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#100DaysOfML

Dedicating 100 Days to Machine Learning and its Application

Day 1

(3/4/2020)

Horse-Human Classifier

Implemented a Neural Network to classify a given image as either a Horse or a Human.

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Day 2

(4/4/2020)

Neural Style Transfer

Implemented a Neural Network to Transfer Style from one image to another.

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Day 3

(6/4/2020)

Gradient Descent

Implemented Gradient Descent Algorithm using Traditional methods. Done as a part of my Nanodegree.

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Day 4

(8/4/2020)

Sentiment Analysis

Implemented Sentiment Analusis on Movie Review Dataset using Neural Networks based on traditional approach.

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Day 5

(12/4/2020)

Digits MNIST - Pytorch

Implemented image classification of handwritten digits of MNIST Dataset using the Pytorch framework.

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Day 6

(13/4/2020)

Fashion MNIST - Pytorch

Implemented image classification of Fashion MNIST Dataset using the Pytorch framework.

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Day 7

(14/4/2020)

CIFAR-10 Classification

Implemented image classification of CIFAR-10 Dataset using the Pytorch framework.

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Day 8

(15/4/2020)

AutoEncoders

Implemented Autoencoding process on MNIST Dataset using Pytorch.

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Day 9

(17/4/2020)

Denoising Images

Implemented a Autoencoder to Denoise an image.

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Day 10

(18/4/2020)

Simple RNN

Implemented a basic RNN to predict time series.

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Day 11

(19/4/2020)

Char-RNN

Implemented a Char-RNN using the LSTM Architecture to generate new texts.

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Day 12

(22/4/2020)

Generate TV Script

Implemented a RNN model to Generate TV Scripts.

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Day 13

(24/4/2020)

Day-Night Classifier

Implemented a Day-Night Image classifier with OpenCV.

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Day 14

(25/4/2020)

Harr Cascades

Implemented Face and Eye Detection using Harr Cascades Algorithm in OpenCV.

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Day 15

(26/4/2020)

Harris Corner Detection

Implemented Harris Corner Detection algorithm using OpenCV.

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