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

NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion

Github: https://github.com/microsoft/nuwa

Paper: https://arxiv.org/abs/2111.12417v1

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

@Machine_learn

Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion

Github: https://github.com/wutong16/density_aware_chamfer_distance

Paper: https://arxiv.org/abs/2111.12702

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


@Machine_learn

📐 RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (PyTorch)

Github: https://github.com/DingXiaoH/RepMLP

Pre-trained model: https://drive.google.com/drive/folders/1eDFunxOQ67MvBBmJ4Bw01TFh2YVNRrg2?usp=sharing

Paper: https://arxiv.org/abs/2112.11081v1

Task: https://paperswithcode.com/task/semantic-segmentation

@Machine_learn

🛠 Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow.🛠

💻 Github: Link

📄 Paper: Link

✏️ Tasks: Link

@Machine_learn

—————— ConvNeXt ——————--


Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.

Github: https://github.com/facebookresearch/ConvNeXt

Paper: https://arxiv.org/abs/2201.03545

@Machine_learn

vehicle traffic prediction #23Paper @Machine_learn

vehicle Traffic monitoring #10Paper @Machine_learn

vehicle Traffic classification #38Paper @Machine_learn

با عرض سلام ما پكيج ٣٦ پروژه عملي با يادگيري عميق همراه با داكيومنت فارسي را براي دوستاني كه مي خواهند در اين حوزه به صورت عملي كار كنند تهيه كرديم سرفصل هاي اين پكيج به ترتيب زير مي باشند:


1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
هزينه اين پكيج ٥٠٠هزار مي باشد و صرفا هزينه تهيه ديتاست هاست.
جهت خريد مي توانيد با ايدي بنده در ارتباط باشيد
@Raminmousa

Separating Birdsong in the Wild for Classification


http://ai.googleblog.com/2022/01/separating-birdsong-in-wild-for.html

@Machine_learn

کتاب یادگیری ماشین و علم داده: مبانی، مفاهیم، الگوریتم‌ها و ابزارها
دانلود نسخه pdf به صورت رایگان:
https://www.researchgate.net/profile/Milad-Vazan/publication/358263339_yadgyry_mashyn_w_lm_dadh_mbany_mfahym_algwrytmha_w_abzarha/links/61f90216007fb504472c5dc1/yadgyry-mashyn-w-lm-dadh-mbany-mfahym-algwrytmha-w-abzarha.pdf

@Machine_learn

Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

Github: https://github.com/bobetocalo/bobetocalo_pami20

Paper: https://arxiv.org/pdf/2202.02299v1.pdf

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

@Machine_learn

🔝 Omnivore: A Single Model for Many Visual Modalities

Github: https://github.com/facebookresearch/omnivore

Code: https://github.com/facebookresearch/omnivore/blob/main/inference_tutorial.ipynb

Paper: https://arxiv.org/abs/2201.08377

Dataset: https://paperswithcode.com/dataset/epic-kitchens-100

@Machine_learn

#Cloud_Point #Virtual_Reality 16 Papers @Machine_learn

#Cloud_Point #Virtual_Reality 8 Papers @Machine_learn

GAN Cloud Point Generation #Virtual_Reality 9 Papers @Machine_learn