Harsh Yadav – Medium

Harsh Yadav

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Harsh Yadav
in
Towards Data Science

Dropout in Neural Networks

Dropout layers have been the go-to method to reduce the overfitting of neural networks. It is the underworld king of regularisation in the…

Jul 5, 2022

Dropout in Neural Networks

Jul 5, 2022

Harsh Yadav
in
Towards Data Science

Residual Blocks in Deep Learning

Residual block, first introduced in the ResNet paper solves the neural network degradation problem

Jul 11, 2022

Residual Blocks in Deep Learning

Jul 11, 2022

Harsh Yadav
in
Towards Data Science

Computer Vision: Convolution Basics

A deep dive into the basic cell of many neural networks.

Jul 5, 2022

Computer Vision: Convolution Basics

Jul 5, 2022

Harsh Yadav
in
Towards Data Science

Preserving Data Privacy in Deep Learning | Part 3

Implementation of Federated Learning with NON-Independent and Identically Distributed (non-IID) dataset.

Aug 11, 2020

Preserving Data Privacy in Deep Learning | Part 3

Aug 11, 2020

Harsh Yadav
in
Towards Data Science

Preserving Data Privacy in Deep Learning | Part 2

Distribution of a balanced dataset into a non-IID/real-world dataset, further divided into clients for federated learning.

Jul 14, 2020

Preserving Data Privacy in Deep Learning | Part 2

Jul 14, 2020

Harsh Yadav
in
Towards Data Science

Preserving Data Privacy in Deep Learning | Part 1

Understanding the basics of Federated Learning and its implementation using PyTorch

Jul 6, 2020

Preserving Data Privacy in Deep Learning | Part 1

Jul 6, 2020

Harsh Yadav

Harsh Yadav

Deep Learning enthusiast, with prior experience at Publicis Sapient, National University of Singapore, and SUSTech China.

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