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Ajit jaokar's Blog (67)

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

Explaining AI from a Life cycle of data

 

Background

When I was teaching a session on AI at an MBA program at the London School of Economics,   I thought of explaining AI from the perspective of the life-cycle of Data. This explanation is useful because more people are used to data (than to code). I welcome comments on this approach. Essentially, we consider how data is used and transformed for AI and what are its…

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Added by ajit jaokar on March 4, 2019 at 11:05am — No Comments

Python machine learning libraries

Introduction

This blog is a part of the learn machine learning coding basics in a weekend . We recommend the book Python Data Science Handbook by Jake…

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Added by ajit jaokar on February 19, 2019 at 1:30pm — No Comments

Learn #MachineLearning Coding Basics in a weekend - Glossary and Mindmap

For background to this post, please see Learn #MachineLearning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book. …

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Added by ajit jaokar on February 11, 2019 at 10:30am — 5 Comments

Learn #MachineLearning Coding Basics in a weekend – a new approach to coding for #AI

image source - wikipedia

UPDATE

Hello all

we are now closing this 

we have been…

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Added by ajit jaokar on January 30, 2019 at 12:00pm — 363 Comments

Advice to a fresh graduate for getting a job in AI/ Data Science

 

After a recent webinar, I was asked about advice for getting a job in AI for a fresh graduate

 

This is a good question and not often answered

Here are my thoughts

 

Background

  • Firstly, AI is a vast topic. Everyone has a limited view on AI based on their personal…
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Added by ajit jaokar on January 21, 2019 at 2:22pm — No Comments

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 — 2 Comments

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 — 1 Comment

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

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