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dlnn-week-07

This week we discussed about Recurrent Neural Networks, Long Short Term Memory Cells, Back Propagation through time and how to apply LSTMs for time series/ sequence data. Then we implemented a simple random LSTM Neural Network to predict stock market values using a real data-set from AAPL.

dlnn-week-08-gans

This week we discuss about Generative Adversarial Networks

dlnn-week-10

In this week we discussed about Reinforcement Learning. Under the topic, Q Learning and Policy based Learning sections were discussed. And we implemented a RL Agent for Cart-Pole environment available in Gym Module.

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.

dlon-tutorial-02

dlon-week-01

This includes the Materials of the First Week of Deep Learning & Neural Network 3 Months Online Course. In the First Week Practical Session we discussed about basics of Python Programming Language

dlon-week-02-practical

dlon-week-04

This week we discussed about Feature Extraction and Convolution Neural Network Architecture.

face-mask-detection-keras

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.

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.

mlon-week-02

mlon-week-03

mlon-week-04

This week we discussed about Support Vector Machine

mlon-week-05

mlw-day-01

mlw-tutorial-01

object-detection-using-cascade-classifiers

opencv

Open Source Computer Vision Library

python-modules-tutorial

python-oop-tutorial

python-plays-dinosaur-game

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

python-tutorial

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

real-time-domestic-energy-disaggregation-using-deep-learning-v1

real-time-domestic-energy-disaggregation-using-deep-learning-v2

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.

tensorflow-object-detection-api-configuration

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

test

tkinter-examples

traffic-sign-classification.keras

Deep leaning for traffic sign classification in keras and python