Understanding the maths behind forward and back propagation is not very easy.
There are some very good – but also very technical explanations.
For example : The Matrix Calculus You Need For Deep Learning Terence Parr and Jeremy Howard is an excellent resource but still too complex for beginners.
I found a much simpler explanation in the ml cheatsheet.
Added by ajit jaokar on April 30, 2019 at 9:00pm — No Comments
Automated machine learning is a fundamental shift to machine learning and data science. Data science as it stands today, is resource-intensive, expensive and challenging. It requires skills which are in high demand. Automated Machine learning may not quite lead to the beach lifestyle for the data…Continue
Added by ajit jaokar on April 26, 2019 at 10:51am — No Comments
At the Data Science for IoT course at the University of Oxford – I have been working on a strategy implementing Artificial Intelligence holistically on the Cloud and Edge. This is a complex approach with many new concepts to learn.…Continue
Added by ajit jaokar on April 23, 2019 at 11:00am — No Comments
In this post, I explain
To provide some context, I posted…Continue
Added by ajit jaokar on April 17, 2019 at 8:06am — No Comments
“The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions”.
The ability of AI to recognise emotions is a fascinating subject and has wide-ranging applications across many fields of…Continue
Added by ajit jaokar on April 7, 2019 at 2:00pm — No Comments