Best of Deep Learning — February 2025

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   Video

   Fireship·1y

   OpenAI o3 tries to curb stomp DeepSeek...

   Recent restrictions have seen the banning of Deep Seek by countries like Italy, the US, Australia, and Taiwan. Meanwhile, OpenAI has launched the new 03 Mini model and a Deep Research feature for Pro users, aiming to remain competitive. These developments are part of a broader trend in the AI landscape, with open-source solutions making rapid progress. Despite corporate efforts, some AI tools face performance issues, and Google's Gemini has similar features to OpenAI's new offerings.

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2. 2
   Article

   Data Engineer Things·1y

   10 minutes are all you need to understand how Transformers work in LLM

   Understanding how transformers work in large language models (LLMs) can be achieved quickly by breaking down the steps involved in the process. Starting from tokenization, where input data is converted into tokens, these tokens are then embedded into numerical representations understood by the model. These embeddings are processed through multiple transformer layers that use attention mechanisms to determine the importance of each token in relation to others. Finally, the processed data is projected back onto the vocabulary to predict the next token in a sequence. This foundational knowledge helps in exploring further intricacies of models like GPT-2.

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   4
3. 3
   Article

   Machine Learning Mastery·1y

   The 2025 Machine Learning Toolbox: Top Libraries and Tools for Practitioners

   2024 marked a pivotal year for machine learning (ML) and artificial intelligence (AI) as they became mainstream, with the introduction of large language models (LLM) such as ChatGPT leading the way. In 2025, trends like generative AI and model explainability will continue shaping the ML landscape. Key libraries and tools for ML practitioners include LangChain for LLM application development, JAX for high-performance numerical computation, Fastai for deep learning, InterpretML for model explainability, and TokenSHAP for token-level analysis in LLMs. Embracing these tools can help practitioners build robust and transparent ML solutions.

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4. 4
   Article

   DiamantAI·1y

   Reinforcement Learning Explained

   Reinforcement learning involves teaching an AI to adapt and learn by interacting with its environment. Key topics include agents & environment, policy, Q-learning, the exploration-exploitation dilemma, function approximation & memory, hierarchical methods, meta-learning, and multi-agent setups.

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