How to Use Discretization Transforms for Machine Learning - Machine Learning
https://machinelearningmastery.com/discretization-transforms-for-machine-learning/
https://machinelearningmastery.com/discretization-transforms-for-machine-learning/
MachineLearningMastery.com
How to Use Discretization Transforms for Machine Learning - MachineLearningMastery.com
Numerical input variables may have a highly skewed or non-standard distribution.
This could be caused by outliers in the data, multi-modal distributions, highly exponential distributions, and more.
Many machine learning algorithms prefer or perform…
This could be caused by outliers in the data, multi-modal distributions, highly exponential distributions, and more.
Many machine learning algorithms prefer or perform…
Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
https://cvit.iiit.ac.in/research/projects/cvit-projects/speaking-by-observing-lip-movements
https://cvit.iiit.ac.in/research/projects/cvit-projects/speaking-by-observing-lip-movements
cvit.iiit.ac.in
Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
Centre for Visual Information Technology (CVIT) is a research centre at International Institute of Information Technology, Hyderabad.
Recursive Feature Elimination (RFE) for Feature Selection in Python
https://machinelearningmastery.com/rfe-feature-selection-in-python/
https://machinelearningmastery.com/rfe-feature-selection-in-python/
Point2Mesh in PyTorch
Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud.
https://ranahanocka.github.io/point2mesh/
Github: https://github.com/ranahanocka/point2mesh
Paper: https://arxiv.org/abs/2005.11084
Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud.
https://ranahanocka.github.io/point2mesh/
Github: https://github.com/ranahanocka/point2mesh
Paper: https://arxiv.org/abs/2005.11084
GitHub
GitHub - ranahanocka/point2mesh: Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]
Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020] - ranahanocka/point2mesh
How to Use Polynomial Feature Transforms for Machine Learning
https://machinelearningmastery.com/polynomial-features-transforms-for-machine-learning/
https://machinelearningmastery.com/polynomial-features-transforms-for-machine-learning/
MachineLearningMastery.com
How to Use Polynomial Feature Transforms for Machine Learning - MachineLearningMastery.com
Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified and modeled by a learning algorithm. Another approach is to engineer new features that expose these interactions…
GPT-3: Language Models are Few-Shot Learners
Github: https://github.com/openai/gpt-3
Paper: https://arxiv.org/abs/2005.14165v1
Github: https://github.com/openai/gpt-3
Paper: https://arxiv.org/abs/2005.14165v1
GitHub
GitHub - openai/gpt-3: GPT-3: Language Models are Few-Shot Learners
GPT-3: Language Models are Few-Shot Learners. Contribute to openai/gpt-3 development by creating an account on GitHub.
DADS: Unsupervised Reinforcement Learning for Skill Discovery
https://ai.googleblog.com/2020/05/dads-unsupervised-reinforcement.html
https://ai.googleblog.com/2020/05/dads-unsupervised-reinforcement.html
blog.research.google
DADS: Unsupervised Reinforcement Learning for Skill Discovery
Finding Cross-Lingual Syntax in Multilingual BERT
https://ai.stanford.edu/blog/finding-crosslingual-syntax/
https://ai.stanford.edu/blog/finding-crosslingual-syntax/
Eisen
A simple, fast and robust deep learning development framework
https://github.com/eisen-ai/eisen-core
https://eisen-ai.github.io/
A simple, fast and robust deep learning development framework
https://github.com/eisen-ai/eisen-core
https://eisen-ai.github.io/
GitHub
eisen-ai/eisen-core
Core functionality of Eisen. Contribute to eisen-ai/eisen-core development by creating an account on GitHub.
How to Perform Feature Selection With Numerical Input Data - Machine Learning Mastery
https://machinelearningmastery.com/feature-selection-with-numerical-input-data/
https://machinelearningmastery.com/feature-selection-with-numerical-input-data/
MachineLearningMastery.com
How to Perform Feature Selection With Numerical Input Data - MachineLearningMastery.com
Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with real-valued input and output data, such as using the Pearson’s…
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
https://www.catalyzex.com/paper/arxiv:2003.06754
https://www.catalyzex.com/paper/arxiv:2003.06754
How Detectron2 helps make mines safer and more efficient
https://ai.facebook.com/blog/how-detectron2-helps-make-mines-safer-and-more-efficient/
Article: https://medium.com/pytorch/how-datarock-is-using-pytorch-for-more-intelligent-decision-making-d5d1694ba170
https://ai.facebook.com/blog/how-detectron2-helps-make-mines-safer-and-more-efficient/
Article: https://medium.com/pytorch/how-datarock-is-using-pytorch-for-more-intelligent-decision-making-d5d1694ba170
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How Detectron2 helps make mines safer and more efficient
Datarock, a SaaS solution targeted at the mining industry, leverages various PyTorch tools, including PyTorch-based object detection library Detectron2, to train ML models with geological imagery.