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Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.
Explore the future of search with Nils Reimers, discussing RAG, multilingual embedding, complex data search, and Cohere-Rerank. Gain insights on LLMs' impact on search technology.
Explore Flash Attention 2.0 with author Tri Dao, covering its motivation, improvements, and future directions. Gain insights into memory bottlenecks and IO awareness in attention mechanisms for long sequences.
Insights from MosaicML's first employee on the company's journey, efficiency strategies, competitive edge, and challenges in AI model evaluation.
Comprehensive introduction to MLOps, covering key concepts and resources for building automated ML-powered applications. Includes walkthroughs of popular courses and practical insights for aspiring ML engineers.
Comprehensive guide on building a deep learning machine, covering component selection, cost comparisons, and practical tips for assembling a high-performance workstation tailored to AI tasks.
Explore neural audio compression techniques achieving 10x compression rates, with in-depth analysis of paper and code implementation, including VQ-VAE, VQ-GAN, and AudioGen applications.
Explore text-guided audio synthesis with AudioGen, delving into its architecture, audio representation, language modeling, and innovative approaches for generating diverse sounds from textual descriptions.
Explore OpenAI's Whisper: a robust speech recognition system using large-scale weak supervision. Dive into the paper, code, and architecture, covering dataset collection, evaluation metrics, and practical implementation.
Explore 3D parallelism in large language models: pipeline, model, and data parallelism. Dive into BLOOM's codebase to understand scaling techniques behind recent ML breakthroughs.
Deep dive into OPT-175B codebase, covering setup, training script, model construction, CUDA kernels, and mixed precision training concepts for large language models.
Explore three groundbreaking large language models: BLOOM, OPT, and GPT-NeoX-20B. Gain insights into their development, challenges faced during training, and unique features of each project.
Learn three methods to use Stable Diffusion: HuggingFace Spaces, Colab notebooks, and local setup. Gain practical skills in image generation, metadata reproduction, and interpolation techniques.
Comprehensive guide to scaling machine learning models, covering parallelism techniques, mixed precision training, and memory optimization strategies for training large-scale AI models efficiently.
Explore DeepMind's DQN implementation process, including project organization, code walkthrough, and visualization techniques for reinforcement learning algorithms.
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