Search Results for "transfer function model" - Page 8
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Showing 297 open source projects for "transfer function model"
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ml-design-patterns
Source code accompanying O'Reilly book: Machine Learning Design
The ml-design-patterns repository contains the source code and examples that accompany the book “Machine Learning Design Patterns,” providing practical implementations of reusable solutions for common challenges in machine learning systems. It organizes patterns into categories such as data representation, problem framing, and model training, helping practitioners understand how to structure ML pipelines effectively. The repository includes implementations of techniques like feature hashing, embeddings, feature crosses, and multimodal inputs, which are essential for handling diverse data types. It also covers strategies for improving model performance and robustness, including transfer learning, checkpointing, ensemble methods, and rebalancing techniques for imbalanced datasets. ... -
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3 Rs of Software Architecture
A guide on how to write readable, reusable, and refactorable software
This guide aims to help software developers think more clearly about how to build systems that are not only functional today but maintainable into the future. It focuses on three architectural “ilities”: readability, reusability, and refactorability, presenting them in a hierarchical framework so developers can evaluate and improve their code and system design. The project uses a simple shopping-cart application written in JavaScript and React/Redux to illustrate how code evolves from “bad”... -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
Tensorflow 2017 Tutorials is a structured set of tutorials that introduce developers to TensorFlow, starting with basic neural network constructs and progressing to sophisticated model architectures and training techniques. This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the basics, the project includes examples of convolutional neural networks, recurrent networks, autoencoders, reinforcement learning, generative adversarial networks, and transfer learning workflows. ... -
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EfficientNet Keras
Implementation of EfficientNet model. Keras and TensorFlow Keras
This repository contains a Keras (and TensorFlow Keras) reimplementation of EfficientNet, a lightweight convolutional neural network architecture achieving state-of-the-art accuracy with an order of magnitude fewer parameters and FLOPS, on both ImageNet and five other commonly used transfer learning datasets. Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. ... -
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TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
...These examples cover a wide range of topics including convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders, and transformer-based models such as GPT and BERT. Each section of the repository includes runnable code and structured experiments designed to illustrate how different architectures and algorithms function in real applications. The tutorials use well-known benchmark datasets such as MNIST, CIFAR, and Fashion-MNIST to demonstrate practical model training and evaluation workflows. -
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SentEval
A python tool for evaluating the quality of sentence embeddings
...Because the interface is minimal, researchers can plug in encoders from any framework or language model and obtain a broad evaluation with little glue code. SentEval helped establish common baselines and reporting conventions in the sentence-representation community, reducing friction when comparing new methods. -
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WaveletStat
Wavelet analysis of 1D and 2D statistical distributions
This is WaveletStat, a scientific software to analyse statistical distributions by Continuous Wavelet Transforms (CWTs). Taking an input random sample, it computes the corresponding CWT, filters out the noise based on a p-value criterion, and reconstructs a denoised model of the density function by matching pursuit iterations. The C++ code has two branches: the 1D branch can be used on 1D distributions only, while 1D2D can be used both on 1D and 2D distributions (though less optimal on 1D). The code is fully multithreaded. There are also some tutorial examples analysing distributions in the asteroids main belt, including sample GNUPLOT script to render the computing results in EPS graphs. ... -
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gsasnp2
PubMed ID: 29562348 / DOI: 10.1093/nar/gky175
* GSA-SNP2 is a successor of GSA-SNP (Nam et al. 2010, NAR web server issue). GSA-SNP2 accepts human GWAS summary data (rs numbers, p-values) or gene-wise p-values and outputs pathway genesets ‘enriched’ with genes associated with the given phenotype. It also provides both local and global protein interaction networks in the associated pathways. * Article: SYoon, HCTNguyen, YJYoo, JKim, BBaik, SKim, JKim, SKim, DNam, "Efficient pathway enrichment and network analysis of GWAS summary data... -
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StarGAN
Official PyTorch Implementation
StarGAN is an implementation of the Star Generative Adversarial Network, a model designed for multi-domain image-to-image translation using a single unified GAN architecture. Unlike earlier GAN approaches that required separate models for each domain pair, StarGAN enables flexible attribute transfer across multiple domains within one network, significantly improving efficiency and scalability. The repository includes full training and inference pipelines for tasks such as facial attribute manipulation and style transfer. ... -
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The Neural Process Family
This repository contains notebook implementations
...Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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Laravel Searchable
A php trait to search laravel models
Searchable is a trait for Laravel 4.2+ and Laravel 5.0 that adds a simple search function to Eloquent Models. Searchable allows you to perform searches in a table giving priorities to each field for the table and its relations. This is not optimized for big searches, but sometimes you just need to make it simple (Although it is not slow). By default, multi-word search terms are split and Searchable searches for each word individually. Relevance plays a role in prioritizing matches that match... -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0). Applicability. Many people already have their own ML workflows and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes. Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and... -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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doT
The fastest + concise javascript template engine for nodejs
Created in search of the fastest and concise JavaScript templating function with emphasis on performance under V8 and nodejs. It shows great performance for both nodejs and browsers. doT.js is fast, small and has no dependencies. doT is a really solid piece of software engineering (I didn’t create it) that is rarely updated exactly for this reason. It took me years to grasp how it works even though it’s only 140 lines of code, it looks like magic. -
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Incremental DOM
An in-place DOM diffing library
Incremental DOM is a lightweight library for building DOM trees by issuing imperative update instructions, avoiding heavyweight virtual DOM diffing at runtime. Instead of creating and diffing large object graphs, templates compile to a sequence of function calls that “patch” the live DOM in place. This model eliminates allocations associated with virtual trees and allows updates to be streamed directly to the DOM, which can improve memory usage and reduce GC pressure. It integrates naturally with template compilers (such as those that generate calls from HTML-like markup) but can also be used by hand for fine-tuned rendering. ... -
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Spotlight
Deep recommender models using PyTorch
Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. Spotlight offers a slew of popular datasets, including Movielens 100K, 1M,... -
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Dep
Go dependency management tool experiment
Dep was an official experiment to implement a package manager for Go. While dep has many discrete components and moving parts, all of these parts revolve around a central model. Dep is a tool intended primarily for use by developers, to support the work of actually writing and shipping code. It is not intended for end users who are installing Go software - that's what go get does. It is strongly recommended that you use a released version of dep. While tip is never purposefully broken, its... -
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RELEASE NOTE: Get raxmlGUI 2.0 at the NEW PROJECT LOCATION: https://antonellilab.github.io/raxmlGUI/ raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used software for phylogenetic inference using maximum likelihood. A userfriendly graphical front-end for phylogenetic analyses using RAxML (Stamatakis, 2006). Please cite: Silvestro, Michalak (2012) - raxmlGUI: a graphical front-end for RAxML. Organisms Diversity and Evolution 12, 335-337. DOI:...
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automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model... -
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Batch PIE
A batch pipelined image editor
Current filter functionality: - Simple editing options: Image cropping, resizing, rotation, Color brightness curve alignment - Histobram processing: Convolution, statistics (e. g. f_max or median analysis) - Image segmentation: The actual segmentation process as well as group weight calculation for further filtering (both functions rely on self defined custom dynamic mathematical functions) - Dynamic mathematical functions for custom and automated image filtering: General mathematical operations, using image or matrix as f(x, y), export f(x, y) as image or matrix, mapping variables on other ones and of course boolean operation for case sensitivity - A flexible variables model of dynamic mathematical function that sets no restriction on particular variables count - Sub project support for an organized total process targeting to save time using previously created editing routines instead of redoing steps each time -
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fft
A fast distributed file transfer
fft is a distributed file transfer tool designed to accelerate large file movement by coordinating multiple relay nodes in parallel rather than depending on a single server’s bandwidth ceiling. Its architecture splits a transfer into concurrent “workers” that fetch or push chunks across multiple paths, improving throughput on high-latency or bandwidth-constrained links. The project is implemented in Go and exposes a straightforward command-line interface so operators can stand up senders,... -
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Canon EOS DIGITAL Info
Utility for Reading/ editing some Infos on Canon EOS DSLRs
Canon doesn’t have shutter count included on the EXIF information of an image file, as opposed to Nikon and Pentax. There’s no official Canon based application to find the shutter count for an EOS DSLR. However, there are a few free tools that may help you to do this. They provide some details about the camera, including product Name, firmware version, battery level, shutter Counter, date/time, and owner/artist/copyright strings. But it does not support this features: Editing the... -
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Machine learning Resources
Some learning materials and research introduction on machine learning
...It serves as a curated knowledge base that introduces fundamental algorithms and techniques used in modern machine learning systems. The repository organizes materials that cover topics such as classification algorithms, neural networks, feature engineering, and model evaluation. Many sections reference research papers, tutorials, and open-source implementations that allow users to explore specific machine learning methods in greater depth. The project is maintained by researcher Jindong Wang, whose work focuses on machine learning research areas including transfer learning, domain adaptation, and robust learning methods. -
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Learn_Deep_Learning_in_6_Weeks
This is the Curriculum for "Learn Deep Learning in 6 Weeks"
Learn_Deep_Learning_in_6_Weeks compresses an introductory deep learning curriculum into six weeks of structured learning and practice. It begins with neural network fundamentals and moves through convolutional and recurrent architectures, optimization strategies, regularization, and transfer learning. The materials emphasize code-first understanding: building small models, training them on accessible datasets, and analyzing their behavior. Each week culminates in a tangible outcome—such as a working classifier or sequence model—so progress is visible and motivating. The plan also introduces practical considerations like GPU usage, checkpoints, and debugging training dynamics. ... -
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Snapshot Demo
Snapshot models the genes and reactions within human cells
Snapshot is a poly-omics model of the human cell allowing you to edit genes and see how it affects cell functionality and what diseases might arise. The full Genome scale metabolic model consists of the following: 92 Genes 53 Reactions 109 Metabolites 3 Cellular Compartments 7 Diseases Which can be found here for only £2!: https://www.scarboroughbiotech.com/ Note demo version is a much smaller genome scale model.