dataset.py model.py requirement.txt test.py train.py Repository files navigation README Apache-2.0 license classification-torch A simple demo of implementing cat and dog classification 这个项目是一个简单的使用resnet18实现猫狗分类的例子,主要学习:1)是神经网络实现分类的原理 2)了解模型优化流程...
It is important to get ahigh-level feeling (qualitative) of your dataset along with a fine-grained analysis (quantitative). If you are working with a public dataset, someone else might have already dived into the data and reported their analysis (it is quite common in Kaggle...
In competitions, such as ones found onKaggle, the competitor receives the training set (labeled data) and test set (unlabeled data). This can be a good place to test pseudo-labeling. Thedatasetwe will use is from theMercedes-Benz Greener Manufacturing competition– the goal is the predict th...
In which we have developed binary classification using Deep Convolutional Neural Network (DCNN). Google collab notebooks are used to model DCNN with GPU based Keras library and Tensor flow as back end. Experiments are conducted on Tuberculosis Chest X-ray dataset obtained from Kaggle community and ...
Let’s start with a simple and very easy multi-class classification dataset, theIris dataset, and compare performances of Scikit-learn’sSGDClassifierwith the Amazon Machine Learning multi-class classification . The SGDClassifier is set up similarly to the Amazon Machine Learning SGD parameters: ...
FastDFS依赖无法导入 fastdfs-client-java 导入爆红 <!-- FastDFS--> <dependency> <group...
ShubhaMahobia / RNN-Classification Star 0 Code Issues Pull requests This project involves building a sentiment analysis model using Recurrent Neural Networks (RNN) to classify movie reviews from the IMDb dataset as either positive or negative. The IMDb dataset consists of 50,000 highly polarized...
How would a random predictor perform (especially in classification problems)? Dataset can be unbalanced… What would the loss look like for a random predictor? What is (are) the best metric(s) to measure progress on my task? What are the limits of this metric? If it’s ...
We used the ASAP dataset to train a basic LSTM deep learning model. For automated text scoring, which is concerned with the quality of writing, stop words contain crucial information for the system to predict accurate scores and should therefore, remain in text. We compared cases with and ...
After removing the blurred and duplicated images from the dataset using a numerical threshold, the trained model achieved specificity and sensitivity values0.98 in the identification of DR retinas. For severity grading, the classification accuracy values of 0.84, 0.95, and 0.98 were recorded for the ...