Building Versatile Mobile Apps with Python and REST - 2020
#python #django
@Machine_learn

🚀 Stable Diffusion web UI

UI на основе библиотеки Gradio для Stable Diffusion. Большое количество фич для генерации контента с удобным интерфейсом.

🖥 Github: https://github.com/AUTOMATIC1111/stable-diffusion-webui

⏩ Scripts: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts

⭐️ Features: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features

@Machine_learn

🚀 DiffusionDet: Diffusion Model for Object Detection

DiffusionDet — первая диффузионная модель для обнаружения объектов.

🖥 Github: https://github.com/shoufachen/diffusiondet

➡️ Paper: https://arxiv.org/abs/2211.09788v1

🗒 Getting Started: https://github.com/ShoufaChen/DiffusionDet/blob/main/GETTING_STARTED.md

🖥 Dataset: https://paperswithcode.com/dataset/imagenet

@Machine_learn

با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)

جهت تهیه می تونین به ایدی بنده مراجعه کنین

@Raminmousa

https://bibis.ir/science-books/data/2022/Fundamentals-of-Data-Engineering-by-Joe-Reis-Matt-Housley_bibis.ir.pdf

Fundamentals ofData
Engineering
#book #ML
@Machine_learn

Deep Learning with Keras : : CHEAT SHEET
#Cheat_sheet #keras
@Machine_learn

Data science : : CHEAT SHEET
#Cheat_sheet #DS
@Machine_learn

⏩ PODA: Prompt-driven Zero-shot Domain Adaptation
.

🖥 Github: https://github.com/astra-vision/poda

⏩ Paprer: https://arxiv.org/abs/2212.03241v1

❤️ Pretrainde model: https://drive.google.com/drive/folders/15-NhVItiVbplg_If3HJibokJssu1NoxL?usp=share_link

⭐️ Dataset: https://paperswithcode.com/dataset/cityscapes

@Machine_learn

🔼 IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation

🖥 Github: https://github.com/shendu0321/incepformer

✔️ Project: https://github.com/shendu0321/IncepFormer

🗒 Paper: https://arxiv.org/abs/2212.03035v1

➡️ Data: https://paperswithcode.com/dataset/cityscapes

@Machine_learn

•(Multi-Modal Image Fusion)
。(nfrared and visible image fusion)
。 (Medical image fusion)
•(Digital Photography Image Fusion)
。(Multi-exposure image fusion)
。(Multi-focus image fusion)
• (Remote Sensing Image Fusion)
。(Pansharpening)
•(General Image Fusion Framerwork)
#(Survey)
#(Dataset)
#(Evaluation Metric)
#(General evaluation metric
github.com/miao19980215/Image-Fusion


@Machine_learn

✅ pypop7 (Pure-PYthon library of POPulation-based black-box OPtimization)



$ pip install pypop7

🖥 Github: https://github.com/evolutionary-intelligence/pypop

⏩ Paprer: https://arxiv.org/abs/2212.05652v1

⭐️ Derivative-Free Optimization (DFO): https://link.springer.com/article/10.1007/s10208-021-09513-z

@Machine_learn

✔️ ECON: Explicit Clothed humans Obtained from Normals



🖥 Github: https://github.com/YuliangXiu/ECON

⏩ Paprer: https://arxiv.org/abs/2212.07422

📎 Demo: https://github.com/YuliangXiu/ECON#demo

✔️ Instructions: https://github.com/YuliangXiu/ECON#instructions

@Machine_learn

✅ DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

git clone --recurse-submodules [email protected]:cvg/DeepLSD.git
cd DeepLSD

🖥 Github: https://github.com/cvg/deeplsd

⏩ Paprer: https://arxiv.org/abs/2212.07766v1

✔️ Dataset: https://paperswithcode.com/dataset/hpatches

@Machine_learn

⏩ GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation



🖥 Github: https://github.com/chenhongyiyang/gpvit

➡️Paprer: https://arxiv.org/abs/2212.06795v1

✔️Data Preparation: https://paperswithcode.com/dataset/must-c

@Machine_learn

✅️ JRBD: Egocentric Perception of Humans


⭐️ Dataset: https://jrdb.erc.monash.edu/

🖥 Github: https://github.com/JRDB-dataset/jrdb_toolkit/

⏩ JRDB-Pose: https://jrdb.erc.monash.edu/dataset/pose#toolkit

✅ Paper: arxiv.org/pdf/1910.11792.pdf

@Machine_learn

🔥 Yolo8 is coming!


Github
Docs
Colab

@Machine_learn

⚡️ MVTN: Learning Multi-View Transformations for 3D Understanding



🖥Github: https://github.com/ajhamdi/mvtorch

⭐️ Paper: https://arxiv.org/abs/2212.13462v1

⏩ Dataset: https://paperswithcode.com/dataset/modelnet

⏩ Сlassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb

➡️ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb

@Machine_learn

Python Concurrency with asyncio Matthew Fowler
Matthew Fowler (2022)
#book #python 2022

@Machine_learn