GitHub - prateekgrover-in/Machine-Learning-Tutorials: Collection of all notebooks and files used in implementing DL algorithms and structures.

Collection of all notebooks and files used in implementing ML and DL algorithms on a variety of datasets.

Exploratory Data Analysis on Metropolitan Musuem Dataset : Cleaning the dataset, and performing EDA using Seaborn Library.

Linear Regression : Understanding Linear Regression using Iris Dataset

Implementing a NN from Scratch and Particle Swarm Optimization : As part of assignment in course BITS F312: Neural Networks and Fuzzy Logic

Training a VGG-16 Network on CIFAR - 100 Training a Deep Neural Network (VGG 16) for Image Classification on CIFAR - 100 Dataset.

Trying Style Transfer using Pretrained Model Using pretrained VGG-19 to Modify an image according to another style image.

Using U-Nets for Segmentation of Brain Tumour in MRI Scans : Using BRATS 2015 Dataset Understanding Variational Autoencoders: Creating a distribution of MNIST Dataset through the use of VAE.

Understanding Deep Convolutional Generative Adversarial Networks: Using CelebA Dataset to generate faces using DCGANs.

Name		Name	Last commit message	Last commit date
Latest commit History 36 Commits
Brain-Tumour-Segmentation-in-MRI.ipynb		Brain-Tumour-Segmentation-in-MRI.ipynb
CapsuleNet.py		CapsuleNet.py
EDA-Metropolitan-Museum-Dataset.ipynb		EDA-Metropolitan-Museum-Dataset.ipynb
GANCelebA.ipynb		GANCelebA.ipynb
Linear-Regression.ipynb		Linear-Regression.ipynb
Neural-Networks-and-Particle-Swarm-Optimization.ipynb		Neural-Networks-and-Particle-Swarm-Optimization.ipynb
README.md		README.md
Style_Transfer.ipynb		Style_Transfer.ipynb
VGG16_CIFAR_100.ipynb		VGG16_CIFAR_100.ipynb
Variational_Autoencoders.ipynb		Variational_Autoencoders.ipynb

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