And here is the final result with 5 recommended books and their respective scores. Code: https://
github.com/PacktPublishin
g/Hands-On-Graph-Neural-Networks-Using-Python/blob/main/Chapter17/chapter17.ipynb
… You'll find two other advanced use cases, related to time series forecasting and anomaly detection (the topic of my PhD!).
MACHINE LEARNING
-
Graph Neural Networks: Book Recommendations and Advanced Applications
By
–
-
Graph Neural Networks: Creating and Implementing GNNs with PyTorch
By
–
The goal is to help you create, implement, and apply GNNs to solve real-world problems! You'll learn how to create graph datasets, implement GNNs using Python and #PyTorch Geometric (from @PyG_Team
), and build/train models for classification, prediction, and more. -
GNN Guide: Building Recommender Systems with LightGCN
By
–
This book is carefully crafted to provide a step-by-step guide for those new to the world of #GNN, while also offering advanced use cases and examples. For example, the following BookCrossing dataset is used to build a recommender system using LightGCN.
-
Graph Neural Networks Hands-On Guide Released with Python Code
By
–
I am thrilled to announce the release of my book, Hands-On Graph Neural Networks! It's been almost a year's worth of hard work, research, and collaboration with fellow experts in the field. The entire code is available on GitHub: https://
github.com/PacktPublishin
g/Hands-On-Graph-Neural-Networks-Using-Python
… #AI #Python -
Causal Language Models: Seq2Seq Architecture Without Cross-Attention
By
–
Causal LMs are seq2seq models just with a causal mask and shared encoder decoder with no cross attention.
-
Elsevier Journal Editorial Board Launches Non-Profit Open Access
By
–
Elsevier journal NeuroImage's editorial board resigned. They're starting their own non-profit open access journal. Reminiscent of @JmlrOrg
: While founded in 2000, it picked up steam in 2001 after editorial board of "Machine Learning Journal" did the same https://
jmlr.org/history.html -
LLM Study: Retrieval Impact on Knowledge-Intensive Tasks
By
–
A comprehensive study of LLMs with and without retrieval. Retrieval helps with knowledge-intensive tasks significantly. @wbx_life @_weiping @PengXu51108979 @MohammadShoeybi @ChaoweiX @ctnzr
-
Caltech Welcomes Researcher for AutoML Science Collaboration
By
–
Great to have you with us @crwhite_ml at @Caltech to work on challenging problems in AutoML for Science!
-
Network filtering algorithm detects hierarchical community structures in data
By
–
New network filtering algorithm can be used to find hierarchical community structure in various types of data. The algorithm can detect structures in financial time series data sets as well as image and electrocardiogram data. Paper: https://
bit.ly/3JkkeQB -
Fine-tuning Pre-trained Models: Computer Vision Deep Learning Guide
By
–
🚀 Dive into our latest video on harnessing pre-trained models, with a focus on fine-tuning!https://t.co/1uBhnJJRVH#keras #tensorflow #finetuning #dataset #ai #computervision #machinelearning #deeplearning #artificialintelligence #learnopencv #opencv pic.twitter.com/7p5v24rFMo
— Satya Mallick (@LearnOpenCV) 17 avril 2023Dive into our latest video on harnessing pre-trained models, with a focus on fine-tuning! https://
youtube.com/watch?v=dGuY1y
tu1zs
… #keras #tensorflow #finetuning #dataset #ai #computervision #machinelearning #deeplearning #artificialintelligence #learnopencv #opencv