gradient-ascent
There are 37 repositories under gradient-ascent topic.
utkuozbulak/pytorch-cnn-adversarial-attacks
Pytorch implementation of convolutional neural network adversarial attack techniques
HySonLab/LatentDE
LatentDE: Latent-based Directed Evolution for Protein Sequence Design
mftnakrsu/DeepDream
Generative deep learning: DeepDream
zer0int/CLIP-XAI-GUI
CLIP GUI - XAI app ~ explainable (and guessable) AI with ViT & ResNet models
zer0int/CLIP-Interrogator-LongCLIP-hallucinwords
CLIP Interrogator, fully in HuggingFace Transformers 🤗, with LongCLIP & CLIP's own words and / or *your* own words!
zer0int/CLIP-text-image-interpretability
Get CLIP ViT text tokens about an image, visualize attention as a heatmap.
pranshu28/cnn-viz
Visualizing and interpreting features of CNN model
archana1998/Gradient-Ascent_FK-GRiD
Submission for the Flipkart GRiD 2.0 hackathon under the track "Fashion Intelligence Systems"
IEEE-FCIH-SB/Machine-Learning-Content-2025
Here is the content of ML committe in season 2025
zer0int/CLIP-Direct-Ascent-Synthesis
Like a CLIP + VQGAN. Except without a VQGAN.
zer0int/CLIP-gradient-ascent-embeddings
Use CLIP to create matching texts + embeddings for given images; useful for XAI, adversarial training
atifali/hill-climbing
Numerical Optimization using "hill climbing" (aka Gradient Ascent)
saurabbhsp/machineLearning
Repository for machine learning problems implemented in python
zer0int/CLIPInversion
What do we learn from inverting CLIP models? And what does a CLIP 'see' in an image?
AmanPriyanshu/The-Unlearning-Protocol
Choose which data to make your model forget (Unlearn!), but watch out - every deletion ripples!
ChryssaNab/Machine_Learning-AUEB
This repository hosts the programming exercises for the course "Machine Learning" of AUEB Informatics.
csgwon/pytorch-simple-examples
Simple example notebooks using PyTorch
dboesono/Recommendation-Movie
A user-friendly web application built with Streamlit that offers personalized movie recommendations based on user ratings using a baseline predictive model and RBM neural network
MauroCE/LogisticRegression
Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more
sashakttripathi/MNIST-Softmax-Classification
Image classifier which classifies MNIST database of handwritten digits 0-9 using 28x28 pixel images
zer0int/OpenVision
OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning. + A closer look in PyTorch!
alinaduca/ML_SeminarSuplimentar
Repo for working on the additional Machine Learning seminar tasks, year 2023-2024.
krzyszsz/ApproximateOptimization
A simple heuristic optimizer.
StatisKit/FFP17
Computational Studies of Adja Magatte Fall Internship
zer0int/CLIP-HeadHunter
Head-Hunter: A Visual Bias Explorer. Attention Head Max Visualization to find, rank, and visualize heads; map bias; see what a CLIP 'sees'.
zer0int/CLIP-ResNet-classic-DeepDream
Classic original Inception style DeepDream, but with CLIP ResNet. And CLIP ViT for comparison.
zer0int/CLIP-text-to-image
CLIP guiding self towards an image, from text prompt
AmbarChatterjee/FDS_HW2
Interactive exploration of logistic regression, multinomial classification, and transfer learning using Python and Jupyter Notebooks in the context of data science education.
Filetto-Di-Salmone/Assignment2
2nd assignment of the Fundamentals of Data Science exam, taught by Prof. Fabio Galasso at Sapienza University of Rome in A.Y. 2022/23
santolg/UW-MSIM
Machine learning projects
vijitVM/Machine-Learning-
Machine Learning Problems
zer0int/Golden-Gate-CLIP
Like Golden Gate Claude, but with a CLIP Vision Transformer ~ feature activation manipulation fun!
JayLohokare/reinforce-algorithm-policy-deepRL
OpenAI Gym's Cartpole environment REINFORCE algorithm implementation
jfdev001/gradient-ascent-and-simulated-annealing
Gradient ascent and simulated annealing optimization algorithms for multivariate Gaussian space from scratch.
msancor/FDS-HW2
Python project for the Fundamentals of of Data Science class for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project is exploring Logistic Regression & Multinomial Regression concepts along with training classifiers using Gradient Descent/Ascent.