DeepLearning Frameworks

TensorFlow is an end-to-end platform for machine learning. It has a comprehensive, flexible ecosystem of tools and libraries to build and deploy machine learning-powered applications.


Keras is a highly-productive deep learning interface running on top of TensorFlow. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.

PyTorch is a machine and deep learning framework used primarily for natural language processing and computer vision applications. In the community, PyTorch has grown as a research-first library.


MXNet is a lean, flexible and scalable deep learning framework suited for flexible research, prototyping, and production of deep learning applications.

FastAI is a deep learning library providing high and low-level components to achieve state-of-the-art results in standard deep learning domains. FastAI sits on top of the PyTorch framework.

https://www.nvidia.com/en-us/gtc/keynote

I registered myself .. pls do this is very interesting

Mathematics for Machine Learning

AI and DataScience Reources - USeful

Samples, Reference Architectures & Best Practices : https://github.com/microsoft/AI (Microsoft)

I have listed very import weblinks. Basically these resources and already explained in video as well. Please check out if you want to learn python in depth.

Django RestFramework ViewSet.. Simple way to define Views in DRF.

Standard Error Estimate on Regression Problems in ML

👉🏼👨🏻‍💻
Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning.

🔸 Purpose :
It enables creating deep learning pipelines with just a few lines of code.

🔸 Compatibility
It is compatible with Python 3.6+ and PyTorch 1.3+ versions.

🔸 Other features
It enables creating configuration files for storing the model’s hyperparameters.

It supports some of the best deep learning R&D practices such as Stochastic Weight Averaging (SWA), Ranger optimizer, one-cycle training, fp16 precision, distributed training and so on.

🔸Installation
pip install -U catalyst


Learn more about Deep learning pipelines
🦾🤓

content by (iampython.com - A Python Opensource Community For Tech Practitioners )

GraphQL is a great technology for building APIs and it is very useful for exposing the output from machine learning models and other calculations.

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👉🏼👨🏻‍💻 🔸 Cardea, a software system built by researchers and software engineers at MIT's Data to AI Lab (DAI Lab)
One-stop machine learning platform turns health care data into insights

Github Link :
https://github.com/MLBazaar/Cardea

Django Rest-framework (Half the way to go)


Code
https://github.com/iampython-team/DjangoRestFramework

Doc
https://github.com/iampython-team/DjangoRestFramework/blob/master/DRF_Doc.pdf

Video Playlist
https://youtube.com/playlist?list=PLw945x_O7SHqseUNT1V_6EpKlon-mxylb