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Ajit jaokar's Blog (52)

The Mathematics of Data Science: Understanding the foundations of Deep Learning through Linear Regression

 

Note: This is a long post, but I kept it as a single post to maintain continuity of the thought flow

In this longish post, I have tried to explain Deep Learning starting from familiar ideas like machine learning. This approach forms a part of my forthcoming book. You can connect with me on Linkedin to know more about the book. I have used this approach in my teaching. It is based on ‘learning by…

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Added by ajit jaokar on January 14, 2019 at 12:29pm — 1 Comment

AI / Machine learning Cloud APIs: AWS – Azure - GCP - our experience

Most of us start working with specific programming languages like TensorFlow and Pyspark

So, we are relatively not so used to working with Cloud APIs.

But Cloud APIs for Machine Learning and Deep Learning can make your life a lot easier in building AI and Machine Learning services

Of course, the Cloud APIs have a cost – but they…

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Added by ajit jaokar on January 5, 2019 at 2:00pm — No Comments

Why I agree with Geoff Hinton: I believe that Explainable AI is over-hyped by media

 

Geoffrey Hinton dismissed the need for explainable AI. A range of experts have explained why he is wrong.

 

I actually tend to agree with…

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Added by ajit jaokar on December 27, 2018 at 12:30pm — 2 Comments

A simplified explanation for Understanding the Mathematics of Deep Learning

Introduction

In this post, I explain the maths of Deep Learning in a simplified manner.  To keep the explanation simple, we cover the workings of the MLP mode (Multilayer Perceptron). I have drawn upon a number of references – which are indicated in the post in the relevant sections.

 

Deep Learning models are playing a significant role in many domains. In the simplest case, deep learning involves stacking multiple neural network layers to address a…

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Added by ajit jaokar on December 17, 2018 at 11:10am — No Comments

Interpreting Exploratory Data Analysis (EDA)

Introduction

Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (graphical and quantitative) to better understand data. It is easy to get lost in the visualizations of EDA and to also lose track of the purpose of EDA. EDA aims to make the downstream analysis easier.…

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Added by ajit jaokar on December 6, 2018 at 5:49am — No Comments

The Halley’s comet Data Scientist

Image source: Wikipedia

Introduction

This post is about how not to be the Halley’s comet Data Scientist i.e. to keep yourselves motivated in your Data Science journey

The views expressed here are my own

Many professionals want to transition their career to AI.

But most will not

Often, we see a…

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Added by ajit jaokar on November 15, 2018 at 12:49pm — 2 Comments

AI technologies used in Robotics

 

 

Background

 

Robotics today is not the same as assembly line Robots of the industrial age because AI is impacting many areas of Robotics.

Specifically, AI is changing Robotics in two key areas

  1. Robots are becoming autonomous…
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Added by ajit jaokar on November 7, 2018 at 4:32am — No Comments

How to Choose a Machine Learning Model – Some Guidelines

 

 

In this post, we explore some broad guidelines for selecting machine learning models

 

The overall steps for Machine Learning/Deep Learning are:

  • Collect…
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Added by ajit jaokar on October 15, 2018 at 11:30am — 7 Comments

AI and Algorithmocracy: What the Future Will Look Like

Introduction

With the recent news about Facebook and Cambridge analytica, we are rightly concerned about the power and impact of algorithms to shape political…

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Added by ajit jaokar on October 1, 2018 at 2:00am — No Comments

MBA vs. Data Science qualifications: Does #AI and #DataScience explain the fall in MBA applications?



Last week, the financial times wrote that there was a sharp fall in MBA applications in the USA.

The Elite MBA programs were not affected by this trend, but most others were 

Many factors contribute to this fall in…

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Added by ajit jaokar on September 24, 2018 at 11:20am — No Comments

Landing a 150k USD #datascientist job: Seven things you need to know

 

On Linkedin, many Data Science enthusiasts who aspire to be Data Scientists follow me.

 

One person asked the question:

 

What do I need to know to get a $150K job as a Data Scientist?

 

It’s a good…

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Added by ajit jaokar on September 16, 2018 at 12:28am — 1 Comment

How to use IoT datasets in #AI applications (full stack)

Introduction

Recently, google launched a Dataset search – which is a great resource to find Datasets.  In this post, I list some IoT datasets which can be used for Machine Learning or Deep Learning applications. But finding datasets is only part of the story.  A static dataset for IoT is not enough i.e. some of the interesting analysis is in streaming mode. To…

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Added by ajit jaokar on September 12, 2018 at 3:00am — No Comments

How to create an #Enterprise #AI Business case driven by Data

Introduction

In a previous post (four quadrants of the Enterprise AI business case) – I laid the foundations of a strategy for deploying Enterprise AI. In this post, we…

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Added by ajit jaokar on September 2, 2018 at 4:53am — No Comments

Can we use deliberate practise for learning to code #AI and #machinelearning

In this post, I explore if ideas of deliberate practise can be applied to teaching coding for Artificial Intelligence / Machine Learning

I am exploring these ideas in a free coding workshop/ meetups in London.

There…

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Added by ajit jaokar on August 18, 2018 at 10:30pm — 7 Comments

Four Quadrants of the Enterprise AI business case

Introduction

In this post, I discuss the development of the Enterprise AI business case through a framework of four quadrants.  According to Gartner: “The mindset shift required for AI can lead to “cultural anxiety” because it calls for a deep change in behaviors and ways of thinking”. Deployment of AI in an Enterprise is complex and multi-disciplinary. Hence, this framework is evolutionary.  The vendors and initiatives listed are included to…

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Added by ajit jaokar on August 12, 2018 at 7:11am — 1 Comment

How you can stay up to date with your #AI and #MachineLearning knowledge

 

Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can…

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Added by ajit jaokar on August 7, 2018 at 6:32am — 3 Comments

Data Science for Internet of Things - The Big Picture

This big picture view lays the foundation of our book Data Science for the Internet of Things. (Co-authored by Ajit Jaokar, Jean Jacques Bernard and Sukanya Mandal)

We address the question: at what points can we add analytics to the data after it leaves the sensor and…

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Added by ajit jaokar on May 24, 2018 at 4:00pm — 5 Comments

GDPR and AI – strategies and options in a nutshell

I had the pleasure to meet @KirkDBorne last week and participate in a panel moderated by Kirk - Decoding #GDPR, #IoT, and #UX in the #BigData world: discussing #AI #MachineLearning #DataSecurity at the…

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Added by ajit jaokar on March 26, 2018 at 4:00am — 2 Comments

In 2018 - AI will start to bring jobs back from offshore destinations to the West

In my previous article, I wrote about the impact of Robotic Process Automation which drives Enterprise AI. In that article, I said: The first group of workers to…

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Added by ajit jaokar on January 7, 2018 at 11:00am — 1 Comment

IOTA – The potential to drive Data Science for IoT

I have a close circle of clued-on/tech savvy friends whose views I take seriously. For the last few weeks, one of these friends has been sending me emails extolling the merits of something called IOTA – which calls itself as the next generation Blockchain.  At first, I…

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Added by ajit jaokar on December 24, 2017 at 12:00pm — No Comments

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