khanimar Goto Github PK

2015

Public material for CS109

2015lab1

alewrap

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

arctic-captions

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

awesome-public-datasets

A topic-centric list of HQ open datasets in public domains. PR ☛☛☛

awesome-rl

Reinforcement learning resources curated

bi-prediction-of-marketing-campaign-performance

The main idea of this report is to provide the useful insights from the given data as well as creating the best possible forecasting model for a marketing campaign for a new product and to predict the probabilities of completion.

caffe

Caffe: a fast open framework for deep learning.

chalearn-2021-lap

Code and pre-trained models for my submission to the ChaLearn 2021 LAP challenge.

cicd-demo-1

clevr-iep

Inferring and Executing Programs for Visual Reasoning

coco-caption

convnet-benchmarks

Easy benchmarking of all public open-source implementations of convnets

crayon

A language-agnostic interface to TensorBoard

cs231n

cs231n.github.io

Public facing notes page

dat4

General Assembly's Data Science course in Washington, DC

deep-fbanks

Deep Filter Banks for Texture Recognition, Description and Segmentation (CVPR15)

deep-learning-boot-camp

A nonprofit community run, 5-day Deep Learning Bootcamp

deep-planning

An application of stacked denoising autoencoders to multi-modal (images and audio) abstract feature discovery

densenet

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

dlib

A toolkit for making real world machine learning and data analysis applications in C++

examples

Many examples of many features of many software packages

extension-ffi

Examples of C extensions for PyTorch

fast-neural-style

Feedforward style transfer

fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

fm_c_plusplus

Fast and Efficient C++ implementation of the Fast Marching (FM) integrator for non-convex domains

heart_attack_prediction

The problem that this project is going to anlyze is heart attack. The final objectives of this project is to use the provided data to perform the following tasks: Performing EDA to obtain useful primary insights Using the provided set of input features and their corresponding labels to predict the likelihood of a potential heart attack in each sample. In other words, the problem is to provide a binary classifier that is able to predict if a person is prone to a heart attack or not.