-Of-Sample Performance of Trading Algorithms
Ensuring Data Security In A Cloud Based Knowledge Base
Why Natural Language Processing Will Change Everything
Labels:
AI,
Algorithms,
Hadoop,
Machine Learning,
NLP,
Security,
Uber
Friday, July 29, 2016
Recent Big Data and Machine Learning Info Digest 2016/07/29
Big Data Processing with Apache Spark - Part 4: Spark Machine Learning
Top Programming Languages Trends: The Rise of Big Data
Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
POKEMON: VISUALIZE 'EM ALL!
Machine Learning Problem Bible (MLPB)
What factors can increase your data science salary?
Top Programming Languages Trends: The Rise of Big Data
Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
POKEMON: VISUALIZE 'EM ALL!
Machine Learning Problem Bible (MLPB)
What factors can increase your data science salary?
Labels:
Bible,
Data Scientist,
Deep Learning,
Face Recognition,
Go,
Machine Learning,
Pokemon,
R,
Salary,
Spark
Saturday, July 23, 2016
Recent Big Data and Machine Learning Info Digest 2016/07/23
Google used DeepMind AI to cut its power bill
How Google is Remaking Itself as a "Machine Learning First" Company
How Big Data Is Helping the NYPD Solve Crimes Faster
Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur
Creating a Beer Recommendation Engine
How Google is Remaking Itself as a "Machine Learning First" Company
How Big Data Is Helping the NYPD Solve Crimes Faster
Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur
Creating a Beer Recommendation Engine
Labels:
Beer,
DeepMind,
Google,
Kaggle,
Machine Learning,
NYPD,
Recommendation,
Steven Levy
Sunday, July 17, 2016
Recent Big Data and Machine Learning Info Digest 2016/07/17
How do I learn machine learning?
Residual neural networks are an exciting area of deep learning research
The Many Applications of Deep Learning
Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter
Data Storytelling: Separating Fiction From Facts
The Big Data Ecosystem is Too Damn Big
Residual neural networks are an exciting area of deep learning research
The Many Applications of Deep Learning
Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter
Data Storytelling: Separating Fiction From Facts
The Big Data Ecosystem is Too Damn Big
Labels:
AI,
Big Data,
Deep Learning,
Facebook,
Machine Learning,
ResNets,
storytelling,
Yann LeCun
Saturday, July 9, 2016
Recent Big Data and Machine Learning Info Digest 2016/07/09
Exclusive: Why Microsoft is betting its future on AI
Building a data science portfolio: Machine learning project
In-depth introduction to machine learning in 15 hours of expert videos
5 secrets for writing the perfect data scientist resume
Machine Learning Driven Programming: A New Programming For A New World
Building Your Data Science Practice
Building a data science portfolio: Machine learning project
In-depth introduction to machine learning in 15 hours of expert videos
5 secrets for writing the perfect data scientist resume
Machine Learning Driven Programming: A New Programming For A New World
Building Your Data Science Practice
Labels:
AI,
Data Scientist,
Machine Learning,
Microsoft,
resume
Thursday, June 30, 2016
Recent Big Data and Machine Learning Info Digest 2016/06/30
Machine Learning Trends and the Future of Artificial Intelligence
What is the Difference Between Deep Learning and “Regular” Machine Learning?
Hadoop Strata Talk - Uber, your hadoop has arrived
How Google is remaking itself as a "Machine Learning First" Company
Why Investments In Big Data And Analytics Are Not Yet Paying Off
The Apache Software Foundation Announces Apache® Bahir™ as a Top-Level Project
What is the Difference Between Deep Learning and “Regular” Machine Learning?
Hadoop Strata Talk - Uber, your hadoop has arrived
How Google is remaking itself as a "Machine Learning First" Company
Why Investments In Big Data And Analytics Are Not Yet Paying Off
The Apache Software Foundation Announces Apache® Bahir™ as a Top-Level Project
Saturday, June 25, 2016
Recent Big Data and Machine Learning Info Digest 2016/06/25
Spark Makes Inroads into NoSQL Ecosystem
RPy2: Combining the Power of R + Python for Data Science
Deep Learning for Recommender Systems - Budapest RecSys Meetup
FOR DATA WORK, “IT’S ACTUALLY PRETTY HARD TO ARGUE *AGAINST* USING PYTHON”
A poet does TensorFlow
Hello, TensorFlow!
Data Mining Reveals the Crucial Factors That Determine When People Make Blunders
RPy2: Combining the Power of R + Python for Data Science
Deep Learning for Recommender Systems - Budapest RecSys Meetup
FOR DATA WORK, “IT’S ACTUALLY PRETTY HARD TO ARGUE *AGAINST* USING PYTHON”
A poet does TensorFlow
Hello, TensorFlow!
Data Mining Reveals the Crucial Factors That Determine When People Make Blunders
Labels:
Data Science,
Deep Learning,
NoSQL,
Python,
R,
Recommendation,
Spark,
TensorFlow
Sunday, June 19, 2016
Recent Big Data and Machine Learning Info Digest 2016/06/19
16 Free Machine Learning Books
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks
A Comparative Roundup: Artificial Intelligence vs. Machine Learning vs. Deep Learning
Tableau Software: How to harness the power of data through data storytelling
The newest tool in the fight against cancer is a huge genetic database driven by algorithms
Movie written by algorithm turns out to be hilarious and intense
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks
A Comparative Roundup: Artificial Intelligence vs. Machine Learning vs. Deep Learning
Tableau Software: How to harness the power of data through data storytelling
The newest tool in the fight against cancer is a huge genetic database driven by algorithms
Movie written by algorithm turns out to be hilarious and intense
Labels:
AI,
Algorithms,
CNN,
Deep Learning,
Machine Learning,
storytelling,
Tableau
Saturday, June 11, 2016
Recent Big Data and Machine Learning Info Digest 2016/06/11
Google's AI software is moving into your iPhone
IBM targets data scientists with a new development platform based on Apache Spark
Reproducible Data Science with Docker Containers - Ben Hamner
The $50 Question : What's the Difference between the Data Scientist and the Data Analyst ?
Machine Learning & Deep Learning Tutorials
IBM targets data scientists with a new development platform based on Apache Spark
Reproducible Data Science with Docker Containers - Ben Hamner
The $50 Question : What's the Difference between the Data Scientist and the Data Analyst ?
Machine Learning & Deep Learning Tutorials
Labels:
AI,
Data Scientist,
Deep Learning,
Docker,
Google,
IBM,
iPhone,
Machine Learning,
Spark,
Tutorial
Thursday, June 2, 2016
Recent Big Data and Machine Learning Info Digest 2016/06/02
AI startup taps human 'swarm' intelligence to predict winners
Crapbots: How Fake #AI and not-so-deep Learning Could Stunt the Bot Revolution
Deep Learning Trends @ ICLR 2016
Facebook is using artificial intelligence to become a better search engine
Introducing DeepText: Facebook's text understanding engine
Crapbots: How Fake #AI and not-so-deep Learning Could Stunt the Bot Revolution
Deep Learning Trends @ ICLR 2016
Facebook is using artificial intelligence to become a better search engine
Introducing DeepText: Facebook's text understanding engine
Labels:
AI,
Deep Learning,
DeepText,
Facebook,
ICLR,
Search Engine
Friday, May 27, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/27
The real prerequisite for machine learning isn’t math, it’s data analysis
What is a resilient distributed dataset?
Learning Path for Developers & IT Professionals to become a Data Scientist
Easier data analysis in Python with pandas (video series)
What is a resilient distributed dataset?
Learning Path for Developers & IT Professionals to become a Data Scientist
Easier data analysis in Python with pandas (video series)
Labels:
Data Analysis,
Data Scientist,
Hadoop,
HDInsight,
Hortonworks,
Machine Learning,
Math,
Microsoft,
Pandas,
Python,
RDD,
Spark
Thursday, May 19, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/19
Labels:
AWS,
Big Data,
Data Scientist,
Deep Learning,
Tutorial
Sunday, May 15, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/15
Benchmarking 20 Machine Learning Models Accuracy and Speed
'Big Data' Is No Longer Enough: It's Now All About 'Fast Data'
What should I learn in data science in 100 hours?
Preview of Apache Spark 2.0 now on Databricks Community Edition
'Big Data' Is No Longer Enough: It's Now All About 'Fast Data'
What should I learn in data science in 100 hours?
Preview of Apache Spark 2.0 now on Databricks Community Edition
Labels:
Benchmark,
Databricks,
Fast Data,
Machine Learning,
Model,
Quora,
Spark
Thursday, May 12, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/12
How to Become a Data Scientist
Categorisation of Machine Learning algorithms for business applications
Data Is the New Dollar: Turning Data Into Business Profit
8 reasons you'll do big data this year
Categorisation of Machine Learning algorithms for business applications
Data Is the New Dollar: Turning Data Into Business Profit
8 reasons you'll do big data this year
Sunday, May 8, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/08
“Hadoop Practitioners Alike Should Rejoice In The Rise Of Spark…”- Interview With Altiscale’s Mike Maciag
Machine Learning Could Be Google's Secret Cloud Weapon
HBase: The database big data left behind
Inside Palantir, Silicon Valley’s Most Secretive Company
The next stop on the road to revolution is ambient intelligence
Machine Learning Could Be Google's Secret Cloud Weapon
HBase: The database big data left behind
Inside Palantir, Silicon Valley’s Most Secretive Company
The next stop on the road to revolution is ambient intelligence
Wednesday, May 4, 2016
Recent Big Data and Machine Learning Info Digest 2016/05/04
Hadoop Creator: If You Want To Succeed With Big Data, Start Small
Spark Takes On Dataflow in Benchmark Test
Uber's plan to get more people into fewer cars
ENGINEERING INTELLIGENCE THROUGH DATA VISUALIZATION AT UBER
Why GE, Chevron, and Other Energy Giants Are Backing This Big Data Startup
Artificial intelligence in the cloud promises to be the next great disrupter
Spark Takes On Dataflow in Benchmark Test
Uber's plan to get more people into fewer cars
ENGINEERING INTELLIGENCE THROUGH DATA VISUALIZATION AT UBER
Why GE, Chevron, and Other Energy Giants Are Backing This Big Data Startup
Artificial intelligence in the cloud promises to be the next great disrupter
Friday, April 29, 2016
Recent Big Data and Machine Learning Info Digest 2016/04/29
Learning Python For Data Science
How do I prepare myself for a "Big Data" interview/job when I have no knowledge about it?
Big Data: Will We Soon No Longer Need Data Scientists?
Top 10 IPython Notebook Tutorials for Data Science and Machine Learning
How do I prepare myself for a "Big Data" interview/job when I have no knowledge about it?
Big Data: Will We Soon No Longer Need Data Scientists?
Top 10 IPython Notebook Tutorials for Data Science and Machine Learning
Labels:
Data Scientist,
IBM,
interview,
IPython,
Machine Learning,
NLP,
Python,
Quora,
Watson Analytics
Sunday, April 24, 2016
Recent Big Data and Machine Learning Info Digest 2016/04/24
The need to lead in data and analytics
This open source tool from MIT Data Lab will change how you see big data
Who’s the real main character in Shakespearean tragedies? Here’s what the data say
Who’s the Michael Jordan of computer science? New tool ranks researchers' influence
This open source tool from MIT Data Lab will change how you see big data
Who’s the real main character in Shakespearean tragedies? Here’s what the data say
Who’s the Michael Jordan of computer science? New tool ranks researchers' influence
Labels:
Machine Learning,
McKinsey,
Michael Jordan,
MIT,
Shakespeare
Saturday, April 16, 2016
Recent Big Data and Machine Learning Info Digest 2016/04/16
Google launches distributed version of its TensorFlow machine learning system
Step-by-step video courses for Deep Learning and Machine Learning
The 5-Minute Guide to Understanding the Significance of Apache Spark
A Pocket Guide to Data Science
Step-by-step video courses for Deep Learning and Machine Learning
The 5-Minute Guide to Understanding the Significance of Apache Spark
A Pocket Guide to Data Science
Labels:
Data Science,
Google,
Guide,
Machine Learning,
Spark,
TensorFlow
Monday, April 11, 2016
Recent Big Data and Machine Learning Info Digest 2016/04/11
Data in the emerging world of stream processing
13 Machine Learning & Data Science Startups from Y Combinator Winter 2016
Doing Data Science Right — Your Most Common Questions Answered
THE YEAR DATA STREAMING BECOMES MAINSTREAM
A Complete Tutorial to learn Data Science in R from Scratch
A Complete Tutorial to Learn Data Science with Python from Scratch
13 Machine Learning & Data Science Startups from Y Combinator Winter 2016
Doing Data Science Right — Your Most Common Questions Answered
THE YEAR DATA STREAMING BECOMES MAINSTREAM
A Complete Tutorial to learn Data Science in R from Scratch
A Complete Tutorial to Learn Data Science with Python from Scratch
Labels:
Confluent,
Data Science,
Kafka,
LinkedIn,
Machine Learning,
Python,
R,
Streaming,
Y Combinator
Thursday, April 7, 2016
Recent Big Data and Machine Learning Info Digest 2016/04/07
Google Brain’s Quoc Le speaks about Deep learning’s progress and its future
Singular Expertise Only Gets You So Far with Big Data
Chief Scientist At 1-Page Reveals How To Get Started In Machine Learning
The big data market
Singular Expertise Only Gets You So Far with Big Data
Chief Scientist At 1-Page Reveals How To Get Started In Machine Learning
The big data market
Labels:
Big Data,
Data Scientist,
Deep Learning,
Google,
Machine Learning,
Quora
Saturday, March 26, 2016
Recent Big Data and Machine Learning Info Digest 2016/03/16
Microsoft to open-source A.I. development platform based on Minecraft
Kaggle - Airbnb New User Bookings - 2nd Place Solution
How we scaled data science to all sides of Airbnb over 5 years of hypergrowth
Designing Machine Learning Models: A Tale of Precision and Recall
How Airbnb uses machine learning to detect host preferences
Kaggle - Airbnb New User Bookings - 2nd Place Solution
How we scaled data science to all sides of Airbnb over 5 years of hypergrowth
Designing Machine Learning Models: A Tale of Precision and Recall
How Airbnb uses machine learning to detect host preferences
Saturday, March 19, 2016
Recent Big Data and Machine Learning Info Digest 2016/03/19
NOT A DATA SCIENTIST? YOU CAN STILL BE DATA SAVVY
Why Hadoop Must Evolve Toward Greater Simplicity
Big Data Insights - IT Support Log Analysis
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department
Why Hadoop Must Evolve Toward Greater Simplicity
Big Data Insights - IT Support Log Analysis
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department
Sunday, March 13, 2016
Recent Big Data and Machine Learning Info Digest 2016/03/13
BEGINNER’S GUIDE TO THE HISTORY OF DATA SCIENCE
Spark and big data discovery: An evolutionary perspective
Instagram Surveillance: How Data Analysts Link Your Photos To Your Credit Card Purchases
HADOOP AND SPARK: A MATCH MADE IN (BIG DATA) HEAVEN
Spark and big data discovery: An evolutionary perspective
Instagram Surveillance: How Data Analysts Link Your Photos To Your Credit Card Purchases
HADOOP AND SPARK: A MATCH MADE IN (BIG DATA) HEAVEN
Tuesday, March 8, 2016
Recent Big Data and Machine Learning Info Digest 2016/03/08
How IBM Is Hoping To Close The Massive Big Data And Analytics Skills Gap
House Hunting with Watson Analytics
21 Scary Things Big Data Knows About You
How do I become a data scientist without a PhD?
House Hunting with Watson Analytics
21 Scary Things Big Data Knows About You
How do I become a data scientist without a PhD?
Monday, February 29, 2016
Tuesday, February 23, 2016
Recent Big Data and Machine Learning Info Digest 2016/02/23
8 Ways To Monetize Data
50 Years of Data Science by David Donoho
Facebook Says It Has Created The Most Accurate Population Density Models Ever
Top 10 Quora Machine Learning Writers and Their Best Advice
Top 10 Data Mining Algorithms, Explained
Advanced Analytics Software’s Most Important Feature? Gartner Says it’s VCF
50 Years of Data Science by David Donoho
Facebook Says It Has Created The Most Accurate Population Density Models Ever
Top 10 Quora Machine Learning Writers and Their Best Advice
Top 10 Data Mining Algorithms, Explained
Advanced Analytics Software’s Most Important Feature? Gartner Says it’s VCF
Labels:
Algorithms,
Data Mining,
Data Science,
David Donoho,
Facebook,
Gartner,
MIT,
Quora,
VCF
Sunday, February 21, 2016
Recent Big Data and Machine Learning Info Digest 2016/02/21
18 Reasons Data Scientists are Difficult to Manage
Building the Next Generation of Apps with IBM Watson
Recommending Recommendation Systems
The Nine Must-Have Datasets for Investigating Recommender Systems
Build your own neural network classifier in R
Building the Next Generation of Apps with IBM Watson
Recommending Recommendation Systems
The Nine Must-Have Datasets for Investigating Recommender Systems
Build your own neural network classifier in R
Labels:
Data Scientist,
IBM,
Jun Ma,
Lab41,
Neural Network,
R,
Recommendation,
Watson
Tuesday, February 16, 2016
Recent Big Data and Machine Learning Info Digest 2016/02/16
Sick of ETL? Database virtualization can help (Big Data and RDBMS)
Bossie Awards 2015: The best open source big data tools
AI and robots threaten to unleash mass unemployment, scientists warn
Top 12 Explanations You’ll Hear in 2016 for Why Big Data Isn’t Paying Off
Bossie Awards 2015: The best open source big data tools
AI and robots threaten to unleash mass unemployment, scientists warn
Top 12 Explanations You’ll Hear in 2016 for Why Big Data Isn’t Paying Off
Saturday, February 13, 2016
Recent Big Data and Machine Learning Info Digest 2016/02/13
Online reading behavior predicts stock movements
How eBay's Kylin Tool Makes Sense Of Big Data
From Discovery to Selection: 10 Things We Learned About Machine Learning and Data Science
Teach Yourself Deep Learning with TensorFlow and Udacity
How eBay's Kylin Tool Makes Sense Of Big Data
From Discovery to Selection: 10 Things We Learned About Machine Learning and Data Science
Teach Yourself Deep Learning with TensorFlow and Udacity
Labels:
eBay,
Google,
Kylin,
Machine Learning,
TensorFlow
Sunday, January 31, 2016
Recent Big Data and Machine Learning Info Digest 2016/01/31
How can a Data Scientist student develop domain expertise?
Deep Learning with Spark and TensorFlow
15 big data and analytics companies to watch
8 ways IBM Watson Analytics is transforming business
Hadoop Jobs: 9 Ways To Get Hired
Deep Learning with Spark and TensorFlow
15 big data and analytics companies to watch
8 ways IBM Watson Analytics is transforming business
Hadoop Jobs: 9 Ways To Get Hired
Labels:
DataVisor,
Deep Learning,
domain expertise,
Hadoop,
Job,
Spark,
Striim,
TensorFlow,
Watson,
WebAction
Wednesday, January 27, 2016
Recent Big Data and Machine Learning Info Digest 2016/01/27
Labels:
AI,
Big Data,
Blockchain,
Certification,
Data Science,
Kaggle,
Marvin Minsky,
Online Course,
R,
recipes
Sunday, January 24, 2016
Monday, January 18, 2016
Recent Big Data and Machine Learning Info Digest 2016/01/18
This is the story of analytics at Kickstarter
Interview: Brad Klingenberg, Director of Styling Algorithms at Stitch Fix
Advice for data scientists - StitchFix
Monetizing Mobile Deep Linking: Who is Really Controlling the User?
Thanks Shaohua for above information sharing.
Interview: Brad Klingenberg, Director of Styling Algorithms at Stitch Fix
Advice for data scientists - StitchFix
Monetizing Mobile Deep Linking: Who is Really Controlling the User?
Thanks Shaohua for above information sharing.
Friday, January 15, 2016
Talks (video) Given by Well-known Data Scientists
Hilary Mason (was chief scientist at bit.ly and was regarded as one of the most powerful women in data tech)
DJ Patil (U.S. Chief Data Scientist at White House Office of Science and Technology Policy)
Jeff Hammerbacher (built Facebook data science team, chief sicentist as Cloudera)
DJ Patil (U.S. Chief Data Scientist at White House Office of Science and Technology Policy)
Jeff Hammerbacher (built Facebook data science team, chief sicentist as Cloudera)
Wednesday, January 13, 2016
Recent Big Data and Machine Learning Info Digest 2016/01/13
Michael Stonebraker: Big Data is (at least) Four Different Problems
10 More lessons learned from building real-life Machine Learning systems — Part I
Artificial Intelligence: Can Watson Save IBM?
Algorithms can predict how students will answer questions, and even explain why they would get questions wrong [paper]
Epic visualizations from FlowingData in 2015
Benchmarking streaming computation engines at Yahoo!
The untapped potential of health care APIs
Microsoft researchers win Imagenet computer vision challenge
Learn python overtakes learn java
Data scientists keep forgetting the one rule every researcher should know by heart
Thanks Shaohua for above information sharing.
- Stonebraker on "SQL Databases v. NoSQL Databases"
- Stonebraker on "NoSQL and Enterprises"
- Stonebraker on "mapReduce and Parallel DBmss"
10 More lessons learned from building real-life Machine Learning systems — Part I
Artificial Intelligence: Can Watson Save IBM?
Algorithms can predict how students will answer questions, and even explain why they would get questions wrong [paper]
Epic visualizations from FlowingData in 2015
Benchmarking streaming computation engines at Yahoo!
The untapped potential of health care APIs
Microsoft researchers win Imagenet computer vision challenge
Learn python overtakes learn java
Data scientists keep forgetting the one rule every researcher should know by heart
Thanks Shaohua for above information sharing.
Labels:
FlowingData,
IBM,
Imagenet,
NoSQL,
Python,
Shaohua Zhang,
Stonebraker,
Watson
Monday, January 4, 2016
Starting R Programming course
Following our course list, I started my first one - R Programming from Coursera today.
According to the Wikipedia, R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
According to the Wikipedia, R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.
The philosophy of S (so as R ) is what John Chambers said in the "Stages in the Evolution of S":
"...we wanted users to be able to begin in an interactive environment, where they did not consciously think of themselves as programming. Then as their needs became clearer and their sophistication increased, they should be able to slide gradually into programming, when the language and system aspects would become more important."
Sunday, January 3, 2016
Choose Big Data Courses
To start our journey, we first need to choose the Big Data courses.
There are so many courses (online and offline). Just google the 3 words, there are 121,000,000 results.
So, how should we start? Some people can help us with their recommendations:
There are so many courses (online and offline). Just google the 3 words, there are 121,000,000 results.
So, how should we start? Some people can help us with their recommendations:
Based on our own research and experience, K and I come out the following courses first (we might adjust along the way).
What do you think? Want to study together?
Saturday, January 2, 2016
New Year, New Journey.
Last year we have been organizing 3 Big Data related events (one of them with IBM Big Data University) through ACSIP.
When K and I started to explore the idea about Big Data education/training, we realized that we need to equip ourselves first. From May 5 to June 16 last year, we both took the MIT course- Tackling the Challenges of Big Data. We then together gave a try on one of Kaggle Competitions - Titanic: Machine Learning from Disaster. When Toronto Blue Jays was on their way to clinch a playoff berth and division championship in 2015, we worked together to make predictions of Jays' games by using A Markov Chain Approach to Baseball with MLB statistics data available online.
At the beginning of new year, we would like to get deeper into the Big Data field. We will start to learn more related courses together.
It will be fun and challenge. If you like, you can follow our journey.
Subscribe to:
Posts (Atom)