Ultimate Walkthrough for ML-Agents in Unity3D

Hey! This will be a fast-paced, complete walkthrough of building an A.I. with Unity’s ML-Agents. Like a good TV Chef, I already have a simple game prepared, which you can clone from GitHub. Make sure you do, if you want to follow along! 🙂
Repository: A.I. Jumping Cars
Currently, it’s just a human-controlled game, no machine learning involved, YET! By pressing the space key, you can let the car jump to dodge the incoming vehicles.
We will train an A.I. via Machine Learning to do the same thing, hopefully, better than we — or at least I — can. If you prefer to watch a video instead, this is for you:

#machine-learning #programming #artificial-intelligence #coding #unity3d

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Buddha Community

Ultimate Walkthrough for ML-Agents in Unity3D

Hey! This will be a fast-paced, complete walkthrough of building an A.I. with Unity’s ML-Agents. Like a good TV Chef, I already have a simple game prepared, which you can clone from GitHub. Make sure you do, if you want to follow along! 🙂
Repository: A.I. Jumping Cars
Currently, it’s just a human-controlled game, no machine learning involved, YET! By pressing the space key, you can let the car jump to dodge the incoming vehicles.
We will train an A.I. via Machine Learning to do the same thing, hopefully, better than we — or at least I — can. If you prefer to watch a video instead, this is for you:

#machine-learning #programming #artificial-intelligence #coding #unity3d

ML and Trading - Impact of Machine Learning in Trading | Mobinius

https://www.mobinius.com/blogs/impact-of-machine-learning-in-trading

#machine learning #ml-development-company #ml-development-solutions #ml-development-services #hire-ml-developers

Deploying trained ML model on Heroku using Flask | End-to-End ML Project Tutorial

The series will cover everything from Data Collection to Model Deployment using Flask Web framework on Heroku!

GitHub Repository: https://github.com/dswh/fuel-consumpt…

Subscribe: https://www.youtube.com/c/DataSciencewithHarshit/featured

#ml #heroku #ml #deploying

Home | MLDevelopmentCompany

https://technologiesmobini.wixsite.com/mldevelopment

#ml-development-company #ml-solutions #ml-services #ml-developers

Overview Of Azure ML And ML Studio

In this article, we are going to learn about Azure ML and ML Studio. As we know Azure is Microsoft Cloud computing service. And Machine learning supported by Azure is called Azure ML. It’s a complete automated framework to build, teach, train and deploy as a web service and have visual development environment to make it easy for data scientists.

Benefits of having Azure ML as a cloud solution,

1. It’s providing Azure ML Studio which it uses to create model and deploy instantly.
2. Visual user interface, drag and drop feature, real time data visualization.
3. All projects, experiments are stored in the cloud. You can access it from anywhere.
4. Almost all input data types are supported by Azure ML as data source.
5. Extend model using R and Python or use trained model as module.

Supported Input data types

1. Hive
2. HTTP
3. MySQL
4. SQL Server
5. PostgreSQL
6. Power Query
7. SharePoint
8. Azure DB
9. Web API
10. Local Files
11. Teradata

There are many other data sources; you can check it on the Microsoft portal.

Azure ML Studio

Here I will give an overview of Azure ML Studio. You need to sign up in Azure portal and select Machine Learning Studio to launch it. It opens in the browser and looks like the below image.

It’s workbench software which has predefined protocols to follow while building and training a model. As per the image, the visual workspace enables developers to quickly create models and visualize data with just some clicks.

It has 6 high level navigations menus and those are Projects, Experiments, Web Service, Dataset, Trained Model and Settings.

Projects

It lists all projects and models created by users. Project contains combinations of all module experiments and datasets.

Experiments

It allows developers to build, test and iterate multiple times on either its new model or existing model. You can copy models and do many experiments and get accurate predictive results.

Web Service

Tested and trained models are deployed as web services as public APIs to use outside of the Azure environment. It predict results based on input parameters. It returns value based on trained deployed model data.

Dataset

Dataset contains uploaded datasets in Azure ML studio. It lists uploaded datasets and you can also pick from Microsoft sample datasets, which can be utilized for your experiments. You can use big New + buttons to add data files from your local computer.

Trained Model

Save your trained models and experiment for future uses.

Settings

Settings tab allows us to view and edit workspace and regenerate authorization token.

#azure #overriew #azure-ml #ml-studio