animal is good
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Updated
May 18, 2017 - Jupyter Notebook
animal is good
Online machine learning competition for high school students.
DL pre-trained mode fine-tuning for cat-dog classification example
Fine-tuning Language Models with Conditioning on Two Human Preferences
The project aims to detect the faces and to determine whether the person wears a face mask or not.
image processing task I did using fine-tuning, DCNNs and common data augmentation techniques. dataset consisted of 1600 x-ray images of human stomach which included 800 each of pylori(helicobacter) positive and negative. augmented the dataset using common data augmentation techniques.
A Convolution Neural Network Model for predicting various types of food.
Using Pytorch with Django To distinguish Cats from Dogs by Fine Tuning pretrained Model.
Image classification using both non-DL and DL approaches. Some interesting techniques are included like SIFT-feature extraction and multiple kernel learning (MLK).
Mask RCNN model for instance segmentation of power cables for infrastructure inspection purposes.
Ongoing minor project
Implementation on tensorflow fine tuning of generic CNN based model
Switching from GPU to the future of Machine learning the TPU. Over 1 million images trained Resnet50 in under 20 mins compared to days or weeks on GPU and all for 0$ free on Google Colab Notebooks in Google Drive, clone repo and jump right in!!
QuickCNN is high-level library written in Python, and backed by the Keras, TensorFlow, and Scikit-learn libraries. It was developed to exercise faster experimentation with Convolutional Neural Networks(CNN). Majorly, it is intended to use the Google-Colaboratory to quickly play with the ConvNet architectures. It also allow to train on your local…
Keras implementation of multi-label classification of movie genres from IMDB posters
This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.
Pretrained VGG-16 network as feature extractor for Object Recognition (Python, Keras, Scikit-Learn)
Fine-tuning an already learned model, adapts the architecture to other datasets
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