training-data-analyst | Labs and demos for courses for GCP Training ( http | Learning library

 by   GoogleCloudPlatform Jupyter Notebook Version: Current License: Apache-2.0

kandi X-RAY | training-data-analyst Summary

kandi X-RAY | training-data-analyst Summary

training-data-analyst is a Jupyter Notebook library typically used in Tutorial, Learning, Tensorflow, Docker applications. training-data-analyst has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Labs and demos for courses for GCP Training (

            kandi-support Support

              training-data-analyst has a medium active ecosystem.
              It has 6824 star(s) with 5362 fork(s). There are 261 watchers for this library.
              It had no major release in the last 6 months.
              There are 115 open issues and 70 have been closed. On average issues are closed in 102 days. There are 212 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of training-data-analyst is current.

            kandi-Quality Quality

              training-data-analyst has no bugs reported.

            kandi-Security Security

              training-data-analyst has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              training-data-analyst is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              training-data-analyst releases are not available. You will need to build from source code and install.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of training-data-analyst
            Get all kandi verified functions for this library.

            training-data-analyst Key Features

            No Key Features are available at this moment for training-data-analyst.

            training-data-analyst Examples and Code Snippets

            No Code Snippets are available at this moment for training-data-analyst.

            Community Discussions


            chown command result in user invalid error in google colab
            Asked 2020-Nov-23 at 00:58

            I try to run code !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst from in Google Colab jupyter notebook.

            Although my account authenticated with authentication_user() method:



            Answered 2020-Nov-23 at 00:58

            It is an error because the user jupyter does not exist.

            You need to create it first with



            ''ls' is not recognized as an internal' in python
            Asked 2020-Oct-22 at 21:29

            While working on a python tutorial, i have following code that doesn't run and I dont recognize. what kind of import should I do for them in order to have it run?

            I did import os, import sys, not helping.



            Answered 2020-Oct-22 at 21:29

            This isn't Python code. These are Unix commands, not meant to be ran in Windows, which appears that you are.

            You shouldn't need to modify permissions of example data, and Python has native functions for listing contents of directory and files that you should be using instead.

            For example, os.glob() and open()

            Based on the usage of /home/jupyter, I would guess you skipped part of the tutorial that is using a Docker container



            Hybrid recommendation system with matrix factorization and linear regression
            Asked 2020-May-14 at 18:47

            I'm following a tutorial that for creating a recommendation system in BigQueryML. The tutorial uses matrix factorization first to calculate user and item factors. In the end I have a model that can be queried with user ids or item ids to get recommendations.

            The next step is feeding the factors and additional item + user features into a linear regression model to incorporate more context.

            "Essentially, we have a couple of attributes about the movie, the product factors array corresponding to the movie, a couple of attributes about the user, and the user factors array corresponding to the user. These form the inputs to our “hybrid” recommendations model that builds off the matrix factorization model and adds in metadata about users and movies."

            I just don't understand why the dataset for linear regression excludes the user and item ids:



            Answered 2020-May-14 at 18:47

            In the example you have shared, the goal is to fit a linear regression to the discovered factor values so that a novel set of factor values can be used to predict the rating. In this kind of setup, you don't want information about which samples are being used; the only crucial information is the training features (the factor scores) and the rating (the training/test label). For more on this topic, take a look at "Dimensionality reduction using non-negative matrix factorization for information retrieval."

            If you included the movie ids and user ids in as features, your regression would try to learn on those, which would either add noise to the model or learn that low ids = lower score etc. This is possible, especially if this ids are in some kind of order you're not aware of, such as chronological or by genre.

            Note: You could use movie-specific or user-specific information to build a model, but you would have many, many dimensions of data, and that tends to create poorly performing models. The idea here is to avoid the problem of dimensionality by first reducing the dimensionality of the problem space. Matrix factorization is just one method among many to do this. See, for example, PCA, LDA, and word2vec.



            How to use tensorflow to ingest sharded CSVs
            Asked 2020-Jan-04 at 20:00

            This is a problem I am working on in google cloud platform with tensorflow v1.15

            I am working on this notebook In this section, I am supposed to return a function that feeds model.train()



            Answered 2020-Jan-04 at 20:00

            The training-data-analyst repository you mentioned, also has the solutions to all the notebooks.

            From analysing the provided solution it looks like the def fn() part is reduntant. the read_dataset function should simply return a tf.Data.dataset:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install training-data-analyst

            You can download it from GitHub.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • HTTPS


          • CLI

            gh repo clone GoogleCloudPlatform/training-data-analyst

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular Learning Libraries


            by freeCodeCamp


            by CyC2018


            by TheAlgorithms


            by kdn251

            Try Top Libraries by GoogleCloudPlatform


            by GoogleCloudPlatformPython


            by GoogleCloudPlatformGo


            by GoogleCloudPlatformJupyter Notebook


            by GoogleCloudPlatformGo


            by GoogleCloudPlatformJupyter Notebook