This Repository contains material to learn about machine learning algorithms concepts along with implementation. This also provides you the material to prepare yourself for interviews.
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Updated
May 25, 2024
This Repository contains material to learn about machine learning algorithms concepts along with implementation. This also provides you the material to prepare yourself for interviews.
[CVPR 2024] Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matching
Evolutionary search for survival analysis loss function for neural networks
Alternative loss function of binary cross entropy and focal loss
Directional Distance Field for Modeling the Difference between 3D Point Clouds
A dependency free library of standardized optimization test functions written in pure Python.
A better pytorch-based implementation for the mean structural similarity. Differentiable simpler SSIM and MS-SSIM.
A neat, lightweight and single neuron perceptron written in C++ from scratch without any external library, trained using the perceptron trick and loss function
Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction
Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)
Piecewise linear approximations for the static-dynamic uncertainty strategy in stochastic lot-sizing
A codebase for a traffic optimization research project.
Deep Learning Loss Functions
Bahdanau Attention Mechanism | Tensorflow Custom Layers/Model/Loss Function/Metrics | LSTM | Encoder | Decoder | Cross-Attention | Language Translation | Bleu Score | Dropout
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples.
An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.
A new loss proposed that are sensitive towards image corruption and high information to noise trade off
📄 Official implementation regarding the paper "Programmatically Evolving Losses in Machine Learning".
Core components of neural networks An introduction to Keras Setting up a deep-learning workstation Using neural networks to solve basic classification and regression problems
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