akhilaku
I'm a Deep Learning, Machine Learning enthusiast. I love to learn new technologies. Fast learner
Dhanalakshmi Srinivasan College of Engineering Coimbatore
Pinned Repositories
Assembly-Language-Programs-For-8086-MicroProcessors
Birthday-Card-Android-App
Face-Recognition-Programs
Front-End-Development-Course
Learning Front End Development course from Girl Script Foundation's new initiative #CovidCodingProgram.
gitazure
hello-world
just another repository
Image-Recognition-and-Classification-Device-for-Blind-People
Project idea is to implement an image recognition and classification device in spectacles of blind people, so that it can recognize and classify the things(visuals) in front of them
Image-Recognition-and-Classification-Device-for-Blinds
This project for helping Blind People to recognize the what is in front of them(visuals in front of them) using Machine Learning, Python, Intel Movidius Neural Compute Stick (NCS) with Raspberry Pi-3 for Image Classification Application.
Image-recognition-by-Deep-Learning-using-MATLAB-software
Using Deep Learning for recognizing an image using MATLAB Software.
Translator-using-Python
Language translator using Python Programming language
akhilaku's Repositories
akhilaku/Image-Recognition-and-Classification-Device-for-Blinds
This project for helping Blind People to recognize the what is in front of them(visuals in front of them) using Machine Learning, Python, Intel Movidius Neural Compute Stick (NCS) with Raspberry Pi-3 for Image Classification Application.
akhilaku/Image-recognition-by-Deep-Learning-using-MATLAB-software
Using Deep Learning for recognizing an image using MATLAB Software.
akhilaku/Translator-using-Python
Language translator using Python Programming language
akhilaku/Assembly-Language-Programs-For-8086-MicroProcessors
akhilaku/Face-Recognition-Programs
akhilaku/Front-End-Development-Course
Learning Front End Development course from Girl Script Foundation's new initiative #CovidCodingProgram.
akhilaku/gitazure
akhilaku/Introduction-to-TensorFlow-for-Artificial-Intelligence-Machine-Learning-and-Deep-Learning
This repository is for contributing good quality study materials for learning TensorFlow for beginners from the basics to the intermediate or to an advanced level. Anyone can contribute your knowledge in this field, which can help the people who are interested in learning TensorFlow.
akhilaku/Learning-CSS-From-Basics-To-Advanced
akhilaku/Learning-HTML-From-Basics
Start learning the HTML from basics
akhilaku/Creating-Chrome-Extension
akhilaku/Image-Classification-App-for-Blinds
akhilaku/Stock-Prediction-Using-Twitter-Sentiment-Analysis
Sentiment analysis of the collected tweets is used for prediction model for finding and analysing correlation between contents of news articles and stock prices and then making predictions for future prices will be developed by using machine learning.
akhilaku/100-Days-of-Learning
akhilaku/100DaysLearningChallenge
akhilaku/codeWith-hacktoberfest
akhilaku/Demo
akhilaku/Demo-PROJECT
akhilaku/dsalgo
Contains Algorithms useful for interview preparation, various practice problems of Arrays, Stacks, queue etc. Contributors are Welcome but, DO NOT MAKE THIS REPO ACT LIKE A SOURCE OF +1.
akhilaku/Elephant-Poaching-Risk-Monitoring-System
akhilaku/fullstack-course4
Example code for HTML, CSS, and Javascript for Web Developers Coursera Course
akhilaku/Let-s-Learn
akhilaku/My-To-Do-List-App
This is a To-Do-List App created by me on Android Studios using Kotlin Programming language. This is my First Android App created using Kotlin Programming language which I learned as a part of program 30 days of Kotlin with Google Developers.
akhilaku/Plant-Disease-Detection-Web-application
Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.
akhilaku/Python-code-files
akhilaku/python-mini-projects
A collection of simple python mini projects to enhance your python skills
akhilaku/Python-Programs
akhilaku/TensorFlow-Lite-Examples
akhilaku/Tensorflow-Lite-Examples-Android
akhilaku/Text-Files