Name: Arunkumar Venkataramanan
Type: User
Company: @Deep-Brainz, Tech Founder and Product Leader
Bio: Tech Product Manager and Architect | Serial Entrepreneur, AI Innovator, Founder, CEO, Chief Technologist, Product Leader at @Deep-Brainz AI and Stealth Startup
Twitter: arunkumar_bvr
Location: Bangalore, India
Blog: https://arunkumarramanan.github.io
Arunkumar Venkataramanan's Projects
Python best practices guidebook, written for Humans.
The "Python Machine Learning (1st edition)" book code repository and info resource
100+ Python challenging programming exercises
A short guide on features of Python 3 with examples (updated for python 3.7)
Jupyter notebook for Udemy course: Python data science and machine learning bootcamp
Python Data Science Handbook: full text in Jupyter Notebooks
Problem Solving with Algorithms and Data Structures using Python
Data Structures package for Problem Solving with Algorithms and Data Structures using Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
Check out improved:
Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)
Minicourse in Deep Learning with PyTorch
A CV Toolkit
Simple examples to introduce PyTorch
pytorch1.0 updated. Support cpu test and demo.
PyTorch implementations of Generative Adversarial Networks.
PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more
Pytorch starter kit for Kaggle competitions
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
A PyTorch implementation of MobileNet V2 architecture and pretrained model.
Use Watson Studio and PyTorch to create a machine learning model to recognize hand-written digits
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
A PyTorch implementation of Google AI's BERT model with script to load Google's pre-trained models
Kaggle | 9th place single model solution for TGS Salt Identification Challenge
pytorch-seq2seq is a framework for sequence-to-sequence (seq2seq) models in PyTorch.
PyTorch Tutorial for Deep Learning Researchers
Minimal PyTorch implementation of YOLOv3