A Survey of the Recent Architectures of Deep Convolutional Neural Networks #CNN #Paper #Survey @Machine_learn

CNN EXPLAINER: Learning Convolutional Neural Networks with Interactive Visualization #CNN #book #Introduction #DL @Machine_learn

A Survey of Convolutional Neural Networks:
Analysis, Applications, and Prospects #CNN #Paper #Survey @Machine_learn

Convolutional Neural Networks: A Comprehensive Survey #CNN #Survey #Paper @Machine_learn

Review of deep learning: concepts, CNN
architectures, challenges, applications, future
directions #CNN #Survey #Paper @Machine_learn

Survey: Convolution Neural networks in Object
Detection #CNN #Survey #Paper @Machine_learn

#CNN #Survey @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 کارهای عملی، پروپوزال و پایان نامه دارند می تونن با ایدی بنده یا شماره واتس اپ بنده 09333900804 در ارتباط باشند.

Introduction to RNNs!
Arun Mallya! #RNN #Slide @Machine_learn

Computational Tutorial:
An introduction to LSTMs in Tensorflow #Slide #RNN @Machine_learn

Recurrent Neural Network
TINGWU WANG,
MACHINE LEARNING GROUP,
UNIVERSITY OF TORONTO #Slide #RNN @Machine_learn

A Critical Review of Recurrent Neural Networks
for Sequence Learning #Survey #RNN @Machine_learn

Time Series Data Imputation: A Survey on Deep Learning Approaches #RNN #Survey @Machine_learn

Survey on Recurrent Neural Network in Natural Language Processing #Survey #RNN @Machine_learn

Recurrent Neural Networks for Edge Intelligence: A Survey #Survey #RNN @Machine_learn

#RNN #Slide and #Survey @Machine_learn

GoEmotions: A Dataset for Fine-Grained Emotion Classification


https://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html

@Machine_learn

🔊 Torchaudio: an audio library for PyTorch

Github: https://github.com/pytorch/audio

Paper: https://arxiv.org/abs/2110.15018v1

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

@Machine_learn

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.
link: https://arxiv.org/abs/2111.00905

@Machine_learn