Build a Career in Data Science
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
@Machine_learn

💬 GLIGEN: Open-Set Grounded Text-to-Image Generation

GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.


⭐️ Project: https://gligen.github.io/

⭐️ Demo: https://aka.ms/gligen

✅️ Paper: https://arxiv.org/abs/2301.07093

🖥 Github: https://github.com/gligen/GLIGEN

@Machine_learn

AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling

Autoregressive approach for modeling dynamically deforming human bodies by Meta.


🖥 Github: github.com/facebookresearch/AutoAvatar

⭐️ Project: zqbai-jeremy.github.io/autoavatar

✅️ Paprer: arxiv.org/pdf/2203.13817.pdf

⏩ Dataset: https://amass.is.tue.mpg.de/index.html

⭐️ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4

@Machine_learn

🖥 Deep BCI SW ver. 1.0 is released.

🖥 Github: https://github.com/DeepBCI/Deep-BCI

⏩ Paper: https://arxiv.org/abs/2301.08448v1

➡️ Project: http://deepbci.korea.ac.kr/

@Machine_learn

✅️ StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis




🖥 Github: github.com/autonomousvision/stylegan-t

✅️ Paper: arxiv.org/pdf/2301.09515.pdf

⭐️ Project: sites.google.com/view/stylegan-t

✔️ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo

🖥 Projected GAN: https://github.com/autonomousvision/projected-gan

@Machine_learn

Pandas Basics
Oswald Campesato
#book #pandas #python
@Machne_learn

PACO: Parts and Attributes of Common Objects

🖥 Github
⭐️ Paper
➡️Project

@Machine_learn

❔ PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development



🖥 Github: https://github.com/primeqa/primeqa

🖥 Notebooks: https://github.com/primeqa/primeqa/tree/main/notebooks

✅️ Paper: https://arxiv.org/abs/2301.09715v2

⭐️ Dataset: https://paperswithcode.com/dataset/wikitablequestions

✔️ Docs: https://primeqa.github.io/primeqa/installation.html

@Machine_learn

🔥 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