My Journey with Data Science - Part 3

If you are reading this blog you would know that I am a seeker in the path of Data Science. I thought of sharing information on what I am reading for the benefit of fellow seekers.

Earlier I had shared similar information as part of the blogs An email to a Data Science enthusiast, An email to a Data Science enthusiast - Part 2, My Journey with Data Science - Part 1 and My Journey with Data Science - Part 2

Our opinions are greatly influenced by what we read, hence here is a subset of what I have read over last two years (Since I wrote Part 2 of the blog)

Artificial Intelligence

1.  13 Mind-Blowing Things Artificial Intelligence Can Already Do Today
2.  The Next-Generation Applications Of Artificial Intelligence And Machine Learning
3.  New AI fake text generator may be too dangerous to release, say creators
4.  AI Can Now Create Artificial People - What Does That Mean For Humans?
5.  AI Discovers Something Fascinating in Old Scientific Papers that Humans May Have Missed
6.  AI Transformation Playbook - How to lead your company into the AI era
7.  A Gentle Introduction to Transfer Learning for Deep Learning
8.  Artificial Intelligence Can Now Write Amazing Content -- What Does That Mean For Humans?
9.  How India’s data labellers are powering the global AI race
10.  Forty percent of ‘AI startups’ in Europe don’t actually use AI, claims report
11.  The Fascinating Ways Facial Recognition AIs Are Used In China
12.  Arm Leads Project to Develop an Armpit-Sniffing Plastic AI Chip
13.  The Amazing Ways How Unilever Uses Artificial Intelligence To Recruit & Train Thousands Of Employees
14.  Courts Are Using AI to Sentence Criminals. That Must Stop Now
15.  The State of Explainable AI

 Trends

1.  Azure solution architectures
2. Modern Data warehousing Using Azure Data Lake, CDM Folders And Power BI Dataflows
3. MIC?R??O??SO??FT A?ND? G?RA?P?HC?O?R??E C?O??LLAB?OR??ATE TO? ACC?ELER??A?TE A?R?TIFIC??IA?L INTELLIG?ENC?E
4. Using AWK and R to parse 25tb
5. Data Science at the Command Line
6. Python already replaced Excel in banking
7. Small data projects and data quality, gain faster benefits
8. Understanding few-shot learning in machine learning
9. Continual/Lifelong Learning with Deep Architectures.
10. 20 Amazing Facts About Quantum Computing Everyone Should Read
11. Beyond Interactive: Notebook Innovation at Netflix

 Good Reads

1.  And then comes scaffold
2. MACHINEHACK WINNERS: HOW THESE DATA SCIENCE ENTHUSIASTS SOLVED THE ‘PREDICT BOOKS PRICE’ HACKATHON
3. Very nice write-up on DMPs
4. AI, Big Data and Block chain to re-imagine the supply chain
5. The Future of Data Engineering
6. GCP Data engineering fundamentals Quick reference notes - Part 1
7. The report that I wait to read every year, for me to get a sense of where the industry is moving and what should I ask my teams to focus on
8. CHIHUAHUA OR MUFFIN? SEARCHING FOR THE BEST COMPUTER VISION API
9. Machine learning 'causing science crisis'
10. Don't spend money, spend a lot of time, do it consistently and continue learning
11. Why There Will Be No Data Science Job Titles By 2029
12. Picking Optimization
13. What are the benefits and effects of Natural Language Generation (NLG) on Business Intelligence?
14. Graph databases and machine learning will revolutionize MDM strategies
15. What Most People Get Wrong About Data Lakes
16. 5 Things You Need To Know About Data Science
17. 25 Open Datasets for Deep Learning Every Data Scientist Must Work With
18. The Artistic Guide to Big Data: Hadoop/Spark

 Curious to know what you think of the links