Robot development with Jupyter
Wolf Vollprecht : https://medium.com/@wolfv/robot-development-with-jupyter-ddae16d4e688
#Robotics #Jupyter #Python
Wolf Vollprecht : https://medium.com/@wolfv/robot-development-with-jupyter-ddae16d4e688
#Robotics #Jupyter #Python
Medium
Robot development with Jupyter
This post shows available tools to build browser based, advanced visualizations in Jupyter Notebooks for ROS and standalone web apps using
Practical AI
A practical approach to machine learning to enable everyone to learn, explore and build : https://github.com/practicalAI/practicalAI
#Python #Numpy #Pandas
A practical approach to machine learning to enable everyone to learn, explore and build : https://github.com/practicalAI/practicalAI
#Python #Numpy #Pandas
GitHub
GitHub - GokuMohandas/Made-With-ML: Learn how to design, develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications. - GokuMohandas/Made-With-ML
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
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
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
GitHub
jkoutsikakis/pytorch-wrapper
Provides a systematic and extensible way to build, train, evaluate, and tune deep learning models using PyTorch. - jkoutsikakis/pytorch-wrapper
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
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
Medium
Exploring the London Stock Exchange using Graph Networks in Neo4j — Part 1
Back in December I attended an event called ‘Network Science in Financial Service’ at the Alan Turing Institute. I found the approach of…
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
Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning" : https://github.com/ctgk/PRML
#DeepLearning #MachineLearning #Python
GitHub
GitHub - ctgk/PRML: PRML algorithms implemented in Python
PRML algorithms implemented in Python. Contribute to ctgk/PRML development by creating an account on GitHub.
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
Sebastian Theiler: https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a
#ReinforcementLearning #MachineLearning #Python #TensorFlow
Medium
Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)
And scoring 350+ by implementing extensions such as double dueling DQN and prioritized experience replay
400+ textbooks free to download
CS books on Python, deep learning, data science & AI.
Springer: http://bit.ly/SpringerCS
#DeepLearning #Python #Programming #Coding
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
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
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
mit6874.github.io
Spring 2021 6.874 Computational Systems Biology: Deep Learning in the Life Sciences
Course materials and notes for MIT class 6.802 / 6.874 / 20.390 / 20.490 / HST.506 Computational Systems Biology: Deep Learning in the Life Sciences