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Sharmistha Chatterjee liked ajit jaokar's blog post 23 sources of data bias for #machinelearning and #deeplearning
Tuesday
Vincent Granville commented on ajit jaokar's blog post 23 sources of data bias for #machinelearning and #deeplearning
"An example is with Covid-19. How many people were at some point positive and recovered? If most people don't get tested, you may be missing (say) 20,000,000 in US, making death rate and other stats wrong by a factor 10. None of my family was…"
Saturday
Prasanth liked ajit jaokar's blog post Data science cookbook style code reference in Python for beginners
Jul 5
ajit jaokar liked ajit jaokar's blog post 23 sources of data bias for #machinelearning and #deeplearning
Jul 3
Fernando Agustin Méndez Monroy commented on ajit jaokar's blog post IoT Anomaly detection - algorithms, techniques and open source implementation
"Sometimes we want to get rid of anomalies, sometimes we actually want to understand them. One example is Climate Variability due to Climate Change."
Jul 3
Michael Y. Choi liked ajit jaokar's blog post How exactly do you identify an AI start-up?
Jun 30
Michael Y. Choi liked ajit jaokar's blog post IoT Anomaly detection - algorithms, techniques and open source implementation
Jun 30
ajit jaokar's blog post was featured

23 sources of data bias for #machinelearning and #deeplearning

In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good – so below is an actual representation because I found it useful as it isfull paper link below 1) Historical Bias. Historical bias is the already existing bias and socio-technical issues in the world and can seep into from…See More
Jun 30
ajit jaokar posted blog posts
Jun 28
Kwai Leung commented on ajit jaokar's blog post Free book – for #datascience interviews - Guide to competitive programming
"It works. Thank you."
Jun 27
ajit jaokar commented on ajit jaokar's blog post Free book – for #datascience interviews - Guide to competitive programming
Jun 27
Hani Patel commented on ajit jaokar's blog post Free book – for #datascience interviews - Guide to competitive programming
"https://link.springer.com/book/10.1007/978-3-319-72547-5 - This is the link from where I downloaded. @kwaiLeung "
Jun 27
Kwai Leung commented on ajit jaokar's blog post Free book – for #datascience interviews - Guide to competitive programming
"Where can I click and download this book? Thank you."
Jun 27
Eileen Corrigan liked ajit jaokar's blog post Free book – for #datascience interviews - Guide to competitive programming
Jun 25
ajit jaokar's blog post was featured

Free book – for #datascience interviews - Guide to competitive programming

Recently Springer made some good books on maths free to download.Competitive programming strategies are useful for many data science interviews and they help to improve your maths foundations.  There are not many books on this subject (although there are many good websites and YouTube resources).So, I hope you find this book usefulFrom the bookCompetitive programming…See More
Jun 23
Anthony Fiedler commented on ajit jaokar's blog post How exactly do you identify an AI start-up?
"Good review of AI Start-up identification in new spaces, but I think a majority of the start-ups that arise are taking applications from start-ups in one area and applying it to adjacent fields, or ones where efficiency rises enough to create value,…"
Jun 22

Profile Information

Company:
futuretext
Job Title:
founder
Seniority:
Professor
Industry:
IoT, Telecoms, Smart Cities
Short Bio:
My research is focused on Data Science for IoT. I teach same at Oxford Uni and UPM in Madrid (@forumoxford + @citysciences). Also launching a course / certification Data Sciences for IoT for industry. Personal research interests - Deep learning algorithms for IoT/future city domains
LinkedIn Profile:
http://www.opengardensblog.futuretext.com/archives/2016/01/data-sci...
Interests:
Finding a new position, Networking, New venture, Other

Ajit jaokar's Blog

23 sources of data bias for #machinelearning and #deeplearning

Posted on June 30, 2020 at 12:02pm 1 Comment

In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good – so below is an actual representation because I found it useful as it is

full paper link below

 

1) Historical Bias. Historical bias is the already…

Continue

IoT Anomaly detection - algorithms, techniques and open source implementation

Posted on June 26, 2020 at 10:00am 1 Comment

Background

Anomaly detection for IoT is one of the archetypal applications for IoT.

Anomaly detection techniques are also used outside of IoT.

In my teaching at the #universityofoxford - we use anomaly detection as a use case because it brings together many of…

Continue

Free book – for #datascience interviews - Guide to competitive programming

Posted on June 23, 2020 at 12:00pm 4 Comments

Recently Springer made some good books on maths free to download.

Competitive programming strategies are useful for many data science interviews and they help to improve your maths foundations.  There are not many books on this subject (although there are many good websites and YouTube resources).

So, I hope you find this book…

Continue

How exactly do you identify an AI start-up?

Posted on June 17, 2020 at 1:00pm 1 Comment

 

I like this image from the twitter feed of the Paris based Venture Capitalist Michael Jackson

It shows that everyone wants to be an ‘AI start-up’ - but only when it suits them! 

So, how exactly do you build (and identify) an AI start-up

I always like to start off with a set of ground…

Continue

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