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Welcome to this project on Autoencoders for MNIST Fashion. In this project, we will understand how to implement Autoencoders using TensorFlow 2.
We will be understanding how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2. We will also depict the reconstructed output images by the autoencoder model.
Skills you will develop:
Welcome to the project on Hosting an Image Classification App on Heroku. In this project, we will get a basic understanding of how to deploy a web app on Heroku, a Platform as a Service.
Heroku is a cloud platform for the deployment and management purposes of web applications. It could be considered as one of the best solutions for hosting web-apps very quickly, thus allowing the developer to concentrate more on development.
This project uses the
face_recognition library in Python to find a celebrity look-alike from a picture that you upload.
face_recognition library, which is built using [dlib]’s state-of-the-art face recognition
built with deep learning, is considered one of the simples libraries used for face recognition and manipulation. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark. With this library you can find faces, find and manipulate facial features, and identify faces in pictures. You can also use this library with other Python libraries to do real-time face recognition.
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Welcome to the project on Yolov4 with OpenCV for Object Detection. In this project, we will learn how to use a YOLOv4 network pretrained on the MSCOCO dataset for object detection.
Object detection has applications in various fields, from home automation to self-driving computers. YOLOv4 is one of the recent state-of-art object detection models. This project provides an overview of how to use a YOLOv4 pretrained model.
Welcome to this project on Deploying App with Docker, Travis CI & AWS Elastic Beanstalk. In this project, we will understand about Docker, Travis, and some services of AWS.
We will first make a simple static website, then dockerize the app. Then we will push it to GitHub and enable Travis to track changes in that repository. Further, we will understand the app deployment on the AWS Elastic Beanstalk using S3 and IAM. We will also host the app on a public domain bought from Google Domains, and configure it with the help of Amazon Route 53.
Welcome to this project on Deploying Multi-Container Docker App on AWS. In this app, we will learn how to build a Deploy Multi-Container Application using Flask, Redis and PosgreSQL.
We will use NGINX-uWSGI along with Flask as the web service, and connect it with the PostgreSQL and Redis container services. Then, we will understand how to automate the process of deploying the web-app on to Docker Hub, using GitHub and Travis CI. Finally, we will understand how to automate deployments on to AWS Elastic Beanstalk using GitHub and Travis.
Welcome to the project on How to build low-latency deep-learning-based flask app. In this project, we will refactor the entire codebase of the project [ How to Deploy an Image Classification Model using Flask]. That monolithic code will be refactored to form two microservices - the flask service and model service. The model service acts as a server that renders pretrained Tensorflow model as a deep learning API, and keeps listening for any incoming requests. The flask service requests the model service, and displays the response from the model server. This way, we write cleaner code and promote service isolation.
Welcome to this project on the CartPole game using OpenAI gym. In this project, we will use OpenAI gym tool to understand how to make CartPole game from the basic.
In this topic, we will learn MongoDB and various concepts like - CRUD operations, query optimization, data modeling, aggregations, MapReduce, indexing, replication, sharding, administration and security
Vulture is a Python library that is used to remove dead code from a Python program. So let us see how to use Vulture to remove dead code from a sample program in Python.
This is a Hands-On assessment to help you learn how to create a deep neural network using TensorFlow in Python. We are going to use the MNIST example to demonstrate.
Welcome to the project on Deploy Flask app with AWS RDS and ElastiCache Redis. In this project, we will learn how to use Amazon RDS and Amazon ElastiCache, how to connect them to AWS Elastic Beanstalk, and deploy a project based on these three technologies.
It is highly recommended to go through the playlist Deploy Multi-Container Docker App on AWS, before going through this project, for a better understanding of this project series.