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adpy-day-06

advanced-python-day-01

advanced-python-day-02

advanced-python-day-03

advanced-python-day-04

advanced-python-day-05

advanced-python-day-05-new

apdy-tutorial-03

apdy-tutorial-04

assignment-02-part-i

This assignment is based on KNN classifier, you have to apply KNN classifier to the given dataset and provide the results as required.

covid-19-prediction-sl

This is an analysis done based on the confirmed cases of COVID-19 virus up to 24th of March. Using Linear Regression, the confirmed cases are predicted for coming 10 days (upto 05th of April). Lets hope this wont be accurate.

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

deep-learning-neural-networks-1-day-workshop

In the Deep Learning & Neural Networks 1 Day Workshop, we discussed about the concepts of Artificial Intelligence, Machine Learning & Deep Learning. We covered the theories and mathematics behind Deep Feed Forward type Neural Networks and at the last phase of the workshop a Neural Network was implemented in Google Co-Lab using Tensorflow and Keras.

diabetic-app

diabetic-app-new

diabetic-app-python-backend

diabetic-app-python-backend

dlnn-08

This week we discussed about Unsupervised Deep Learning Algorithms. Under the topic we started Autoencorders and implemented a model to denoise the noise in Handwritten Digits. The MNIST dataset with random noise was used.

dlnn-assignment-01

This Assignment is based on basic Python Programming Concepts

dlnn-assignment-02

Implementation of a simple FFNN for predicting the probability of having a heart disease

dlnn-assignment-03

dlnn-tutorial-02

dlnn-tutorial-03

In this tutorial we are going to discuss how to train the Tensorflow object detection Api to detect your custom object (example- Banana),

dlnn-tutorial-1

This tutorial explains how to build up a drawing canvas - web application using Python-Flask and JavaScript. The back end is implemented using Flask. At the end of the tutorial, you will be guided to the 1st in class project, Handwritten Digits Recognition app. A FFNN type neural network will be used for the project and the FFNN will be trained using the MNIST dataset.

dlnn-tutorial-3

This tutorial covers basic function of OpenCV, Numpy and Matplotlib. At the end of the Tutorial You have an Assignment to complete. Please open the DLNN Tutorial-02.ipynb file to view the Tutorial

dlnn-week-01

Introduction to Artificial Intelligence, Python Programming Basics and Essential Python Module

dlnn-week-02

In this week we discussed about Feed Forward Type Neural Networks, Supervised Deep Learning and Forward Propagation and implemented simple 4 layer Deep FFNN for Iris Flower example.

dlnn-week-03

This week we discussed about loss functions, loss optimization, gradient decent, Adaptive learning rate optmizers and back-propagation algorithm. Started the 1st in class project, handwritten digits recognition app using flask and keras. A FFNN type neural network was implemented and trained using the MNIST data-set.

dlnn-week-04

This week we discussed about Convolution Neural Networks. And started building up a simple CNN model for detecting cats and dogs.

dlnn-week-05

In this week trained a CNN to identify cat & dogs and tested it with some unseen data. We experienced that the CNN is suffering from over-fitting while training and ended up with a low validation accuracy like 75%. In coming weeks we will discuss about regularization methods for minimizing and avoiding over-fitting such dropout, early stopping, batch normalization and etc. As the 2nd In class project we implemented the NVIDIA self driving car model with Udacity Self Driving Car Simulator.

dlnn-week-06

This Week we discussed, how view and visualize trainable parameters (Weights and Biases) of a trained Neural Network. Then under applications of CNN we implemented the Tensor-flow object detection API. We applied used the mobile net coco version for this implementation.