با عرض سلام دوستانی که نیاز به تهیه ی پکیچ ما دارند می تونن به ایدی بنده پیام بدن @Raminmousa . همچنین دوستانی که نیاز به مشاوره در رابطه با ابده های جدید ، کارهای عملی، پروپوزال و پایان نامه دارند می تونن با ایدی بنده یا شماره واتس اپ بنده 09333900804 در ارتباط باشند.

Deep Learning for disentangling Liquidity-constrained and Strategic Default #DL #Liquidity #Paper @Machine_learn

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Github: https://github.com/Ildaron/Laser_control

Paper: https://www.mdpi.com/2072-4292/13/21/4486

@Machine_learn

🔹 LUMINOUS: Indoor Scene Generation for Embodied AI Challenges

Github: https://github.com/amazon-research/indoor-scene-generation-eai

Paper: https://arxiv.org/abs/2111.05527v1

Dataset: https://paperswithcode.com/dataset/ai2-thor

@Machine_learn

A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

Github: https://github.com/extreme-classification/deepxml

Paper: https://arxiv.org/abs/2111.06685v1

Dataset: https://paperswithcode.com/dataset/extreme-classification

@Machine_learn

Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize


http://ai.googleblog.com/2021/12/improving-vision-transformer-efficiency.html

@Machine_learn

General and Scalable Parallelization for Neural Networks


http://ai.googleblog.com/2021/12/general-and-scalable-parallelization.html

@Machine_learn

🎓 GAN-Supervised Dense Visual Alignment

Github: https://github.com/wpeebles/gangealing

Project: https://www.wpeebles.com/gangealing

Paper: https://arxiv.org/abs/2112.04894v1

Dataset: https://paperswithcode.com/dataset/celeba

@Machine_learn

🕷 Bayesian Active Learning (BaaL)

Github: https://github.com/ElementAI/baal

Documentation: https://baal.readthedocs.io.

Paper: https://arxiv.org/abs/2112.06586v1

Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source

@Machine_learn

با عرض سلام دوستانی که نیاز به تهیه ی پکیچ ما دارند می تونن به ایدی بنده پیام بدن @Raminmousa . همچنین دوستانی که نیاز به مشاوره در رابطه با ابده های جدید ، کارهای عملی، پروپوزال و پایان نامه دارند می تونن با ایدی بنده یا شماره واتس اپ بنده 09333900804 در ارتباط باشند.

Training Machine Learning Models More Efficiently with Dataset Distillation


http://ai.googleblog.com/2021/12/training-machine-learning-models-more.html

@Machine_learn

Cluster Analysis: Basic Concept #Clustering #book @Machine_learn

DATA CLUSTERING Algorithms and Applications #Clustering #Book @Machine_learn

Algorithms for Clustering Data #Book #Clustering @Machine_learn

I gladly announce my first online course on #Statistics and #Mathematics for #MachineLearning and #DeepLearning.

The course will be in English, QA sessions with instructor will be in Turkish, Azerbaijani , or English. TA sessions will be in English.

This is the first course of tribology courses to help attendees to capture foundations and mathematics behind ML,DL models.

The courses are listed as follow:
1. Statistics Foundation for ML
2. Introduction to Statistical Learning for ML
3. Advanced Statistical Learning for DL

The course starts on 15 Jan 2022, at 13:00 to 15:00 (Istanbul time):

Course Fee:
Free for unemployed attendees. :)
200 USD for employed candidates :).

Course contents:
https://lnkd.in/dcXKxUjE

Course Registration:
https://lnkd.in/dMpzMfMG

Please kindly share with the ones who are interested.

Brain tumor detection and segmentation from MRI images using CNN and Unet models.

The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%.
The U-net model is used to segment tumors in MRI images of the brain. After 10 epochs of training, the calculated accuracy is about 98%.
These deep neural networks are implemented with Keras functional API. Use the trained models to detect and segment tumors on brain MRI images. The result is satisfactory.

You can download my U-net trained model from: "https://drive.google.com/drive/folders/1qt7l3HOGIwOguWsMKc5fuwG2NGiGOucf?usp=sharing" and CNN trained model from: "https://drive.google.com/drive/folders/1fXFzMwNG6HrbNp6-GASAgeybeSB3JWCd?usp=sharing".

To access the codes, refer to my GitHub.

Github: https://github.com/AryaKoureshi/Brain-tumor-detection

Website: https://aryakoureshi.github.io/project/BT_detection

@Machine_learn