PyTorch Pocket Reference (2021)
#book #2021 #PyTorch
@Machine_learn

Thai Natural Language
Processing
Word Segmentation, Semantic Analysis,
and Application #NLP #Book #2021
@Machine_learn

Formalising Natural
Languages: Applications
to Natural Language
Processing and Digital
Humanities #NLP #Book #2021

@Machine_learn

Hands-on Question
Answering Systems
with BERT
Applications in Neural
Networks and Natural
Language Processing #NLP #BERT #book #2021

@Machine_learn

Synthetic Data for Deep
Learning #DL #book #2021

@Machine_learn

Image Processing
and Capsule Networks
ICIPCN 2020 #CapsuleNet #DL #image #book #2020

@Machine_learn

Beginning Robotics
with Raspberry Pi
and Arduino
Using Python and OpenCV
Second Edition #OpenCv #book #2021

@Machine_learn

Smart Computing
Techniques and Applications
Proceedings of the Fourth International
Conference on Smart Computing
and Informatics, Volume 1 #book #2021

@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

🔗 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

💡 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

🧍‍♂ 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

🕸 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

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

Advanced Machine Learning with Python
#python #book #2021
@Machine_learn

📶 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

🌐 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

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

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

​​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/