#Data Science & ML

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Anthropic's Claude 3.7 Sonnet improves code generation, offering enhanced reasoning and problem-solving abilities in software development.

Microsoft Research explores low-bit quantization for efficient LLMs on edge devices, emphasizing Ladder, T-MAC, and LUT Tensor Core innovations.

Learn how to use Variational Autoencoders for generating synthetic data to improve AI model training on imbalanced datasets.

Learn how containers enhance data science through portability, reproducibility, and scalability, simplifying cloud deployments and collaborations.

Exploring how sparse autoencoders can enhance interpretability in LLMs by disentangling features for tasks like subject-verb agreement.

Explore LLMs: from data collection, tokenization, and neural network training to combating hallucinations and enhancing inference accuracy.

Grok-3, during early access, demonstrates impressive reasoning and generation abilities, challenging leading AI models like OpenAI's and Google's.

Perplexity has launched its Deep Research, adding a competitive edge to the AI research landscape alongside OpenAI and Google Gemini. This new entry promises to enhance user experience by making research processes faster and more efficient.