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How to be more than just a ‘Notebook Data Scientist’: End-to-End ML (Part 3/3)

How to be more than just a ‘Notebook Data Scientist’: End-to-End ML (Part 3/3)

Do you ever miss the thrill of creating something from scratch? If so, you’ve come to the right place. Enjoy!
Go to the profile of Mandula Thrimanne
Mandula Thrimanne
Understanding BERT: The Power Behind Modern NLP

Understanding BERT: The Power Behind Modern NLP

How BERT Understands Language Context
Go to the profile of Archit Saxena
Archit Saxena
Predictive Maintenance For Constant Flow Machinery Using XGBoost

Predictive Maintenance For Constant Flow Machinery Using XGBoost

How can you use the cutting edge machine learning algorithms to help optimize the operational efficiency of a rather conventional constant…
Go to the profile of Fazil Ahamed
Fazil Ahamed
A Deep Dive into the Self-Attention Mechanism of Transformers

A Deep Dive into the Self-Attention Mechanism of Transformers

Introduction:
Go to the profile of Shreya Srivastava
Shreya Srivastava
Why you should NEVER skip EDA in your ML Projects: End-to-End ML (Part 1/3)

Why you should NEVER skip EDA in your ML Projects: End-to-End ML (Part 1/3)

Let me tell you about the time I learned the hard way how skipping exploratory data analysis (EDA) can lead to a data disaster.
Go to the profile of Mandula Thrimanne
Mandula Thrimanne
Statistics for Data Science 101 Series — Descriptive Statistics

Statistics for Data Science 101 Series — Descriptive Statistics

What is Descriptive Statistics? How does it help and what does it comprise of? Let’s find out!!!
Go to the profile of Adith Narasimhan Kumar
Adith Narasimhan Kumar
Statistics for Data Science 101 Series — An Overview

Statistics for Data Science 101 Series — An Overview

Following the Data Analytics 101 Series, the Statistics 101 Series will delve into a burst of articles that explain the usage and…
Go to the profile of Adith Narasimhan Kumar
Adith Narasimhan Kumar
Value Stream Mapping for Machine Learning Driven Applications

Value Stream Mapping for Machine Learning Driven Applications

And why it need a different mindset than building traditional software
Go to the profile of Akhil Anurag
Akhil Anurag
5 Ways to Fail at Building Machine Learning Applications

5 Ways to Fail at Building Machine Learning Applications

and your guide to avoid them while you are at it!
Go to the profile of Akhil Anurag
Akhil Anurag
Attention is All You Need: Demystifying the Transformer Revolution in NLP

Attention is All You Need: Demystifying the Transformer Revolution in NLP

Unveiling the Revolutionary Transformer Architecture in NLP
Go to the profile of Archit Saxena
Archit Saxena
Data Analytics 101 Series — The ‘Share’ & ‘Act’ Phase

Data Analytics 101 Series — The ‘Share’ & ‘Act’ Phase

What happens when the analysis phase is complete? Is the process complete? No. The analysis performed is useless unless it is shared with…
Go to the profile of Adith Narasimhan Kumar
Adith Narasimhan Kumar
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