Introduction to Probabilistic programming
Last week, I saw a nice presentation on Probabilistic Programming from a student in Iran (link below). I am interested in this subject for… Read More »Introduction to Probabilistic programming
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ajitjaokar
Based in London, Ajit's work spans research, entrepreneurship, and academia relating to artificial intelligence (AI) with Cyber-Physical systems. He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations at the University of Oxford. He is also a visiting fellow in Engineering Sciences at the University of Oxford. Besides this, he also conducts the University of Oxford courses: Digital Twins, Cybseecurity, and Agtech. Ajit works as a Data Scientist through his company, feynlabs - focusing on building innovative early-stage AI prototypes for complex AI applications. Besides the University of Oxford, Ajit has also conducted AI courses at the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM), and as part of The Future Society at the Harvard Kennedy School of Government.
A simple way for getting started with fast.ai for pytorch
At my AI course in the University of Oxford, we are exploring the use of PyTorch for the first time. One of the best libraries… Read More »A simple way for getting started with fast.ai for pytorch
Virtualized network functions: The cloudification of telecoms leading to a new class of applications
The Cloudification of the Telecoms market Last week, Nokia announced 10,000 jobs lost worldwide. Telecoms is a cyclical business with the G cycles (roughly spanning… Read More »Virtualized network functions: The cloudification of telecoms leading to a new class of applications
The Bayesian vs frequentist approaches (Part 3) parametric vs non-parametric models
This post continues our discussion on the Bayesian vs the frequentist approaches. Here, we consider implications for parametric and non-parametric models In the previous blog… Read More »The Bayesian vs frequentist approaches (Part 3) parametric vs non-parametric models
Measuring progress in Symbolic AI: the biggest surprise in AI trends report from Stanford
The biggest surprise for me in the “Measuring trends in Artificial Intelligence” from Stanford University (a must read BTW) is a section on measuring… Read More »Measuring progress in Symbolic AI: the biggest surprise in AI trends report from Stanford
Interesting AI papers published in 2020
For my own research and teaching, I follow AI papers. Here is a list of papers I find interesting. I analysed 3 lists (links below)… Read More »Interesting AI papers published in 2020
The Bayesian vs frequentist approaches: implications for machine learning – Part two
This blog is the second part in a series. The first part is The Bayesian vs frequentist approaches: implications for machine le… In part one,… Read More »The Bayesian vs frequentist approaches: implications for machine learning – Part two
The Bayesian vs frequentist approaches: Implications for machine learning – Part One
Background The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine… Read More »The Bayesian vs frequentist approaches: Implications for machine learning – Part One
Probabilistic Machine Learning book – a great free reference for maths of machine learning
At the #universityofoxford I focus a lot on the mathematics aspect of AI I recommend eight books for the mathematics of AI The Nature Of… Read More »Probabilistic Machine Learning book – a great free reference for maths of machine learning
What distributions do we need to use Deep Learning?
I was asked this question: “What distributions do we need to use Deep Learning?” This is a question with a multi-faceted answer. The direct… Read More »What distributions do we need to use Deep Learning?