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Ajit jaokar's Blog (77)

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 the author (Andreas Kretz.com) patreon site : (…

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Added by ajit jaokar on July 17, 2019 at 11:00am — No Comments

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 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…

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Added by ajit jaokar on July 14, 2019 at 12:30pm — No Comments

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 it at stateofai 2019

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

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Added by ajit jaokar on July 8, 2019 at 9:04am — 1 Comment

Why is it hard for AI to detect human bias?

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…

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Added by ajit jaokar on June 30, 2019 at 9:19am — No Comments

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

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…

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Added by ajit jaokar on June 24, 2019 at 12:30am — No Comments

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)

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

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Added by ajit jaokar on June 16, 2019 at 11:04am — 1 Comment

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 the gap between high school maths and deep learning. Welcome comments

 

Background

As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University of…

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Added by ajit jaokar on June 14, 2019 at 12:33pm — No Comments

Seduced by the Big Data meme: Hadoop vs the Public Cloud

 

Currently, Cloudera is in the news for all the wrong reasons(Cloudera stock down 42%)

Since Cloudera now also incorporates Hortonworks – the current issues are just the latest in the Big Data woes. Apparently, the third vendor…

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Added by ajit jaokar on June 10, 2019 at 10:30am — No Comments

Data science Coding in a weekend series of books …

After testing this idea for the last few months, we have formally launched this concept

 

The idea of ‘Data Science Coding in a weekend’ originated from meetups we conducted in London

 

The idea is simple but effective

 

We choose a complex section of code and try to learn it in detail over…

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Added by ajit jaokar on May 29, 2019 at 7:52am — No Comments

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 the ‘in a weekend’ series of books is to study one complex section of code in a weekend to master the concept. This week. we plan to launch a book called “An…

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Added by ajit jaokar on May 26, 2019 at 10:00am — No Comments

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. while training the model. However, it has a broader application in model selection and hyperparameter tuning.

Let us first explore the process of cross validation itself and then see how it applies to different parts of the data science pipeline

Cross-validation is a resampling procedure used to evaluate machine learning models on a…

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Added by ajit jaokar on May 13, 2019 at 11:00am — 1 Comment

Logistic regression as a neural network

As a teacher of Data Science (Data Science for Internet of Things course at the University of Oxford),  I am always fascinated in cross connection between concepts. I noticed an interesting image on Tess Fernandez slideshare (which I very much recommend you follow) which talked of…

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Added by ajit jaokar on May 10, 2019 at 6:13am — No Comments

The Mathematics of Forward and Back Propagation

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.

The…

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Added by ajit jaokar on April 30, 2019 at 9:00pm — No Comments

How will the Data Scientist’s job change through automated machine learning?

 

 

Introduction

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…

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Added by ajit jaokar on April 26, 2019 at 10:51am — No Comments

Free book – learn azure in a month of lunches – covering Cloud, AI, devops etc

 

 

 

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.…

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Added by ajit jaokar on April 23, 2019 at 11:00am — No Comments

Free Python Data Science coding Book series

In this post, I explain

  1. How you can participate further in the free book series which we are launching based on the early experiences and
  2. Useful resources we recommend based on our experience for learning coding for Data Science (using Python – tensorflow and keras)

 

To provide some context, I posted…

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Added by ajit jaokar on April 17, 2019 at 8:06am — No Comments

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 ability of AI to recognise emotions is a fascinating subject and has wide-ranging applications across many fields of…

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Added by ajit jaokar on April 7, 2019 at 2:00pm — No Comments

Educating for AI – one of the most critical problems in AI

 

Background

One of the hardest problem in AI is not technical

 

It is social

 

Specifically, it is the problem of “educating people for living and working in a world dominated by AI”

 

This blog is based on my talk and notes in panels at the…

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Added by ajit jaokar on March 29, 2019 at 2:16pm — 2 Comments

AI and patents - trends to watch  from the WIPO technology trends report

AI and patents - trends to watch  from the WIPO technology trends report

WIPO Technology Trends report 2019  came as a surprise to me. We in AI are not used to thinking about patents so much because tools / platforms are mostly Open sourced.

 

Here are the key…

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Added by ajit jaokar on March 22, 2019 at 1:05pm — No Comments

Can we fix Smart City deployments using AI, Cloud and Video?

Introduction

I spoke at the iot expo on AI and smart cities  in London this week

Smart cities have been around for more than a decade

The overall numbers for Smart cities are promising

  • 2018 over $81 billion was spent on Smart City initiatives and this number is expected…
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Added by ajit jaokar on March 18, 2019 at 12:35am — 2 Comments

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