💡 TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering

Выпущен TextDiffuser-2 с кодом и демо.

📚 Paper: https://arxiv.org/abs/2311.16465

🖥 Code: https://github.com/microsoft/unilm/tree/master/textdiffuser-2

⚡️ Demo: https://huggingface.co/spaces/JingyeChen22/TextDiffuser-2

@Machine_learn

Sign Language Understanding

🖥 Github: https://github.com/FangyunWei/SLRT

📕 Paper: https://arxiv.org/pdf/2401.05336v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/csl-daily
@Machine_learn

Book: Fight Fraud with Machine Learning
Authors: Ashish Ranjan Jha
ISBN: Null
year: 2023
pages: 288
Tags: #Machine_learning
@Machine_learn

ViQuAE

🖥 Github: https://github.com/paullerner/viquae

📕 Paper: https://arxiv.org/pdf/2401.05736v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/imagenet

✨ Tasks: https://paperswithcode.com/task/cross-modal-retrieval

@Machie_learn

🖼 AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning


🖥 Github: https://github.com/guoyww/animatediff/

💻 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb

📚 Paper: https://arxiv.org/abs/2307.04725

📁 Project: https://animatediff.github.io/

@Machine_learn

Language Tool

LanguageTool is an Open Source proofreading software for English, Spanish, French, German, Portuguese, Polish, Dutch, and more than 20 other languages. It finds many errors that a simple spell checker cannot detect.

Creator: LanguageTool
Stars: ⭐️ 9.8k
Forked by: 1.1k
GitHub repo: https://github.com/languagetool-org/languagetool

#tools #LanguageTools
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@Machine_learn

Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation

🖥 Github: https://github.com/kaistai/prometheus-vision

📕 Paper: https://arxiv.org/abs/2401.06591v1

🔥Datasets: https://paperswithcode.com/dataset/ok-vqa

@Machine_learn

🖼 ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation



🖥 GitHub: https://github.com/bytedance/ImageDream

📚 Paper: https://arxiv.org/abs/2312.02201

🥩 Demo: https://github.com/bytedance/ImageDream/blob/main

@Machine_learn

SAM-Med2D ➕

SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.


🖥 Github: https://github.com/uni-medical/sam-med2d

🖥 Colab: https://colab.research.google.com/github/uni-medical/SAM-Med2D/blob/main/predictor_example.ipynb

📕 Paper: https://arxiv.org/abs/2308.16184

⭐️ Dataset: https://paperswithcode.com/dataset/sa-1b

@Machine_learn

Book: Machine Learning for Financial
Risk Management with Python
Authors: Abdullah Karasan
ISBN: 978-1-492-08518-8
year: 2022
pages: 194
Tags: #Machine_learning #financial
@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

GraphEmb

🖥 Github: https://github.com/ubioinformat/graphemb

📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf

✨ Tasks: https://paperswithcode.com/task/benchmarking

@Machine_learn

paper: Unifying Large Language Models and Knowledge Graphs: A Roadmap
index: Natural Language Processing, Large Language Models, Generative Pre-Training, Knowledge Graphs, Roadmap, Bidirectional Reasoning.

@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

CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion

🖥 Github: https://github.com/haha-lisa/creativesynth

📕 Paper: https://arxiv.org/pdf/2401.14066v1.pdf

✨ Tasks: https://paperswithcode.com/task/image-generation

@Machine_learn

🥳FreeInit with AnimateDiff Gradio Colab


🖥 colab: https://github.com/camenduru/FreeInit-colab

🔮page: https://tianxingwu.github.io/pages/FreeInit/

📚paper: https://arxiv.org/abs/2312.07537

🥩code: https://github.com/TianxingWu/FreeInit

@Machie_learn

🔍 FIND: Interface Foundation Models' Embeddings


🖥 Code: https://github.com/UX-Decoder/FIND

🎓 Demo: http://find.xyzou.net/

🔮 Project Page: https://x-decoder-vl.github.io

🥩 Demo: http://find.xyzou.net

📚 ArXiv: https://arxiv.org/pdf/2312.07532.pdf

@Machine_learn

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

🖥 Github: https://github.com/cvlab-columbia/pix2gestalt

📕 Paper: https://arxiv.org/pdf/2401.14398v1.pdf

✨ Tasks: https://paperswithcode.com/task/3d-reconstruction

@Machine_learn