Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
A sequence to sequence prediction for developing a classification system is of very much required in developing applications. Standard approaches for developing applications won't help in providing accuracy. Hence, as an example let's take an IMDB movie review dataset and create some benchmarks by using RNN, RNN with LSTM and drop out rate, RNN with CNN, and RNN with CNN plus drop out rate to make a composite sequence to sequence classification work. We can compare the model accuracy as well.
The goal of this tensorflow project is to identify hand-written digits using a trained model using the MNIST dataset. The MNIST dataset contains a large number of hand written digits and corresponding label (correct digit)
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
Deep Learning Project using Keras Deep Learning Library to predict the effect of Genetic Variants to enable personalized Medicine.