3D Deep Learning with Python — Design and develop Computer Vision models with 3D data using PyTorch3D: http://
amzn.to/491yDwh v/ @PacktDataML ————
#AI #MachineLearning #ML #DataScience #DataScientist #PyTorch
RESEARCH
-
3D Deep Learning with Python — PyTorch3D for Computer Vision
By
–
-
Deep Learning with TensorFlow and Keras 3rd Ed. Book Announcement
By
–
Deep Learning with TensorFlow and Keras — Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models (3rd Ed., 667 pages): http://
amzn.to/3gitVEJ v/ @PacktDataML —————
#DataScientist #DataScience #AI #MachineLearning #ML -
Practical Guide to Reinforcement Learning from Human Feedback released
By
–
New release from @PacktDataML available at http://
amzn.to/3PMn1ZL A Practical Guide to Reinforcement Learning from Human Feedback (RLHF). Amazon Summary: RLHF is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning -
New benchmark for evaluating autonomous AI agent performance
By
–
a researcher at ICML opens his paper with a question. what does your AI agent do when nobody’s watching? he builds a benchmark. multi-step tasks. tool use. coding assistants, research agents, the kind of thing you trust to run for hours without supervision. he gives the
-
New book: RAG-Driven Generative AI (2nd ed.)
By
–
New Release (2nd edition) from @PacktDataML available at http://
amzn.to/4tULP1b RAG-Driven Generative AI — Build MAS-RAG with DualRAG, GraphRAG, multimodal video pipelines, and Oracle Database 23ai 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀:
Master DualRAG by combining vector search with SQL -
Text-Conditional JEPA for Learning Semantically Rich Visual Representations
By
–
Text-Conditional JEPA for Learning Semantically Rich Visual Representations Paper: https://
arxiv.org/abs/2605.03245 -
Apple Researchers Introduce TC-JEPA for Vision-Language Learning
By
–
Looks like Apple is very interested in JEPA! What if your AI could “read” an image’s caption to solve visual puzzles? Apple researchers present TC-JEPA: a new self-supervised method that uses image captions to guide masked patch predictions. By conditioning on text, the model
-
Causal Software Engineering: A Vision and Roadmap
By
–
Causal Software Engineering: A Vision and Roadmap Paper: https://
arxiv.org/abs/2605.02454 -
Introduction to TimeGAN for Time-Series Data Synthesis
By
–
TimeGAN/TGAN! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/T-S-Gen-Networ
ks
… -
Design Multi-Agent AI Systems — New Packt Release
By
–
New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI