Artificial Intelligence – Cloud and Edge implementations takes an engineering-led approach for the deployment of AI to Edge devices within the framework of the cloud.

We often use the word ‘engineering’ in casual conversation. However, in this context, we attach a specific meaning to Engineering. Engineering is the use of scientific principles to…

This post is part of my forthcoming book The Mathematical Foundations of Data Science. Probability is one of the foundations of machine learning (along with linear algebra and optimization). In this post, we discuss the areas where probability theory could apply in machine learning applications. If you want to know more about…

Introduction

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine ..

But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is…

Overview of changes TensorFlow 1.0 vs TensorFlow 2.0

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users.…

The explanation of Logistic Regression as a Generalized Linear Model and use as a classifier is often confusing.

In this article, I try to explain this idea from first principles. This blog is part of my forthcoming book on the Mathematical foundations of Data Science. If you are interested in knowing more, please follow me on linkedin Ajit Jaokar

We take the following approach:

• We see first briefly how…

Introduction

Will china dominate AI?

When I spoke at the UK China business forum last month, I discussed this topic in response to an audience question.

In the current climate of nationalistic fervour, I see the same question asked in many guises.

For…

Can an ethical and algorithmically transparent cloud kitchen prevent future Amazon fires?

We often view AI with suspicion – but AI can be used to solve complex problems currently facing society where innovative approaches are needed

For many of us, the Amazon fires are disturbing and a…

The AI Edge Engineer: Extending the power of CI/CD to Edge devices using containers

Background

At the Artificial Intelligence – Cloud and Edge implementations course – I have been exploring the idea of extending CI/CD to Edge devices using containers. In this post, I present these ideas under the framework of the ‘AI Edge…

The use of training, validation and test datasets is common but not easily understood. 

In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths.  If you want to know more about the book, please follow me on Linkedin Ajit Jaokar

Background

Jason…

Background

Containerize your Apps with Docker and Kubernetes is an excellent free book from Gabriel N. Schenker

You can download the whole book by registering…

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 network

I saw it on Frederick kratzert’s blog

 …

Introduction

IoT (Internet of Things) has not quite taken off yet as envisaged -  Will the cloud overcome the shortcomings of IoT? I believe that the Cloud is the missing link that enables IoT to create a critical mass towards deployment.

The vision outlined here is part of my forthcoming book on Data Science…

I found an interesting, free book which is still a work in progress book – The Data Engineering Cookbook

I will be contributing through the author (Andreas Kretz.com) patreon site : (…

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 high school maths to learn about the workings of data science and artificial intelligence

In this post we extend that idea to learn about Gradient descent

We…

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 it at stateofai 2019

The report is kind of Mary Meeker theme for AI for me i.e. a great reference…

AI bias is in the news – and it’s a hard problem to solve

But what about the other way round?

When AI engages with humans – how does AI know what humans really…

The Catch 22 problem holding back AI application adoption  ...

Last week, there was an interesting report in the MIT technology review that Artificial Intelligence can help construction industry to help see…

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)

I had heard of Design sprints from Google – but I am not an expert. The organiser, Eran, created…