Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Last update: Dec 29, 2021

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Deep Learning Art041_NN_Feed_Forward

Overview

Art041_NN_Feed_Forward

Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Illustration of a very simple Feed Forward Neural Network:

Illustration of how Neural Network processes inputs to the get to the output:

Owner

SolClover

Data Science and Analytics Professional

GitHub Repository

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🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

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Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Related tags

Overview

Art041_NN_Feed_Forward

Owner

SolClover

Biomarker identification for COVID-19 Severity in BALF cells Single-cell RNA-seq data

Multi-Agent Reinforcement Learning (MARL) method to learn scalable control polices for multi-agent target tracking.

Perform zero-order Hankel Transform for an 1D array (float or real valued).

Adaptive Denoising Training (ADT) for Recommendation.

《Rethinking Sptil Dimensions of Vision Trnsformers》(2021)

Training a deep learning model on the noisy CIFAR dataset

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

Explainability for Vision Transformers (in PyTorch)

Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation" (NeurIPS 2021)

BridgeGAN - Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

Localization Distillation for Object Detection

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A small tool to joint picture including gif

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PyTorch implementation of Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets