janardhanv's repositories
system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Julia
Algorithms implemented in the Julia programming language. We're collaborating with the Humans of Julia community!
coding-interview-university
A complete computer science study plan to become a software engineer.
allennlp
An open-source NLP research library, built on PyTorch.
cloud-ml-examples
A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud
helm
The Kubernetes Package Manager
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
jupyter
Jupyter metapackage for installation, docs and chat
Questgen.ai
Question generation using state-of-the-art Natural Language Processing algorithms
question_generation
Neural question generation using transformers
dovpanda
Directions overlay for working with pandas in an analysis environment
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
mlflow-examples
Basic MLflow examples
pycaret
An open-source, low-code machine learning library in Python
data-science-blogs
A curated list of data science blogs
M5-methods
Data, Benchmarks, and methods submitted to the M5 forecasting competition
pytorch-Deep-Learning
Deep Learning (with PyTorch)
Heroku_Deployment
Heroku is a container-based cloud Platform as a Service (PaaS). Developers use Heroku to deploy, manage, and scale modern apps. Our platform is elegant, flexible, and easy to use, offering developers the simplest path to getting their apps to market.
madewithml
Learn how to responsibly deliver value with applied ML.
Machine-Learning-1
In this repository I have done the implementation of all the machine learning concepts.
fullstack.ai
End-to-end machine learning project showing key aspects of developing and deploying ML driven application
research-notes
Stuff I find useful, mostly on research and computer vision.
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Hyperparameter_Tuning_Techniques
Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. Hyperparameters are crucial as they control the overall behavior of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results.
time-series-forecasting-with-python
A use-case focused tutorial for time series forecasting with python
TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.