Name: Jojo John Moolayil
Type: User
Company: Amazon Web Services
Bio: Published Author, Data Science and Machine Learning professional.
Works at Amazon Web Services Vancouver, BC
Location: Vancouver, BC
Blog: http://jojomoolayil.com/
Jojo John Moolayil's Projects
DCGAN in Pytorch trained on a custom architecture dataset.
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
Deep learning models to identify clickbaits taking content into consideration
Hadoop library for large-scale data processing, now an Apache Incubator project
A collection of Hive UDFs
Source code for all exercises for the book - Deep Learning with Python
Convolutional Neural Networks to predict the aesthetic and technical quality of images.
Keras implementation of residual dense networks and GANs for Image Super-Resolution.
Image classification for everyone.
Team o_O solution for the Kaggle Diabetic Retinopathy Detection Challenge
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
Magenta: Music and Art Generation with Machine Intelligence
Source code for 'Practical Machine Learning with Python' by Dipanjan Sarkar, Raghav Bali, and Tushar Sharma
A couple of scripts to illustrate how to do CNNs and RNNs in PyTorch
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Creating a repository to host basic PyTorch tutorials and sample
Recurrent Neural Networks - A Short TensorFlow Tutorial
Creating a repository to collate all learning links for R and Python for Machine Learning
Collating the codes and the data required for hands on exercises in the book : Smarter Decisions - The Intersection of IoT and Decision Science
StackNet is a computational, scalable and analytical Meta modelling framework
Getting Started With R
Added R Codes for pulling data from worldweatheronline.com