Learn the basics of Data Science in the crash course created by Marco Peixeiro, where you will learn about the theory and code behind the most common algorithms used in data science.

Datasets:

Course Contents

  • ⌨️ (00:00) Introduction
  • ⌨️ (03:06) Setup
  • ⌨️ (04:29) Linear regression (theory)
  • ⌨️ (09:29) Linear regression (Python)
  • ⌨️ (20:59) Classification (theory)
  • ⌨️ (30:16) Classification (Python)
  • ⌨️ (49:30) Resampling & regularization (theory)
  • ⌨️ (56:09) Resampling and regularization (Python)
  • ⌨️ (1:05:17) Decision trees (theory)
  • ⌨️ (1:13:12) Decision trees (Python)
  • ⌨️ (1:24:50) SVM (theory)
  • ⌨️ (1:28:17) SVM (Python)
  • ⌨️ (1:58:24) Unsupervised learning (theory)
  • ⌨️ (2:06:54) Unsupervised learning (Python)
  • ⌨️ (2:20:55) Conclusion

Yannic Kilcher explains why transformers are ruining convolutions.

This paper, under review at ICLR, shows that given enough data, a standard Transformer can outperform Convolutional Neural Networks in image recognition tasks, which are classically tasks where CNNs excel. In this Video, I explain the architecture of the Vision Transformer (ViT), the reason why it works better and rant about why double-bline peer review is broken.

OUTLINE:

  • 0:00 – Introduction
  • 0:30 – Double-Blind Review is Broken
  • 5:20 – Overview
  • 6:55 – Transformers for Images
  • 10:40 – Vision Transformer Architecture
  • 16:30 – Experimental Results
  • 18:45 – What does the Model Learn?
  • 21:00 – Why Transformers are Ruining Everything
  • 27:45 – Inductive Biases in Transformers
  • 29:05 – Conclusion & Comments

Related resources:

  • Paper (Under Review): https://openreview.net/forum?id=YicbFdNTTy

Sascha Dittmann has created a series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, it’s here on Frank’s World and on YouTube.  

Subscribe for more free data analytics videos: https://www.youtube.com/saschadittmann?sub_confirmation=1And don’t forget to click the bell so you don’t miss anything. Share this video with a YouTuber friend: https://youtu.be/mZUdYu345dg

If you enjoyed this video help others enjoy it by adding captions in your native language:https://www.youtube.com/timedtext_video?v=mZUdYu345dg

Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used: https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps: https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service: https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension: https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

✅ Let’s connect:Twitter: https://twitter.com/SaschaDittmannFacebook: https://www.facebook.com/DataDrivenDevInstagram: https://www.instagram.com/saschadittmann/LinkedIn: https://www.linkedin.com/in/saschadittmannGitHub: https://github.com/SaschaDittmann

DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureMLIn this series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, I strongly recommend that you do so:

Subscribe for more free data analytics videos:
https://www.youtube.com/saschadittmann?sub_confirmation=1
And don’t forget to click the bell so you don’t miss anything.

Share this video with a YouTuber friend:

If you enjoyed this video help others enjoy it by adding captions in your native language:
https://www.youtube.com/timedtext_video?v=mZUdYu345dg

Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used:
https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps:
https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service:
https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension:
https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

✅ Let’s connect:
Twitter: https://twitter.com/SaschaDittmann
Facebook: https://www.facebook.com/DataDrivenDev
Instagram: https://www.instagram.com/saschadittmann/
LinkedIn: https://www.linkedin.com/in/saschadittmann
GitHub: https://github.com/SaschaDittmann

DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureML

Sascha Dittmann shows us how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure in this first in a series of videos.

In the first video of this 5-part series, you’ll discover how to create an Azure DevOps project, import sample machine learning code and create a DevOps pipeline to process simple Data Quality Checks.I use services like Azure DevOps and Azure Machine Learning Services for this challenge.

StatQuest with Josh Starmer explains Naive Bayes in a clear way.

When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier – which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you’ll no longer be naive about Naive Bayes!!

Content:

  • 0:00 Awesome song and introduction
  • 1:08 Histograms and conditional probabilities
  • 4:22 Classifying “Dear Friend”
  • 7:33 Review of concepts
  • 9:00 Classifying “Lunch Money x 5”
  • 10:54 Pseudocounts
  • 12:35 Why Naive Bayes is Naive

PyTorch, the popular open-source ML framework, has continued to evolve rapidly since the introduction of PyTorch 1.0, which brought an accelerated workflow from research to production.

In this video, take a deep dive on some of the most important new advances, including model parallel distributed training, model optimization and on device deployment as well as the latest libraries that support production scale deployment working in concert with MLFlow.