State of #AI 2019 Report
I highly recommend the #StateofAI 2019 report. I have followed this report from By Nathan Benaich and Ian Hogarth The report is free and you can download… Read More »State of #AI 2019 Report
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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.
An elegant way to represent forward propagation and back propagation in a neural network
Sometimes, you see a diagram and it gives you an ‘aha ha’ moment Here is one representing forward propagation and back propagation in a neural… Read More »An elegant way to represent forward propagation and back propagation in a neural network
The missing link for IoT – the Cloud
Introduction IoT (Internet of Things) has not quite taken off yet as envisaged – Will the cloud overcome the shortcomings of IoT? I believe… Read More »The missing link for IoT – the Cloud
Free book: The #dataengineering cookbook by Andreas Kretz
I found an interesting, free book which is still a work in progress book – The Data Engineering Cookbook I will be contributing through… Read More »Free book: The #dataengineering cookbook by Andreas Kretz
How to learn the maths of Data Science using your high school maths knowledge – Gradient Descent
This post is a part of my forthcoming book on Mathematical foundations of Data Science. In the previous blog, we saw how you could use basic… Read More »How to learn the maths of Data Science using your high school maths knowledge – Gradient Descent
Can AI detect emotions better than humans?
Introduction “The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions”. Marvin Minsky The… Read More »Can AI detect emotions better than humans?
Can design sprints work for Artificial Intelligence applications?
Can design sprints work for Artificial Intelligence applications? Last week, for the first time, I attended a meetup on Design Sprints( The Design Sprint Underground)… Read More »Can design sprints work for Artificial Intelligence applications?
How to learn the maths of Data Science using your high school maths knowledge
This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge… Read More »How to learn the maths of Data Science using your high school maths knowledge
Azure Machine Learning concepts – an Introduction
Azure Machine Learning concepts – an Introduction Introduction Last week, we launched a free book called Classification and Regression in a weekend. The idea of… Read More »Azure Machine Learning concepts – an Introduction
Understanding Cross Validation across the Data Science pipeline
Cross validation is a technique commonly used In Data Science. Most people think that it plays a small part in the data science pipeline, i.e.… Read More »Understanding Cross Validation across the Data Science pipeline