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Perceptron (@aieml)

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face-mask-detection-keras

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tensorflow-object-detection-api-configuration

This tutorial discusses how to configure the Tensorflow Object Detection API in windows and how implement custom object detection.

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Python-Modules-Tutorial

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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.

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Week-01

Python Programming Basics and Essential Python Module

Week-11

In Week 11 we discussed about Recurrent Neural Networks and Long-Short-Term memory cells. Then we started building a chatbot. As the initial part we discussed about the Word2Vec Algorithm and implemented it in python with the help of Gensim and NLTK libraries

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python-plays-dinosaur-game

This repository contains the materials used for implementing a Python code to automate and play the Chrome Dinosaur Game

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Week-03

MLW-Day-01

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DLNN-Assignment-02

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

Advanced-Python-Day-01

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Week-02

Linear Regression, Polynomial Regression, R2 Score, Image Pre-processing, Changing Color Spaces, Smoothing, Thresholding

python-tutorial

This repository Includes all the materials used in the Python Tutorial in the Perceptron Youtube Channel.

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Tkinter-Examples

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.

STEREO-VISION-BASED-SYSTEM-FOR-POSITION-AND-ORIENTATION-ESTIMATION-OF-ROBOTIC-MANIPULATORS

The objective of this work was to build a robot arm with stereo vision technique to identify a standard object and do the pick and place task. We have successfully completed the estimation of position and the orientation parameters (X, Y, Z) and 𝞡y of the Center of Volume for 4 types of Standard objects, Cubes, Cuboids, Cylinders with negligible and rectifiable errors. Necessary actions were taken to minimize the errors in position and orient estimation. An embedded system was developed with user friendly software interface, and has install the vision system into the Fanuc M10 Robot ARM available in SLIIT Robotic lab. Furthermore a gripper was designed and 3D printed the prototype. It was attached to the end of the robot arm. After testing the gripper the relationship between force and voltage as well as the relationship between linear movement of the screw nut along the power screw and velocity was obtained. Moreover a graphical user interface was developed which is able to train the linear regression model, live predict the coordinates of the objects, and which can check the accuracy of the predicted data as well as which can send predicted data to the gripper and the robot arm .And In addition as a future implementation, the embedded system will be upgraded to device which can be implemented with any kind of customized Robot Arms available in the Industry.

Week-05

In Week 05 We discussed about Contours and Contour Operation under low level image processing section, and Decision Tree Algorithm under Supervised Machine Learning Algorithms

DLON-Tutorial-02

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webapp_covid

Object-Detection-using-Cascade-Classifiers

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MLON-Week-02

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ML-IP-Workshop

All the materials used, codes and notes used in Machine Learning and Image Processing 1 Day Free Workshop held on 01st June 2019 are uploaded here.

DLNN-Week-04

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

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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.

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DLNN-Assignment-01

This Assignment is based on basic Python Programming Concepts

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.

Week-04

In Week 3, we discussed about Support Vector Machine, and we started the In class project 2, Hand Written Digits recognition software. This software is implemented using Tkinter, the standard Graphical User Interface library for python.

Week-02-Group4

This week we discussed about AI and Machine Learning basics. And also about the 3 main types of machine learning and KNN Algorithm.

Machine-Learning-Online-Workshop

In this 3 hours online free workshop we will discuss about everything that you need to learn as a beginner in the field Machine Learning. This is totally FREE and anyone can join. This workshop targets School goers, Undergraduates, Professionals and anyone interested on Artificial Intelligence, Machine Learning, Deep learning or any related fields and the beginners in the field. Firstt I will introduce you the concepts Artificial Intelligence, Machine Learning, Deep Learing and their differences. Then we will discuss about Machine Learning Algorithms and how they work. Thereafter how to apply real world problems to Machine Learning algorithms, how to Train and Evaluate Machine Learning algorithms and finally how to use a trained Machine Learning algorithms to obtain real time predictions. At the last part of the workshop we will train, evaluate and test a machine learning algorithm practically using Python and some other contributed modules.

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Tutorial-3-KMeans-Clustering

In this tuorial, a practical usage of KMeans Clustering is discussed. We can use KMeans Clustering for Image Segmentation tasks. In other word, using KMeans Clustering we can identify different segments present in an image. See the example

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DLNN-Week-11

This week we tried to implement a CNN type Neural Network for Energy Disaggregation. Energy disaggregation is the problem of separating an aggregate energy signal into the consumption of individual appliances in a household.

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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.

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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.

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Week-10

In Week 10 we discussed and demonstrated Tensorflow Object Detection API and NVIDIA self driving car with Udacity Self Driving Car Simulator