|Название: Intermediate Tutorials for Machine Learning
Автор: Derrick Mwiti
Издательство: Amazon.com Services LLC
Формат: pdf, azw3, epub
Размер: 10.1 MB
In these pages, you'll find some intermediate tutorials for Machine Learning. Top 10 Tricks for TensorFlow and Google Colab Users
Google’s Colab is a truly innovative product for Machine Learning. It enables machine engineers to run Notebooks and easily share them with colleagues. Another key advantage is access to GPUs and TPUs. In these pages, we’ll highlight some of the tips and tricks that will help you in getting the best out of Google’s Colab.
Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones. So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we’ll seek to answer in this look at a technique known as federated learning.
The traditional process for training a machine learning model involves uploading data to a server and using that to train models. This way of training works just fine as long as the privacy of the data is not a concern.
However, when it comes to training machine learning models where personally identifiable data is involved (on-device, or in industries with particularly sensitive data like healthcare), this approach becomes unsuitable.
Top 10 Tricks for TensorFlow and Google Colab Users
Machine Learning in Dask
Image Segmentation with Mask R-CNN
LightGBM: A Highly-Efficient Gradient Boosting Decision Tree
Pruning Machine Learning Models in TensorFlow
Feature Ranking with Recursive Feature Elimination in Scikit-Learn
Dealing with Imbalanced Data in Machine Learning
Convolutional Neural Networks (CNNs): Core Concepts Applied
Serving TensorFlow Models
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