Welcome to this project on Classifying Flowers in Iris dataset with Deep Neural Network using Keras. In this project, you will use Python and Keras to build a Deep Neural Network, and apply it to predict the classes of Flowers in the Iris dataset.
Keras is one of the most extensively used APIs in the world of Deep Learning. It provides an amazing developer-friendly deep learning framework to build deep learning models with wide-ranging features to support high scalability, because of which it is not only widely used in academics but also in organizations to build state-of-the-art research models. In …
Welcome to the project on Working with Custom Loss Function. This project aims to provide an understanding of how we could use the custom defined loss functions along with TensorFlow 2.
Though TensorFlow 2 already provides us with a variety of loss functions, knowing how to use a user-defined loss function would be crucial for a machine learning aspirant because often times in real-world industries, it is expected to experiment with various custom defined functions. This exercise is designed to achieve that goal.
Skills you will develop:
An introduction to CloudxLab lab, and how you can submit your work using the Assessment Engine.
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:
TensorFlow 2
scikit-learn
Matplotlib
Numpy
This project uses the face_recognition
library in Python to find a celebrity look-alike from a picture that you upload.
The face_recognition
library, which is built using [dlib][1]’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.
For more information …
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.
Github link: [https://github …
In this project, we will learn how to predict images from their noisy version. We will use the MNIST dataset for this project. First, we will load the dataset, explore it, and they we will learn how to introduce noise to an image. Next we will train a KNN Classifier to predict the original image from it's noisy version.
Skills you will develop:
Data analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making.
The steps to perform Data Analysis depends on the end goal we want to pursue such as to drive business decisions, evaluate performance, for making predictions, etc.
In this tutorial, we will perform Data Analysis with the end goal of feeding the data to a Machine Learning model i.e for making predictions.
This is a beginner-friendly end-to-end project for Data Analysis. The only prerequisite of the project is to know Python. Other than it, everything …
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.
Welcome to this project on Image Stitching using OpenCV. In this project, we will use OpenCV with Python and Matplotlib in order to merge two images and form a panorama.
As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this exercise, we will understand how to make a panorama stitching using OpenCV with Python.
Skills you will develop:
In this project, we will learn how to build a real-time analytics dashboard using Apache Spark Streaming, Kafka, Node.js, Socket.IO, and Highcharts.
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 PostgreSQL.
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 to Docker Hub, using GitHub and Travis CI. Finally, we will understand how to automate deployments on AWS Elastic Beanstalk using GitHub and Travis.
Github link: https://github.com/cloudxlab/user-wishlist-app
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][1]. 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.
Further …
Welcome to the project on the 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.
Github link: https://github.com/cloudxlab/user-wishlist-app/tree/awscache-rds-eb