Gaining Insights in a Simulated Marketplace with Machine Learning at Uber
#artificialintelligence
https://eng.uber.com/simulated-marketplace/
#artificialintelligence
https://eng.uber.com/simulated-marketplace/
Was this Google Executive deeply misinformed or lying in the New York Times?
https://www.fast.ai/2019/05/28/google-nyt-mohan/
https://www.fast.ai/2019/05/28/google-nyt-mohan/
www.fast.ai
Was this Google Executive deeply misinformed or lying in the New York Times?
Making neural nets uncool again
Rank-consistent Ordinal Regression for Neural Networks
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
arXiv.org
Rank consistent ordinal regression for neural networks with...
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category...
Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
Trending deep learning Github repositories
Here's a list of top 100 deep learning Github trending repositories sorted by the number of stars gained on a specific day.
https://github.com/mbadry1/Trending-Deep-Learning
Here's a list of top 100 deep learning Github trending repositories sorted by the number of stars gained on a specific day.
https://github.com/mbadry1/Trending-Deep-Learning
GitHub
GitHub - mbadry1/Trending-Deep-Learning: Top 100 trending deep learning repositories sorted by the number of stars gained on a…
Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day. - mbadry1/Trending-Deep-Learning
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
https://research.fb.com/publications/lightweight-and-efficient-neural-natural-language-processing-with-quaternion-networks/
https://research.fb.com/publications/lightweight-and-efficient-neural-natural-language-processing-with-quaternion-networks/
Facebook Research
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.
Types Of Artificial Intelligence | Artificial Intelligence Explained | What is AI?
https://www.youtube.com/watch?v=y5swZ2Q_lBw
https://www.youtube.com/watch?v=y5swZ2Q_lBw
YouTube
Types Of Artificial Intelligence | Artificial Intelligence Explained | What is AI? | Edureka
🔥Post Graduate Program in Generative AI and ML: https://www.edureka.co/executive-programs/pgp-generative-ai-machine-learning-certification-training
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters-program/generative-ai-prompt-engineering…
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters-program/generative-ai-prompt-engineering…
Learning Latent Dynamics for Planning from Pixels
Hafner et al.: https://planetrl.github.io/
Hafner et al.: https://planetrl.github.io/
PlaNet solves control tasks from pixels by planning in latent space.
Learning Latent Dynamics for Planning from Pixels
Learning Representations by Maximizing Mutual Information Across Views
Article: https://arxiv.org/abs/1906.00910
Code:
https://github.com/Philip-Bachman/amdim-public
Article: https://arxiv.org/abs/1906.00910
Code:
https://github.com/Philip-Bachman/amdim-public
arXiv.org
Learning Representations by Maximizing Mutual Information Across Views
We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could...
ARTIFICIAL INTELLIGENCE 101
AI 101 CheatSheet: http://www.montreal.ai/ai4all.pdf
Code: https://montrealartificialintelligence.com/academy/#Curated-Open-Source-Codes-Implementations-and-Science
AI 101 CheatSheet: http://www.montreal.ai/ai4all.pdf
Code: https://montrealartificialintelligence.com/academy/#Curated-Open-Source-Codes-Implementations-and-Science
MIT Press and Harvard Data Science Initiative launch the Harvard Data Science Review
http://news.mit.edu/2019/mit-press-harvard-data-science-initiative-launch-harvard-data-science-review-0715
http://news.mit.edu/2019/mit-press-harvard-data-science-initiative-launch-harvard-data-science-review-0715
MIT News
MIT Press and Harvard Data Science Initiative launch the Harvard Data Science Review
MIT Press and the Harvard Data Science Initiative have launched the Harvard Data Science Review. An open-access journal published by MIT Press and hosted online via the PubPub platform, HDSR will feature leading global thinkers in the field of data science.
Introduction to Reinforcement Learning : Markov-Decision Process
https://towardsdatascience.com/introduction-to-reinforcement-learning-markov-decision-process-44c533ebf8da
https://towardsdatascience.com/introduction-to-reinforcement-learning-markov-decision-process-44c533ebf8da
Medium
Reinforcement Learning : Markov-Decision Process (Part 1)
In a typical Reinforcement Learning (RL) problem, there is a learner and a decision maker called agent and the surrounding with which it…
Microsoft makes it easier to build popular language representation model BERT at large scale
https://azure.microsoft.com/en-us/blog/microsoft-makes-it-easier-to-build-popular-language-representation-model-bert-at-large-scale/
https://azure.microsoft.com/en-us/blog/microsoft-makes-it-easier-to-build-popular-language-representation-model-bert-at-large-scale/
Microsoft Azure Blog
Microsoft makes it easier to build popular language representation model BERT at large scale | Microsoft Azure Blog
Today we are announcing the open sourcing of our recipe to pre-train BERT (Bidirectional Encoder Representations from Transformers) built by the Bing team, including code that works on Azure Machine Learning, so that customers can unlock the power of training…