The sigmoid function is a classical activation function that modulates input values, growing to half at zero and reaching one for larger values.

Neural networks are a fundamental concept in deep learning, representing a simple yet powerful way to process data through multiple layers of computation. The feedforward neural network takes an input, multiplies it by a matrix, applies a nonlinear operation, and repeats this process to transform the input into an output.

DeepSeek's R1 reasoning model achieves impressive performance by applying reinforcement learning specifically to think step by step through complex problems, similar to OpenAI's approach with their model, o1.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.

Reinforcement learning allows us to tackle problems where we don't know the answers, focusing on whether the model's outputs are good or not, rather than needing predefined solutions.

DeepSeek's V3 model utilizes a mixture of experts architecture, activating only 37 billion out of 671 billion parameters for each token prediction, significantly reducing computation compared to models like Llama 3, which activates all 405 billion parameters.

Vzero is designed to be extremely easy to use, generating refined and realistic outputs for users.

The more reinforcement learning o1 undergoes, the more it improves, meaning that when users present complex problems, the longer o1 is allowed to think, the more accurate its responses become.

DeepSeek's reasoning model, R1, is generating excitement for its potential to drive down compute costs and improve efficiency.