🧠 Lite-HRNet: A Lightweight High-Resolution Network

Github: https://github.com/HRNet/Lite-HRNet

Paper: https://arxiv.org/abs/2104.06403

@Machine_learn

Simple multi-dataset detection

Github: https://github.com/xingyizhou/UniDet

Paper: https://arxiv.org/abs/2102.13086v1

@Machine_learn

Monster Mash: A Sketch-Based Tool for Casual 3D Modeling and Animation


http://ai.googleblog.com/2021/04/monster-mash-sketch-based-tool-for.html

@Machine_learn

Flexible, Scalable, Differentiable Simulation of Recommender Systems with RecSim NG


http://ai.googleblog.com/2021/04/flexible-scalable-differentiable.html

@Machine_learn

How Machine Learning is Changing e-Government @Machine_learn

Google at ICLR 2021


http://ai.googleblog.com/2021/05/google-at-iclr-2021.html

@Machine_learn

tf.keras.Model

https://www.tensorflow.org/api_docs/python/tf/keras/Model

Code: https://github.com/tensorflow/tensorflow/blob/v2.4.1/tensorflow/python/keras/engine/training.py#L138-L2675

@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

A Survey of Data Augmentation Approaches for NLP

Data Augmentation has becoming more and more popular and important task in NLP. On the contrary to Computer Vision where all methods now are well-known and already pre-implemented in libraries, in NLP the situation is not so consistent.

So, there has been published a nice paper that accumulated all known due today techniques, models and applications of data augmentation in texts:
https://arxiv.org/abs/2105.03075

In the appendix you can find the list of open-source that may be useful for your task.
@Machine_learn

ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision


http://ai.googleblog.com/2021/05/align-scaling-up-visual-and-vision.html

@Machine_learn

500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

@Machine_learn

Project Guideline: Enabling Those with Low Vision to Run Independently


http://ai.googleblog.com/2021/05/project-guideline-enabling-those-with.html

@Machine_learn

Neural-network-from-scratch

Github: https://github.com/Joaomlg/neural-network-from-scratch

Code: https://github.com/Joaomlg/neural-network-from-scratch/archive/refs/heads/main.zip

@Machine_learn

Topic : Sudoku solver (SolSudo)

Abstract : SolSudo is a Sudoku solver made using Deep Learning. SolSudo can solve sudokus using images. This has an intelligent solution method. According to this method, the model predicts the blank digits, and when each level is completed, the filled blanks are placed one after another. Each time a digit is filled, new sudoku will be fed to the solver to determine the next digit. Again and again, until there is no blank left. One of the features of this project is detecting sudoku from an image and filling in the blanks. This requires tesseract-ocr, however, which may cause problems. Therefore, I devised a method, in which the Sudoku numbers are entered one by one, and 0 is used for the empty spaces. Below is an example of Sudoku, its detection, and its solution.

Github Link : https://github.com/AryaKoureshi/SolSudo
Linkedin Link : https://www.linkedin.com/posts/arya-koureshi_deeplearning-python-tensorflow-activity-6711641409658716160-kdSD

@Machine_learn

Beginning Robotics with Raspberry Pi and Arduino #2021 #book @Machine_learn

Deep Learning
on Windows #deeplearning #2021 #book @Machine_learn