Name: Aktaruzzaman Aman
Type: User
Company: Self
Bio: My area of interest is Computer vision, Machine learning algorithms, Deep Learning, predictive modeling, data analysis, data mining, data management and STAT
Location: Augusta, GA,USA
Blog: https://www.kaggle.com/aktaruzzaman
Aktaruzzaman Aman's Projects
Data augmentation technique has been demonstrated using ImageDataGenerator tool from keras.preprocessing tool
Data files for all tutorials
In-depth dive into neural nets, deep nets, CNN's, RNN's, face recognition, neural style transfer, object detection and structuring/debugging machine learning projects. Coded using Python 3, numpy, and TensorFlow through Jupyter Notebook
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions.
its a home work on hand signal recognition tutorial by Andrew NG
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
Image segmentation and statistical analysis techniques have been applied to determine cancer cells progression in that area of specimen. OpenCV 'grabCut' algorithm and multiple image threshold and segmentation techniques have been applied
This is a Kaggle beginner competition: Digit Recognizer
kivy_sample_project
This is an implementation of Deutsch and Deutsch, "Latin hypercube sampling with multidimensional uniformity", Journal of Statistical Planning and Inference 142 (2012) , 763-772
Review accuracy estimation methods and compare methods: crossvalidation and bootstrap
Jupyter Interactive Notebook
Classify image by using KERAS pre-trained model. It calls transfer learning. All you need to do is choose your model based on your application and load them in your kernel and last few layers as your application requires and train only those few layer/s then your are good to go
use these data for practice pandas tricks
Plant diseases causes many significant damages and losses in crops around the world. Some suitable measures on disease identification should be introduced to prevent damages and minimize losses. Early Detection of Disease helps in increasing the crop productivity as well as in minimizing expense. Technical approaches using machine learning and computer vision are actively researched to achieve intelligence farming by early detection on plant disease. The accuracy of object detection and recognition systems has been drastically improved by the recent development in Deep Neural Networks. By using these systems and implementation of computer vision and machine learning techniques, plant diseases can be detected. Here we have used transfer learning based approach to diagnose diseases of different plants using its images captured by camera devices either drone or smartphone. Our goal is to build a market oriented product for Plant Disease Detection, a smartphone app compatible with both smartphone camera and drone camera. The target group of the user is those who request a quick diagnosis on common leaf disease at any time of the day i.e. Farmers, agricultural industries, agricultural consultants and Government Agencies & Departments.
A POS/Stock Control System baked with python3 and kivy
IoT security camera running open-cv for object detection 📹