This media is not supported in your browser
VIEW IN TELEGRAM
🔊 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
🖥 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
OReilly.Fundamentals.of.Deep.Learning.pdf
15.9 MB
Fundamentals of Deep Learning
Designing Next-Generation Machine Intelligence Algorithms
#Book #DL
@Machine_learn
Designing Next-Generation Machine Intelligence Algorithms
#Book #DL
@Machine_learn
🚀 Slapo: A Schedule Language for Large Model Training
Slapo is a schedule language for progressive optimization of large deep learning model training.
🖥 Github: https://github.com/awslabs/slapo
⭐️Paper: https://arxiv.org/abs/2302.08005v1
💻 Docs: https://awslabs.github.io/slapo/
@Machine_learn
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
Core.ML.Survival.Guide.pdf
6.9 MB
Core ML Survival Guide: More than you ever wanted to know about mlmodel files and the Core ML and Vision APIs (2020)
#Book #ML
@Machine_leaen
#Book #ML
@Machine_leaen
Deploying TensorFlow Vision Models in Hugging Face with TF Serving
https://huggingface.co/blog/tf-serving-vision
@Machine_learn
https://huggingface.co/blog/tf-serving-vision
@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
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
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
what you know about chatGPT?
Do you want us to give you information about this on the channel?
Do you want us to give you information about this on the channel?
Anonymous Poll
80%
👍
20%
👎
OReilly.Python.in.a.Nutshell.pdf
5.8 MB
Python in a Nutshell: A Desktop Quick Reference, 4th Edition (2023)
#python #2023 #book
@Machine_learn
#python #2023 #book
@Machine_learn
Hariom_Tatsat,_Sahil_Puri_,_Brad_Lookabaugh_Machine_Learning_and.pdf
13.6 MB
Machine Learning & Data Science Blueprints for Finance From Building
Trading Strategies to Robo-Advisors Using Python
Authors: Hariom Tatsat, Sahil Puri & Brad Lookabaugh (2021)
#ML #book
@Machin_learn
Trading Strategies to Robo-Advisors Using Python
Authors: Hariom Tatsat, Sahil Puri & Brad Lookabaugh (2021)
#ML #book
@Machin_learn
Packt.Agile.Model-Based.Systems.Engineering.Cookbook.pdf
35.4 MB
Agile Model-Based Systems Engineering Cookbook: Improve system development by applying proven recipes for effective agile systems engineering, 2nd Edition (2023)
#Book #2023
@Machine_learn
#Book #2023
@Machine_learn
ChatGPT.Prompts.Mastering.pdf
757.3 KB
ChatGPT Prompts Mastering: A Guide to Crafting Clear and Effective Prompts – Beginners to Advanced Guide (2023)
Author: Christian Brown
#book #GPT #2023
@Machine_learn
Author: Christian Brown
#book #GPT #2023
@Machine_learn
⏩ OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception.
OpenOccupancy first surrounding semantic occupancy perception benchmar.
🖥 Github: https://github.com/jeffwang987/openoccupancy
⏩ Paper: https://arxiv.org/abs/2303.03991v1
⭐️ Dataset: https://paperswithcode.com/dataset/synthcity
💨 Project: https://www.mmlab-ntu.com/project/styleganex/
@Machine_learn
OpenOccupancy first surrounding semantic occupancy perception benchmar.
🖥 Github: https://github.com/jeffwang987/openoccupancy
⏩ Paper: https://arxiv.org/abs/2303.03991v1
⭐️ Dataset: https://paperswithcode.com/dataset/synthcity
💨 Project: https://www.mmlab-ntu.com/project/styleganex/
@Machine_learn