Welcome to this project on the Neural Style Transfer. In this project, you will use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image.
Neural Artistic Style Transfer finds a wide range of applications to fancily modify images. This field has so much influenced the technical world that many apps, such as Prisma, have received great craze amongst the users. In recent days, decent work has also been done in this area, which served as a holy grail to our project. The heart of this capability is the convolutional neural network …
Welcome to this project on Sentiment Analysis using TensorFlow 2. This project aims to impart an understanding of how to process English sentences, apply NLP techniques, make the deep learning model understand the context of the sentence, and classify the sentiment the sentence implies.
Our real-world is being flooded with a lot of reviews all around us. Be it an online shopping mart, movie reviews, offline market, or anything else. It has become very common for us to rely on these reviews. Hence it would be really helpful for a Machine Learning aspirant to understand various techniques related to processing …
Welcome to this project on Cat vs Non-cat Classifier using Transfer Learning. In this project, you will use Python and Keras to apply the Transfer Learning technique in order to build an image classifier, and apply it to predict the class of an input image - whether it is a cat or a non-cat.
Deep Learning is computationally intensive, often demanding powerful computational resources to yield reasonable accuracies in the real world. The idea of Transfer Learning has become a boon for the computer vision and deep learning community as it has reduced the hunger of Deep Learning algorithms for powerful …
This is a beginner-friendly end-to-end project for Machine Learning. The only prerequisite of the project is to know Python. Other than it, everything is covered in the project itself.
Perks of this project
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:
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 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 this project on Churning the Emails Inbox with Scala. In this project, you will use Scala to access the data from files and process it to achieve certain tasks. You will explore the MBox email dataset, and use Scala to count lines, headers, subject lines by emails and domains. Know your way on how to work with data in Scala.
Skills you will develop:
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
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 …
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:
An introduction to CloudxLab lab, and how you can submit your work using the Assessment Engine.
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 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 …