Introducing TensorBoard.dev: a new way to share your ML experiment results
Blog by Gal Oshri : https://blog.tensorflow.org/2019/12/introducing-tensorboarddev-new-way-to.html
#ArtificialIntelligence #MachineLearning #TensorFlow

PhiFlow
Research-oriented differentiable fluid simulation framework : https://github.com/tum-pbs/PhiFlow
#ArtificialIntelligence #MachineLearning #TensorFlow

"Differentiable Convex Optimization Layers"
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019

"Differentiable Convex Optimization Layers"
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019

Lucid
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab

Machine Learning Unlocks Library of The Human Brain. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience


https://thetartan.org/2019/11/11/scitech/brain-thoughts

DDSP: Differentiable Digital Signal Processing
Engel et al.
⌨️ Blog: https://magenta.tensorflow.org/ddsp
🎵 Examples: https://g.co/magenta/ddsp-examples
⏯ Colab: https://g.co/magenta/ddsp-demo
💻 Code: https://github.com/magenta/ddsp
📝 Paper: https://g.co/magenta/ddsp-paper
#ArtificialIntelligence #TensorFlow #SignalProcessing

Colab Pro https://colab.research.google.com/signup
#Colab #DeepLearning #TensorFlow

PyTorch Wrapper version 1.1 is out!

New Features:

- Samplers for smart batching based on text length for faster training.

- Loss and Evaluation wrappers for token prediction tasks.

- New nn.modules for attention based models.

- Support for multi GPU training / evaluation / prediction.

- Verbose argument in system's methods.

- Examples using Transformer based models like BERT for text classification.

Check it out in the following links:

install with: pip install pytorch-wrapper

GitHub: https://github.com/jkoutsikakis/pytorch-wrapper

docs: https://pytorch-wrapper.readthedocs.io/en/latest/

examples: https://github.com/jkouts…/pytorch-wrapper/…/master/examples

#DeepLearning #PyTorch #NeuralNetworks #MachineLearning #DataScience #python #TensorFlow

Removing people from complex backgrounds in real time using TensorFlow.js in the web browser
Jason Mayes, GitHub: https://github.com/jasonmayes/Real-Time-Person-Removal
#DeepLearning #TensorFlow #TensorFlowJS

TRFL : TensorFlow Reinforcement Learning
A library of reinforcement learning building blocks
By DeepMind: https://github.com/deepmind/trfl
#DeepLearning #TensorFlow #ReinforcementLearning

TensorBoard in Jupyter Notebooks
Colab Notebook: https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/tensorboard_in_notebooks.ipynb
#TensorFlow #TensorBoard #Jupyter #Notebook

TensorFlow Tutorials
https://www.tensorflow.org/tutorials
#TensorFlow #Tutorials #DeepLearning

Lucid
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab

Affinity: Deep learning library for molecular geometry
Korablyov et al., Molecular Machines Group, MIT Media Lab: https://affinity.mit.edu
#DeepLearning #DrugDiscovery #TensorFlow

RLlib: Scalable Reinforcement Learning
RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.
The Ray Team : https://ray.readthedocs.io/en/latest/rllib.html
#ReinforcementLearning #PyTorch #TensorFlow

Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)
Sebastian Theiler: https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a
#ReinforcementLearning #MachineLearning #Python #TensorFlow

Enzyme, a compiler plug-in for importing foreign code into systems like TensorFlow & PyTorch without having to rewrite it. v/@MIT_CSAIL

Paper: https://bit.ly/EnzymePDF

More: https://bit.ly/EnzymeML

#ML #MachineLearning #PyTorch #TensorFlowJS #NeurIPS #tensorflow #AI

Deep Learning in Life Sciences
by Massachusetts Institute of Technology (MIT)

Course Site: https://mit6874.github.io/

Lecture Videos: https://youtube.com/playlist?list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB

We will explore both conventional and deep learning approaches to key problems in the life sciences, comparing and contrasting their power and limits. Our aim is to enable you to evaluate a wide variety of solutions to key problems you will face in this rapidly developing field, and enable you to execute on new enabling solutions that can have large impact.
As part of the subject you will become an expert in using modern cloud resources to implement your solutions to challenging problems, first in problem sets that span a carefully chosen set of tasks, and then in an independent project.
You will be programming using Python 3 and TensorFlow 2 in Jupyter Notebooks on the Google Cloud, a nod to the importance of carefully documenting your work so it can be precisely reproduced by others.

#artificialintelligence #deeplearning #tensorflow #python #biology #lifescience

Coding TensorFlow
Laurence Moroney: https://www.youtube.com/playlist?list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx
#ArtificialIntelligence #DeepLearning #Tensorflow