#Convolutional-neural-network
Showing 37 of 37 repositories tagged #convolutional-neural-network, ranked by stars
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
π₯π₯High-Performance Face Recognition Library on PaddlePaddle & PyTorchπ₯π₯
TensorFlow (Python API) implementation of Neural Style
High-efficiency floating-point neural network inference operators for mobile, server, and Web
C++ Implementation of PyTorch Tutorials for Everyone
π Difficult algorithm, Simple code.
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)
HDR image reconstruction from a single exposure using deep CNNs
libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.
Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral & Best paper finalist)
Keras tutorial for beginners (using TF backend)
Comparison of famous convolutional neural network models
A Deep Learning UCI-Chess Variant Engine written in C++ & Python :parrot:
<λ¨Έμ λ¬λ κ΅κ³Όμ 3ν>μ μ½λ μ μ₯μ
Application of deep learning for earth observation.
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
Lane detection and classification in an end-to-end Deep Learning fashion
TuSimple lane detection dataset addon with class information.
C.Origami, a prediction and screening framework for cell type-specific 3D chromatin structure.
Recommending Music using a Convolutional Neural Network.
A project demonstrate that downsampling(upsaming) in cnn are not nesscessary
Π‘ustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Machine Learning for Synthetic Aperture Radar Autofocus
3D CNN to predict single-phase flow velocity fields
Multiclass image classification using Convolutional Neural Network
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.
Virtually remove a face mask to see what a person looks like underneath
Implementation of cartoon GAN [Chen et al., CVPR18] with pytorch
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Deployed bird classification webapp using Deep Learning, Docker, and Streamlit. Users can go onto the webapp and either upload their own images of birds or select from a set of images to feed through a Deep Learning model and display a prediction.
Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and other open source LLMs. Sentiment Analysis, Application Context based ChatBots. Custom Data Handling. LLMs - GPT 3.5 / 4o, Gemini Pro 1.5, Claude 3, Llama 3.1, Phi-3, Gemma 2, Falcon 3, Qwen 2.5, Mistral Nemo, Wizard Math
Using Tensorflow to classify the NIST Dataset 19 (Handwriting)
Parametrically designing my PhD thesis cover using adaptive sampling, neural networks, and quantum physics
Hi! Thanks for checking out my tutorial where I walk you through the process of coding a convolutional neural network in java from scratch. After building a network for a university assignment, I decided to create a tutorial to (hopefully) help others do the same and improve my own understanding of neural networks.