#Regularization
Showing 23 of 23 repositories tagged #regularization, ranked by stars
Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
Image-processing software for cryo-electron microscopy
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
Programming assignments and lecture notes of the Deep Learning Specialization taught by Andrew Ng and offered by deeplearning.ai on Coursera.
Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.
Spells for everyday living, also a book -- Models Demystified -- now available!
[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets
The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021
Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.
Implementation of key concepts of neuralnetwork via numpy
🧠👨💻Deep Learning Specialization • Lecture Notes • Lab Assignments
Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021
My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.
Iterative unfolding for Python
Lecture Slides and Programming Exercises that may help study the deep learning book by Goodfellow, Bengio and Courville.
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
Solutions for the Machine Learning Zoomcamp 2022 by DataTalks.Club.
Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.