PacktPub | Hands-On Machine Learning for .NET Developers [FCO]

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PacktPub | Hands-On Machine Learning for .NET Developers [FCO] (Size: 1.7 GB)
  0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url 409 B
  01.The Course Overview.mp4 9.4 MB
  02.Demo of the Application and How to Apply Machine Learning.mp4 12.3 MB
  03.Installing the ML.NET Model Builder.mp4 6.7 MB
  04.Automatically Generate a Model with the ML.NET Model Builder.mp4 7.5 MB
  05.Using the Final Model in the Desktop Application.mp4 16 MB
  06.Generating the Model Using the ML.NET CLI Tool.mp4 7 MB
  07.Demo of the Web API and the Wikipedia Aggression Dataset.mp4 6.9 MB
  08.Digging into the Code Learn What a Training Pipeline Is.mp4 14.7 MB
  09.Implementing a Pipeline for the Aggression Scorer.mp4 17.9 MB
  1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url 307 B
  10.Using the Custom Model in the Web API.mp4 21.6 MB
  11.Evaluating Your Model.mp4 17.5 MB
  12.Splitting the Data into Training and Test Sets.mp4 7.5 MB
  13.Retraining the Model with More Data.mp4 18.5 MB
  14.Evaluating with Cross-Validation.mp4 16 MB
  15.Multiclass Classification and the UCI News Dataset.mp4 11.7 MB
  16.Using AutoML to Find a Suitable Model.mp4 12.3 MB
  17.Building the Pipeline and Evaluating the Performance.mp4 13 MB
  18.Explore the Effect of Imbalanced Data on the Metrics.mp4 15.4 MB
  19.The Restaurant Recommender.mp4 7.9 MB
  20.Building the Restaurant Recommendation Model.mp4 11.2 MB
  21.Exploring Hyper Parameters to Improve the Accuracy.mp4 42 MB
  22.Image Classification and Our Dataset.mp4 5.9 MB
  23.Deep Learning and Transferring Learnings from TensorFlow.mp4 15.3 MB
  24.Training the Custom Image Classification Model.mp4 21.4 MB
  25.Using the Trained Model in the Desktop Application.mp4 9.5 MB
  26.Speeding Up Model Training Using the GPU.mp4 24 MB
  27.What ONNX Is.mp4 6.2 MB
  28.The FER+ ONNX Model.mp4 16.8 MB
  29.Creating Our ONNX Pipeline.mp4 12.9 MB
  3. (FTUApps.com) Download Cracked Developers Applications For Free.url 204 B
  30.Detecting Emotions in Images and Webcam.mp4 21.6 MB
  31.Saving a ML.NET Model in ONNX Format.mp4 9.7 MB
  How you can help our Group!.txt 204 B
  SourceCode.zip 1.2 GB
  ▲ 36 total files

Description


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Forum for discussion >>> https://1hack.us/





By : Karl Tillström
Released : June 2020
Course Source : https://subscription.packtpub.com/video/data/9781800205024

Use machine learning today without a machine learning background

Video Details

ISBN 9781800205024
Course Length 2 hours 47 minutes

Learn

• Quickly implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP.Net Web.APIs, desktop applications, and Dotnet core console apps
• Use the advances in machine learning with models customized to your needs
• Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools
• Improve and retrain your models for better performance and accuracy
• Basic overview of machine learning through a hands-on approach
• Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting
• Data loading and preparation for model training
• Leverage state of the art TensorFlow and ONNX models directly in .NET

About

ML.NET enables developers utilize their .NET skills to easily integrate machine learning into virtually any .NET application. This course will teach you how to implement machine learning and build models using Microsoft's new Machine Learning library, ML.NET. You will learn how to leverage the library effectively to build and integrate machine learning into your .NET applications.

By taking this course, you will learn how to implement various machine learning tasks and algorithms using the ML.NET library, and use the Model Builder and CLI to build custom models using AutoML.

You will load and prepare data to train and evaluate a model; make predictions with a trained model; and, crucially, retrain it. You will cover image classification, sentiment analysis, recommendation engines, and more! You'll also work through techniques to improve model performance and accuracy, and extend ML.NET by leveraging pre-trained TensorFlow models using transfer learning in your ML.NET application and some advanced techniques.

By the end of the course, even if you previously lacked existing machine learning knowledge, you will be confident enough to perform machine learning tasks and build custom ML models using the ML.NET library.

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-.NET-Developers-V

Features:

• Quickly get up and running using state-of-the-art machine learning algorithms in your .Net applications
• Implement machine learning algorithms using real-world data sets, without first learning math
• Leverage state-of-the-art (TensorFlow, ONNX) models, pre-trained by the tech giants, in your own .Net code

Author

Karl Tillström

Karl Tillström has been passionate about making computers do amazing things ever since childhood and is strongly driven by the magic possibilities you can create using programming. This makes advances in machine learning and AI his holy grail; since he took his first class in artificial neural networks in 2007, he has experimented with machine learning by building all sorts of things, ranging from Bitcoin price prediction to self-learning Gomoku playing AI. Karl is a software engineer and systems architect with over 15 years' professional experience in .Net, building a wide variety of systems ranging from airline mobile check-ins to online payment systems. Driven by his passion, he took a Master's degree in Computer Science and Engineering at the Chalmers University of Technology, a top university in Sweden. Follow him and learn more at: https://www.machinelearningfordevelopers.com.



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