"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…"
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"Sometimes we want to get rid of anomalies, sometimes we actually want to understand them. One example is Climate Variability due to Climate Change."
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
"It works. Thank you."
"here is link https://link.springer.com/book/10.1007/978-3-319-72547-5"
"https://link.springer.com/book/10.1007/978-3-319-72547-5 - This is the link from where I downloaded. @kwaiLeung "
"Where can I click and download this book? Thank you."
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
"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,…"
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 3 Likes
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…
ContinueIoT Anomaly detection - algorithms, techniques and open source implementation
Posted on June 26, 2020 at 10:00am 1 Comment 2 Likes
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…
ContinueFree book – for #datascience interviews - Guide to competitive programming
Posted on June 23, 2020 at 12:00pm 4 Comments 3 Likes
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…
ContinueHow exactly do you identify an AI start-up?
Posted on June 17, 2020 at 1:00pm 1 Comment 3 Likes
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…
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