bullor

-NeuralNet-Classifying-handwritten-digits-w-MNIST

[NeuralNet]Classifying handwritten digits with MNIST dataset

5-Techniques-to-Handle-Imbalanced-Data-For-a-Classification-Problem

If you have already dealt with classification problems, you must have faced instances where one of the target class labels’ numbers of observation is significantly lower than other class labels. This type of dataset is called an imbalanced class dataset which is very common in practical classification scenarios.In this article, I’ll discuss the imbalanced dataset, the problem regarding its prediction, and how to deal with such data more efficiently than the traditional approach.

AUC-ROC-Curve-in-Machine-Learning-Clearly-Explained

You’ve built your machine learning model – so what’s next? You need to evaluate it and validate how good (or bad) it is, so you can then decide on whether to implement it. That’s where the AUC-ROC curve comes in.

AWS-Sagemaker-Autopilot-Train-BERT-Model-for-customer-feedbacks

AWS-SageMaker-Detect-Data-Bias-Clarify

AWS-SageMaker-Studio-Register-and-Visualize-Dataset

Breast-Cancer-Classification-Problem-w-WISCONSIN

Breast Cancer Classification problem is analyzed on Wisconsin dataset . K-fold cross-validation is used to assess model performance. Algorithms are debugged with learning and validation curves.Fine Tuning techniques are applied via grid search CV.

BULLOR

Pytorch-YOLO5S-Blood_Cell_Detection-w-BCCD

In this notebook,I implemented a YOLO on BCCD dataset consisting of cell images and I trained the YOLO for object detection / classification purposes.

PytorchDeepCNN-Classifying-Images-with-Deep-Convolutional-Neural-Networks-w-CelebA

In this notebook,I implemented a CNN on complex CelebA dataset consisting of face images and trained the CNN for smile classification using smile attributes of the pictures.

bullor/-NeuralNet-Classifying-handwritten-digits-w-MNIST

[NeuralNet]Classifying handwritten digits with MNIST dataset

bullor/5-Techniques-to-Handle-Imbalanced-Data-For-a-Classification-Problem

If you have already dealt with classification problems, you must have faced instances where one of the target class labels’ numbers of observation is significantly lower than other class labels. This type of dataset is called an imbalanced class dataset which is very common in practical classification scenarios.In this article, I’ll discuss the imbalanced dataset, the problem regarding its prediction, and how to deal with such data more efficiently than the traditional approach.

bullor/AUC-ROC-Curve-in-Machine-Learning-Clearly-Explained

You’ve built your machine learning model – so what’s next? You need to evaluate it and validate how good (or bad) it is, so you can then decide on whether to implement it. That’s where the AUC-ROC curve comes in.

bullor/AWS-Sagemaker-Autopilot-Train-BERT-Model-for-customer-feedbacks

bullor/AWS-SageMaker-Detect-Data-Bias-Clarify

bullor/AWS-SageMaker-Studio-Register-and-Visualize-Dataset

bullor/Breast-Cancer-Classification-Problem-w-WISCONSIN

Breast Cancer Classification problem is analyzed on Wisconsin dataset . K-fold cross-validation is used to assess model performance. Algorithms are debugged with learning and validation curves.Fine Tuning techniques are applied via grid search CV.

bullor/BULLOR

bullor/Pytorch-YOLO5S-Blood_Cell_Detection-w-BCCD

In this notebook,I implemented a YOLO on BCCD dataset consisting of cell images and I trained the YOLO for object detection / classification purposes.

bullor/PytorchDeepCNN-Classifying-Images-with-Deep-Convolutional-Neural-Networks-w-CelebA

In this notebook,I implemented a CNN on complex CelebA dataset consisting of face images and trained the CNN for smile classification using smile attributes of the pictures.

bullor/AWS-Sagemaker-Text-classifier-using-BlazingText-built-in-algorithm

bullor/Complete-Guide-to-Regularization-Techniques-in-Machine-Learning

Complete Guide to Regularization Techniques in Machine Learning

bullor/ExponentialSmoothing-Demand-Forecast-

bullor/Face-Recognition

Coursera - CNN Programming Assignment: In this project, we will build a face recognition system with FaceNet. Face recognition is a method of identifying or verifying the identity of an individual using their face in photos, video, or in real-time

bullor/Feature-Scaling-for-Machine-Learning-Understanding-the-Difference-Btw-Normalization-Standardization

Feature Scaling for Machine Learning: Understanding the Difference Btw Normalization&Standardization

bullor/FitML

A collection of python Machine Learning articles and examples. You will find code related to Reinforcement Learning, Q Learning, MDP, Bellman, OpenAI solutions and others. You can watch our agents here http://bit.ly/2Ayj4vA

bullor/perceptron--learning--algorithm

Code Repository for Perceptron Learning Algorithm

bullor/satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

bullor/wtecc-CICD_PracticeCode

CICD_PracticeCode

bullor/XBNet

Boosted neural network for tabular data