microsoft / Bert Stack Overflow
Licence: mit
Train a BERT model with TensorFlow 2.0 to automatically tag StackOverflow questions!
Stars: ✭ 52
Labels
Projects that are alternatives of or similar to Bert Stack Overflow
Tsr Py Faster Rcnn
This repo contains code related to german traffic sign detection and classification using Faster-RCNN
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Continuousparetomtl
[ICML 2020] PyTorch Code for "Efficient Continuous Pareto Exploration in Multi-Task Learning"
Stars: ✭ 52 (+0%)
Mutual labels: jupyter-notebook
Pytorch musicnet
PyTorch DataSet and Jupyter demos for MusicNet
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Reinforcement Learning Introduction
Code from my blog post & online course
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Spark Mllib Scala Play
Twitter sentiment analysis based on Apache Spark, MLlib, Scala and Akka.
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Onnx tflite yolov3
A Conversion tool to convert YOLO v3 Darknet weights to TF Lite model (YOLO v3 PyTorch > ONNX > TensorFlow > TF Lite), and to TensorRT (YOLO v3 Pytorch > ONNX > TensorRT).
Stars: ✭ 52 (+0%)
Mutual labels: jupyter-notebook
Nn from scratch
Multilayer Neural Network using numpy.
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Alpha pooling
Code for our paper "Generalized Orderless Pooling Performs Implicit Salient Matching" published at ICCV 2017.
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Ner blstm Crf
LSTM-CRF for NER with ConLL-2002 dataset
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Ppd599
USC urban data science course series with Python and Jupyter
Stars: ✭ 1,062 (+1942.31%)
Mutual labels: jupyter-notebook
Blackbox Attack
Blackbox attacks for deep neural network models
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Pix2code Template
Build a neural network to code a basic a HTML and CSS website based on a picture of a design mockup.
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Ml securityinformatics
Short Course - Applied Machine Learning for Security Informatics
Stars: ✭ 51 (-1.92%)
Mutual labels: jupyter-notebook
Feature Selection For Machine Learning
Code Repository for the online course Feature Selection for Machine Learning
Stars: ✭ 52 (+0%)
Mutual labels: jupyter-notebook
Coronamaskon
Mask On-Off control with computer vision
Stars: ✭ 52 (+0%)
Mutual labels: jupyter-notebook
Welcome to "Hands-on deep learning with TensorFlow 2.0 and Azure" Workshop!
Overview
This repository contains content of a four part workshop of using Tensorflow 2.0 on Azure Machine Learning service. The different components of the workshop are as follows:
- Part 1: Preparing Data and Model Training
- Part 2: Inferencing and Deploying a Model
- Part 3: Setting Up a Pipeline Using MLOps
- Part 4: Explaining Your Model Predictions
The workshop demonstrates end-to-end Machine Learning workflow on the example of training a BERT model to automatically tag questions on Stack Overflow.
Getting started with the workshop environment
-
Provision your personal Lab environment
- Open Registration URL: http://bit.ly/2OjknZW
- Enter Activation Code which should be provided by the instructors of the workshop.
- Fill out registration form and Submit it.
- On the next screen click Launch Lab.
- Wait until your personal environment is provisioned. It should take approximatly 3-5 minutes.
-
Login to Azure ML studio
- Once the workshop enviroment is ready, you can open new browser tab and navigate to Azure ML studio, using it's direct URL: https://ml.azure.com. We recommend to use Private Browser window for the login to avoid conflicting credentials if you already have Azure subscription.
- Use credentials provided in the workshop environment to sign-in to Azure ML studio.
- In the Welcome screen select preprovisioned subcription and workspace similar to screenshot below:
- Click Get started!
- In the welcome screen click on Take a quick tour button to familiarize yourself with Azure ML studio.
-
Create Azure Machine Learning Notebook VM
- Click on Compute tab on the left navigation bar.
- In the Notebook VM section, click New.
- Enter Notebook VM name of your choice and click Create. Creation should take approximately 5 minutes.
-
Clone this repository to Notebook VM in your Azure ML workspace
- Once Notebook VM is created and in Running state, click on the Jupyter link. This will open Jupyter web UI in new browser tab.
- In Jupyter UI click New > Terminal.
- In terminal window, type and execute command:
ls
- Notice the name of your user folder and use that name to execute next command:
cd <userfolder>
- Clone the repository of this workshop by executing following command:
git clone https://github.com/microsoft/bert-stack-overflow.git
-
Open Part 1 of the workshop
- Go back to the Jupyter window.
- Navigate to
bert-stack-overflow/1-Training/
folder. - Open
AzureServiceClassifier_Training.ipynb
notebook.
You are ready to start your workshop! Have fun.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].