121 skills found · Page 3 of 5
shreyasvedpathak / Tensorflow Advanced Techniques SolutionsThis repository contains my solutions for the Coursera course TensorFlow: Advanced Techniques Specialization. Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.
Joyies / GDPOOfficial code for GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
CAODH / MolGenBenchCodebase for Paper: Benchmarking Real-World Applicability of Molecular Generative Models from De novo Design to Lead Optimization with MolGenBench
aws-samples / Sample Gen AI Evaluations WorkshopThis workshop teaches systematic approaches to evaluating Generative AI workloads for production use. You'll learn to build evaluation frameworks that go beyond basic metrics to ensure reliable model performance while optimizing cost and performance.
sayantann11 / Clustering Modelsfor MLlustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
sakha1370 / Generative AI Engineering CourseraThis repository features my coursework from the IBM Generative AI Engineering Professional Certificate on Coursera. It includes Jupyter Notebooks with code, explanations, and visualizations, along with HTML/PDF summaries. The content highlights my skills in Generative AI, LLMs, NLP, and model optimization.
oxling / P5js AntsA p5.js ant colony optimization simulator. I used this to create weird generative art.
IDEALLab / IH GAN CMAME 2022IH-GAN, data generation, and topology optimization code associated with our accepted CMAME 2022 paper: "IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures."
arm-education / AI On ArmHands-on course materials for deploying and optimizing generative AI on Arm processors: Raspberry Pi, AWS Graviton, SIMD, quantization (educational)
zdhNarsil / Diffusion Generative Flow SamplersPyTorch implementation for our ICLR 2024 paper "Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization"
xqh19970407 / InvDesFlow ALThe discovery of novel functional materials with targeted properties remains a fundamental challenge in materials science. In this work, we propose InvDesFlow-AL, an active learning-based generative framework for inverse materials design, which iteratively optimizes material generation towards desired properties.
kremerj / GanA 1D toy example of optimizing a generative model using the WGAN-GP model.
marcus-jw / Targeted Manipulation And Deception In LLMsCodebase for "On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback". This repo implements a generative multi-turn RL environment with support for agent, user, user feedback, transition and veto models. It also implements KTO and expert iteration for training on user preferences.
issacAzazel / MolEditVersatile Molecular Editing via Multimodal and Group-optimized Generative Learning
nathanaelbosch / Generative Latent OptimizationPyTorch Implementation: "Optimizing the Latent Space of Generative Networks"
yhinai / TensorGPGPURISC-V vector and tensor compute extensions for Vortex GPGPU acceleration for ML workloads. Optimized for transformer models, CNNs, and generative AI with configurable precision (FP32/16/BF16/INT8).
IdanAzuri / Glico Learning Small SampleGenerative Latent Implicit Conditional Optimization when Learning from Small Sample ICPR 20'
nii-yamagishilab / NELE GANImplementation for paper: Multi-Metric Optimization using Generative Adversarial Networks for Near-End Speech Intelligibility Enhancement
tentenco / Awesome GeoEverything you need to know about Generative engine optimisation (GEO) & AI SEO. DOMINATE AI SEARCH RESULTS Future-proof your business with expert Generative Engine Optimization (GEO). Get discovered by ChatGPT, Gemini, and other AI platforms when your customers ask questions.
EdoardoGruppi / Drug Design ModelsThis project is a reimplementation of the models introduced in the following papers: "Multiobjective de novo drug design with recurrent neural networks and nondominated sorting", "REINVENT 2.0: An AI Tool for De Novo Drug Design", "Hierarchical generation of molecular graphs using structural motifs", "Mol-CycleGAN: a generative model for molecular optimization", "Multi-objective de novo drug design with conditional graph generative model" and "Graph convolutional policy network for goal-directed molecular graph generation".
