Machine learning books and papers

​​Type4Py: Deep Similarity Learning-Based Type Inference for #python Over the past decade, machine learning (ML) has been applied successfully to a variety of tasks such as computer vision and natural language processing. Motivated by this, in recent years, researchers have employed ML techniques to solve code-related problems, including but not limited to, code completion, code generation, program repair, and type inference. Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection, and etc. However, retrofitting types is a cumbersome and error-prone process. To address this, we propose Type4Py, an ML-based type auto-completion for Python. It assists developers to gradually add type annotations to their codebases. @Machine_learn https://github.com/saltudelft/type4py Announcing post: https://mirblog.net/index.php/2021/07/31/development-and-release-of-type4py-machine-learning-based-type-auto-completion-for-python/

Lane Detection With OpenCV (Part 1) 1. Intro 2. Thresholding 3. Perspective Correction 4. Warping https://dzone.com/articles/lane-detection-with-opencv @Machine_learn

با عرض سلام با توجه به درخواست دوستان تخفیف ویژه 20% برای کسانی که نتوانستن پکیج را تهیه کنند تمدید کردیم. برای تهیه این پکیچ با ایدی زیر در ارتباط باشین. @Raminmousa

ℹ️ LIGAR: Lightweight General-purpose Action Recognition Github: https://github.com/openvinotoolkit/training_extensions Paper: https://arxiv.org/abs/2108.13153v1 Models: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc @Machine_learn

🌐 A Partition Filter Network for Joint Entity and Relation Extraction Github: https://github.com/Coopercoppers/PFN Paper: https://arxiv.org/abs/2108.12202v2 @Machine_learn

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی: 1️⃣ @Ai_Tv 2️⃣ @AI_PYTHON 3️⃣ @HomeAI ‏❯ تنسورفلو 1⃣ @cvision ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯ آموزش پایتون : 1⃣ @Programming4all_0to100 2⃣ @raspberry_python 3⃣ @Koolac_Org 4⃣ @pythonchallenge

📶 ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation Github: https://github.com/segmentationblwx/sssegmentation Paper: https://arxiv.org/abs/2108.12382v1 Dataset: https://cs.stanford.edu/~roozbeh/pascal-context/ @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

🕸 Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Github: https://github.com/VITA-Group/Deep_GCN_Benchmarking Paper: https://arxiv.org/abs/2108.10521v1 @Machine_learn

🧍‍♂ D3D-HOI: Dynamic 3D Human-Object Interactions from Videos Github: https://github.com/facebookresearch/d3d-hoi Paper: https://arxiv.org/abs/2108.08420v1 Dataset: https://dl.fbaipublicfiles.com/d3d-hoi/d3dhoi_video_data.zip @Machine_learn

💡 X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics Github: https://github.com/yehli/xmodaler Paper: https://arxiv.org/abs/2108.08217v1 Project: https://xmodaler.readthedocs.io/en/latest/ @Machine_learn

🔗 A Unified Objective for Novel Class Discovery Github: https://github.com/DonkeyShot21/UNO Paper: https://arxiv.org/abs/2108.08536v2 Dataset: https://paperswithcode.com/dataset/cifar-100 @Machine_learn

🔍 TOOD: Task-aligned One-stage Object Detection Github: https://github.com/fcjian/TOOD Paper: https://arxiv.org/abs/2108.07755v2 Dataset: https://paperswithcode.com/dataset/coco @Machine_learn

با عرض سلام با توجه به درخواست دوستان تخفیف ویژه 20% برای کسانی که نتوانستن پکیج را تهیه کنند تمدید کردیم. برای تهیه این پکیچ با ایدی زیر در ارتباط باشین. @Raminmousa