Towards Data Science: Berlin House Rental Market Analysis
http://bit.ly/2QrUGq5
Am I paying a fair price?Continue reading on Towards Data Science »
http://bit.ly/2QrUGq5
Am I paying a fair price?Continue reading on Towards Data Science »
Towards Data Science: Knowledge Graphs for Social Good Workshop: Helping the United Nations achieve Sustainable…
http://bit.ly/2WmOfIR
The UN Sustainable Development Goals (SDGs), set in 2015, are a collection of 17 shared global goals intended to improve the health and…Continue reading on Towards Data Science »
http://bit.ly/2WmOfIR
The UN Sustainable Development Goals (SDGs), set in 2015, are a collection of 17 shared global goals intended to improve the health and…Continue reading on Towards Data Science »
Towards Data Science: The Hottest Topics in Machine Learning
http://bit.ly/2wj6yE9
Understand Topic Modeling and Latent Dirichlet Allocation (LDA) in PythonContinue reading on Towards Data Science »
http://bit.ly/2wj6yE9
Understand Topic Modeling and Latent Dirichlet Allocation (LDA) in PythonContinue reading on Towards Data Science »
Towards Data Science: The Basics: Time Series and Seasonal Decomposition
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When are time series techniques appropriate and how do you perform decomposition?Continue reading on Towards Data Science »
http://bit.ly/2Qu3pIr
When are time series techniques appropriate and how do you perform decomposition?Continue reading on Towards Data Science »
Towards Data Science: Revisiting a Data Science Totem ~ Variables
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Elevate your data science projects by following these variables’ mapContinue reading on Towards Data Science »
http://bit.ly/393A9Pk
Elevate your data science projects by following these variables’ mapContinue reading on Towards Data Science »
Towards Data Science: 7 Habits of Highly Effective Programmers
http://bit.ly/2UmgSDg
About 40 to 45 percent of what we programmers do every day is something we do on autopilotContinue reading on Towards Data Science »
http://bit.ly/2UmgSDg
About 40 to 45 percent of what we programmers do every day is something we do on autopilotContinue reading on Towards Data Science »
Monitoring Machine Learning Models in Production
Once you deploy your model to production, the work isn't over. This guide shows how to monitor your models, why that matters, and how to go about implementing your own ML monitoring solutions.
https://bit.ly/35lfaaj
Once you deploy your model to production, the work isn't over. This guide shows how to monitor your models, why that matters, and how to go about implementing your own ML monitoring solutions.
https://bit.ly/35lfaaj
YOLO v4 released! - Improve speed and better object detection accurately
Compared with the previous YOLOv3, YOLOv4 has the following advantages:
- It is an efficient and powerful object detection model that enables anyone with a 1080 Ti or 2080 Ti GPU to train a super fast and accurate object detector.
- The influence of state-of-the-art «Bag-of-Freebies» and «Bag-of-Specials» object detection methods during detector training has been verified.
- The modified state-of-the-art methods, including CBN (Cross-iteration batch normalization), PAN (Path aggregation network), etc., are now more efficient and suitable for single GPU training.
Paper: https://bit.ly/2YzFj3R
GitHub: https://bit.ly/2WrECHh
Compared with the previous YOLOv3, YOLOv4 has the following advantages:
- It is an efficient and powerful object detection model that enables anyone with a 1080 Ti or 2080 Ti GPU to train a super fast and accurate object detector.
- The influence of state-of-the-art «Bag-of-Freebies» and «Bag-of-Specials» object detection methods during detector training has been verified.
- The modified state-of-the-art methods, including CBN (Cross-iteration batch normalization), PAN (Path aggregation network), etc., are now more efficient and suitable for single GPU training.
Paper: https://bit.ly/2YzFj3R
GitHub: https://bit.ly/2WrECHh
On Guard Against COVID-19: AI Projects That Deserve a Shout-Out
In this article, the author analyzed some of the most interesting and promising solutions in terms of their potential to slow down the spread of the COVID-19 infection, reduce death rates, and improve information hygiene in these turbulent times.
https://bit.ly/2A82qZn
In this article, the author analyzed some of the most interesting and promising solutions in terms of their potential to slow down the spread of the COVID-19 infection, reduce death rates, and improve information hygiene in these turbulent times.
https://bit.ly/2A82qZn
Optimize Response Time of your Machine Learning API In Production
This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.
https://bit.ly/2L2LIg9
This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.
https://bit.ly/2L2LIg9
ICLR 2020 Recordings
All recordings for papers and workshops of the International Conference on Learning Representations 2020 are now available to everyone!
Workshops - https://bit.ly/3dm5S0C
Papers - https://bit.ly/3dgubgu
All recordings for papers and workshops of the International Conference on Learning Representations 2020 are now available to everyone!
Workshops - https://bit.ly/3dm5S0C
Papers - https://bit.ly/3dgubgu
Feature Stores for ML
This is a great collection of talks about the variety of ways that organizations build and manage their feature stores.
https://bit.ly/2LGV7dB
This is a great collection of talks about the variety of ways that organizations build and manage their feature stores.
https://bit.ly/2LGV7dB
Model Evaluation Metrics in Machine Learning
A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
https://bit.ly/3gvS7Pf
A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
https://bit.ly/3gvS7Pf
FP64, FP32, FP16, BFLOAT16, TF32, and other members of the ZOO
There are many floating point formats you can hear about in the context of deep learning. Here is a summary of what are they about and where are they used.
https://bit.ly/3gUS2Vv
There are many floating point formats you can hear about in the context of deep learning. Here is a summary of what are they about and where are they used.
https://bit.ly/3gUS2Vv
Classification of Brain MRI as Tumor/Non Tumor
Learn to train and apply a simple CNN to differentiate between an MRI with a Tumor and an MRI without one.
https://bit.ly/3gX9clr
Learn to train and apply a simple CNN to differentiate between an MRI with a Tumor and an MRI without one.
https://bit.ly/3gX9clr
Beyond fashion: Deep Learning with Catalyst
Step-by-step tutorial for setting up a deep learning pipeline with Catalyst and deploying the model to production.
https://bit.ly/2XATNQa
Step-by-step tutorial for setting up a deep learning pipeline with Catalyst and deploying the model to production.
https://bit.ly/2XATNQa
How to Scale Data With Outliers for Machine Learning
In this tutorial, you will discover how to use robust scaler transforms to standardize numerical input variables for classification and regression.
https://bit.ly/3eZginS
In this tutorial, you will discover how to use robust scaler transforms to standardize numerical input variables for classification and regression.
https://bit.ly/3eZginS
How to Use Polynomial Feature Transforms for Machine Learning
In this tutorial, you will discover how to use polynomial feature transforms for feature engineering with numerical input variables.
https://bit.ly/3h3dUy8
In this tutorial, you will discover how to use polynomial feature transforms for feature engineering with numerical input variables.
https://bit.ly/3h3dUy8
Catalyst 101 — Accelerated PyTorch
In this post, you will learn about Catalyst framework, developed with focus on reproducibility, fast experimentation, and code/idea reusing.
https://bit.ly/2Yah4r7
In this post, you will learn about Catalyst framework, developed with focus on reproducibility, fast experimentation, and code/idea reusing.
https://bit.ly/2Yah4r7
Using AI to predict retinal disease progression
In this post, you will learn about AI system that can predict the development of exAMD which was created by DeepMind in collaboration with Moorfields Eye Hospital and Google Health.
https://bit.ly/2MEbnwv
In this post, you will learn about AI system that can predict the development of exAMD which was created by DeepMind in collaboration with Moorfields Eye Hospital and Google Health.
https://bit.ly/2MEbnwv