ArviZ: Exploratory analysis of Bayesian models
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python

ArviZ: Exploratory analysis of Bayesian models
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python

MRI Images Created by #AI Could Help Train #DeepLearning Models. https://healthitanalytics.com/news/mri-images-created-by-ai-could-help-train-deep-learning-models #BigData #Analytics #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience

Uber’s EvoGrad is a dev library for evolutionary algorithms
Blog by Kyle Wiggers: https://venturebeat.com/2019/07/22/ubers-evograd-is-a-dev-library-for-evolutionary-algorithms/
#EvolutionaryAlgorithms #NaturalEvolutionStrategies #Python

PracticalAI

A practical approach to learning machine learning

GitHub : https://github.com/GokuMohandas/practicalAI

- 📚 Notebooks on topics from basic Python to advanced deep learning techniques w/ #PyTorch

- 🖥️ Run everything using #Colab : https://colab.research.google.com/github/GokuMohandas/practicalAI/

#deeplearning #python #machinelearning #reinforcementlearning

Even young children when they look at a picture, not only identify objects such as "cat," "book," "chair." but also narrate the context and probably caption them. Now, computers are getting smart enough to do that too. In this TED talk, computer vision expert Fei-Fei Li describes the state of the art — including the database of 15 million photos her team built to "teach" a computer to understand pictures — and the key insights yet to come.#alintelligence #deeplearning #datascience #machinelearning #ML #Algorithm #Python #R #professional #industry #bigdata #ai #community #workforce

https://www.youtube.com/watch?v=40riCqvRoMs

Amazing work on generative adversarial networks by Tero Karras, Samuli Laine and Timo Aila of NVIDIA. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. #education #professionals #careers #artificialintelligence #deeplearning #datascience #machinelearning #ML #Algorithm #Python #R #professional #industry #bigdata #ai #community #workforce

The research paper is available : http://stylegan.xyz/paper

Video link : https://www.youtube.com/watch?v=kSLJriaOumA

Neural Network Distiller: A Python Package For DNN Compression Research
Zmora et al.: https://arxiv.org/abs/1910.12232
#DeepLearning #MachineLearning #Python

PyRoboLearn: A Python Framework for Robot Learning Practitioners

Delhaisse et al.: https://robotlearn.github.io/pyrobolearn/

#ArtificialIntelligence #Python #Robotics

This is an exhaustive list of Monte Carlo tree search papers from major conferences including NIPS, ICML, and AAAI. Some of them with publicly available implementations.

https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers

#datascience #machinelearning #deeplearning #python #ai #analytics #datamining

Robot development with Jupyter
Wolf Vollprecht : https://medium.com/@wolfv/robot-development-with-jupyter-ddae16d4e688
#Robotics #Jupyter #Python

Practical AI
A practical approach to machine learning to enable everyone to learn, explore and build : https://github.com/practicalAI/practicalAI
#Python #Numpy #Pandas

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


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

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

Exploring the London Stock Exchange using Graph Networks in Neo4j — Part 1
A PRACTICAL GUIDE, USING GRAPH DATABASES, PYTHON AND DOCKER
Daniel Sharp: https://medium.com/applied-data-science/exploring-stocks-in-the-london-stock-exchange-using-graph-networks-in-neo4j-part-1-58a5455084ab

#Graph #Database #Python #Docker

PRML algorithms implemented in Python
Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning" : https://github.com/ctgk/PRML
#DeepLearning #MachineLearning #Python

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

400+ textbooks free to download
CS books on Python, deep learning, data science & AI.
Springer: http://bit.ly/SpringerCS
#DeepLearning #Python #Programming #Coding

MiniTorch
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python

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