Machine learning books and papers

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی:  1️⃣ @Ai_Tv 2⃣ @ai_python 3⃣ @HomeAI ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯  منابع برنامه‌نویسی   : 1⃣@pythony ‏❯ علم داده : 1️⃣ @DataSciSchool 2⃣ @DataPlusScience ‏❯  تنسورفلو  : 1⃣ @cvision ‏❯ آموزش پایتون: ‏ 1⃣  @Programming4all_0to100   2⃣  @raspberry_python

سلام تا امشب این جایگاه ها رو‌داریم از دوستان کسی خواست بهم اطلاع بده...!

تنها یک جایگاه از این مقاله مونده...!

تنها دو جايگاه از مقالمون باقي مونده دوستاني كه خواستن شركت كنن Title: Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach Abstract: Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case. journal: https://www.sciencedirect.com/journal/information-and-computation @Raminmousa

تنها دو جايگاه از مقالمون باقي مونده دوستاني كه خواستن شركت كنن Title: Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach Abstract: Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case. journal:https://www.sciencedirect.com/journal/information-and-computation @Raminmousa

با عرض سلام مقاله ي جديد بنده اماده ارسال هستش نويسنده اول خودم هستم. Title: Classifying Object into Virtual Reality Environment Using Recurrent Neural Network: A GRU LSTM Hybrid Approach journal: Information and Computation: https://www.sciencedirect.com/journal/information-and-computation دوستاني كه نياز دارن جايگاه ٢ تا ٤ خاليه، به بنده اطلاع بدن @Raminmousa

با عرض سلام تخفيف ٧٠٪؜ دو پكيج يادگيري ماشين و عميق, براي كساني كه نتونستن تهيه كنند رو تا شب در نظر گرفتيم كسايي كه نياز دارن ميتونن به ايدي بنده پيام بدن. @Raminmousa

🔸لیستی از برترین کانال‌های آموزشی در زمینه های هوش‌مصنوعی, پایتون و یادگیری ماشین ‏❯ هوش مصنوعی:  1️⃣ @Ai_Tv 2⃣ @ai_python ‏❯ یادگیری ماشین و یادگیری عمیق : 1️⃣ @Machine_learn ‏❯  تنسورفلو  : 1⃣ @cvision ‏❯ علم داده : 1️⃣ @DataSciSchool 2⃣ @DataPlusScience ‏❯ آموزش پایتون: ‏ 1⃣  @Programming4all_0to100  2⃣ @raspberry_python