🔥 Applied Deep Learning Course

🖥 Github: https://github.com/maziarraissi/Applied-Deep-Learning

⏩ Paper: https://arxiv.org/pdf/2301.11316.pdf

➡️Videos: https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4
@Machine_learn

SLCNN: Sentence-Level Convolutional Neural Network for Text Classification

Ali Jarrahi, Leila Safari , Ramin Mousa

abstract: Text classification is a fundamental task in natural language processing (NLP). Several recent studies show the success of deep learning on text processing. Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of text classification. In this paper, new baseline models have been studied for text classification using CNN. In these models, documents are fed to the network as a three-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the text. Besides, analysing adjacent sentences allows extracting additional features. The proposed models have been compared with the state-of-the-art models using several datasets.
Author: @Raminmousa

@Machine_learn

STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation (ICRA 2023)

🖥 Github: https://github.com/ucaszyp/steps

⏩ Paper: https://arxiv.org/abs/2302.01334v1

➡️ Dataset: https://paperswithcode.com/dataset/nuscenes

@Machine_learn

Chinese doors
@Machine_learn

ChatGPT for Nonfiction Authors

@Machine_learn

🔊 Audio-Visual Segmentation (AVS)


🖥 Github: https://github.com/OpenNLPLab/AVSBench

✅️ Paper: https://arxiv.org/pdf/2301.13190.pdf

⭐️ Project: https://opennlplab.github.io/AVSBench/

✅️ Dataset: http://www.avlbench.opennlplab.cn/download

🔹 Benchmark: http://www.avlbench.opennlplab.cn/


@Machine_learn

Fundamentals of Deep Learning
Designing Next-Generation Machine Intelligence Algorithms
#Book #DL
@Machine_learn

Internet of Things Security Architectures and Security Measures
#Book #iot
@Machine_learn

🚀 Slapo: A Schedule Language for Large Model Training

Slapo is a schedule language for progressive optimization of large deep learning model training.

pip3 install slapo

🖥 Github: https://github.com/awslabs/slapo

⭐️Paper: https://arxiv.org/abs/2302.08005v1

💻 Docs: https://awslabs.github.io/slapo/

@Machine_learn

Inside Deep Learning: Math, Algorithms, Models (2022)
#book #DL

@Machine_learn

📡 Learning Visual Representations via Language-Guided Sampling

New approach deviates from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than minimize a cross-modal similarity.



🖥 Github: https://github.com/mbanani/lgssl

⭐️Paper: https://arxiv.org/abs/2302.12248v1

⏩Pre-trained Checkpoints: https://www.dropbox.com/sh/me6nyiewlux1yh8/AAAPrD2G0_q_ZwExsVOS_jHQa?dl=0

💻 Dataset : https://paperswithcode.com/dataset/redcaps

@Machine_learn

🖥 pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

A bare-bones Python library for quality diversity optimization.

🖥 Github: https://github.com/icaros-usc/pyribs

⏩ Paper: https://arxiv.org/abs/2303.00191v1

⭐️ Dataset: https://paperswithcode.com/dataset/quality-diversity-benchmark-suite

@Machine_learn