📘 The MLOps stack component for experiment tracking
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Updated
May 20, 2024 - Python
Keras is an open source, cross platform, and user friendly neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidML.
📘 The MLOps stack component for experiment tracking
I will update all my implemented machine learning and deep learning projects here. Stay tuned!
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
S7-S8 Main Project: Concatenation of Attention Enhanced Spatial and Temporal Features for Violence Detection from Videos
深度学习TensorFlow2入门指南。Deep Learning TensorFlow2 API and Examples with Python3 and Jupyter Notebook.
Labs and workbooks for IBM Machine Learning Certificate on Coursera. Also included a few resources on side that I found helpful.
Deep Learning for humans
Recurrent neural networks to predict solar radiation measurements.
Visualizer for neural network, deep learning and machine learning models
Data Science DRY OOP Umbrella Library
A deep learning model created to classify the movie reviews as positive or negative.
Automatic colorization of the grayscale images
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Handwritten Digit Classification using artificial neural network with tensorflow + keras.
Keras beit,caformer,CMT,CoAtNet,convnext,davit,dino,efficientdet,edgenext,efficientformer,efficientnet,eva,fasternet,fastervit,fastvit,flexivit,gcvit,ghostnet,gpvit,hornet,hiera,iformer,inceptionnext,lcnet,levit,maxvit,mobilevit,moganet,nat,nfnets,pvt,swin,tinynet,tinyvit,uniformer,volo,vanillanet,yolor,yolov7,yolov8,yolox,gpt2,llama2, alias kecam
Projects for Neural Networks course, Shahid Beheshti University, Fall 2020
Data science projects with Keras
Implement secure transaction system using keras deep learning model for recogination and , intergrated otp verification for security
A modular inverse QSAR pipeline
🌟 This repository houses a collection of image classification models for various purposes, including vehicle, object, animal, and flower classification. Each classifier is built using deep learning techniques and pre-trained models to accurately identify and categorize images based on their respective classes.
Created by François Chollet
Released March 27, 2015