Bhagirath Deshani

@bhagirath07

ML Engineer | Data Scientist

AustraliaJoined June 2022

About

Greetings.

I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.

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Bhagirath Deshani 's blogs

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BDBhagirath Deshani bhagirathkd.hashnode.devFeb 18, 2023 · 1 min read

#117 Machine Learning & Data Science Challenge 117

What are the benefits of TensorFlow over other libraries? The following benefits are: • Scalability • Visualisation of Data • Debugging facility • Pipelining

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BDBhagirath Deshani bhagirathkd.hashnode.devFeb 17, 2023 · 1 min read

#116 Machine Learning & Data Science Challenge 116

What is the difference between CNN and RNN? CNN (Convolutional Neural Network): • Best suited for spatial data like images. • CNN is powerful compared to RNN. • This network takes a fixed type of inputs and outputs. • These are ideal for video and im...

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BDBhagirath Deshani bhagirathkd.hashnode.devFeb 17, 2023 · 1 min read

#115 Machine Learning & Data Science Challenge 115

What is a Pooling Layer? It is generally used in reducing the spatial dimensions and not depth, on a convolutional neural network model.

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BDBhagirath Deshani bhagirathkd.hashnode.devFeb 16, 2023 · 1 min read

#114 Machine Learning & Data Science Challenge 114

What is Keras? It is an Open Source Neural Network library written in Python that runs on top of Theano or TensorFlow. It is designed to be modular, fast, and easy to use. It was developed by François Chollet, a Google engineer

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BDBhagirath Deshani bhagirathkd.hashnode.devFeb 16, 2023 · 1 min read

#113 Machine Learning & Data Science Challenge 113

What are the very important steps of Tensorflow architecture? There are three main steps in the Tensorflow architecture are: • Pre-process the Data • Build a Model • Train and estimate the model

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Bhagirath Deshani

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Recently published

#117 Machine Learning & Data Science Challenge 117

#116 Machine Learning & Data Science Challenge 116

#115 Machine Learning & Data Science Challenge 115

#114 Machine Learning & Data Science Challenge 114

#113 Machine Learning & Data Science Challenge 113

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Bhagirath Deshani

About

Available for

Bhagirath Deshani 's blogs

Recently published

#117 Machine Learning & Data Science Challenge 117

#116 Machine Learning & Data Science Challenge 116

#115 Machine Learning & Data Science Challenge 115

#114 Machine Learning & Data Science Challenge 114

#113 Machine Learning & Data Science Challenge 113