Best of Deep Learning — February 2026

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   Article

   Machine Learning Mastery·13w

   Introduction to Small Language Models: The Complete Guide for 2026

   Small language models (SLMs), typically under 10 billion parameters, are increasingly preferred in production AI systems due to their cost, latency, and privacy advantages over large models. Modern SLMs like Phi-3 Mini, Llama 3.2 3B, and Mistral 7B achieve competitive performance through techniques like knowledge distillation, high-quality training data, quantization, and architectural optimizations. For 80% of predictable, repeated production tasks, SLMs can cut costs by up to 95% and respond in 50–200ms locally. Real-world use cases include customer support, code assistance, document processing, and mobile apps. A hybrid router pattern—SLMs for routine queries, LLMs for complex ones—is emerging as the practical production standard. Getting started requires only Python skills, domain-specific data, and a few hours of GPU time using tools like Ollama and Hugging Face Transformers.

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   Article

   NVIDIA Developer·16w

   Build with Kimi K2.5 Multimodal VLM Using NVIDIA GPU-Accelerated Endpoints

   Kimi K2.5 is a new open-source vision language model with 1T total parameters (32.86B active) that supports text, image, and video inputs with a 262K context length. The model uses a mixture-of-experts architecture with 384 experts and achieves 3.2% parameter activation per token. Developers can access GPU-accelerated endpoints for free prototyping through build.nvidia.com, deploy using vLLM, or fine-tune with NVIDIA NeMo Framework and AutoModel for domain-specific tasks.

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   Video

   bycloud·14w

   DeepSeek Just Added Parameters Where There Were NONE.

   DeepSeek's new MHC (Manifold Constraint Hyperconnections) research challenges the decade-old standard of single residual connections in neural networks by introducing multiple parallel residual connections with learnable weights. The approach stabilizes the previously unstable hyperconnection concept through doubly stochastic matrix constraints, achieving consistent performance gains across benchmarks with only 6.7% compute overhead through aggressive hardware optimization. Testing on a 27B parameter model showed improvements across all benchmarks with better training behavior and scalability.

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