Tags: deep learning*

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  1. This article introduces Google's top AI applications, providing a guide on how to start using them, including Google Gemini, Google Cloud, TensorFlow, Experiments with Google, and AI Hub.
  2. An article discussing the concept of monosemanticity in LLMs (Language Learning Models) and how Anthropic is working on making them more controllable and safer through prompt and activation engineering.
  3. A deep dive into the theory and applications of diffusion models, focusing on image generation and other tasks, with examples and PyTorch code.
  4. An article discussing the use of Deep Q-Networks (DQNs) in reinforcement learning, which combines the principles of Q-Learning with function approximation capabilities of neural networks to address limitations of traditional Q-learning such as scalability issues and inability to handle continuous state and action spaces.
  5. Lambda Stack is an all-in-one package that provides a one line installation and managed upgrade path for deep learning and AI software, ensuring that you always have the most up-to-date versions of PyTorch, TensorFlow, CUDA, CuDNN, and NVIDIA Drivers.
  6. This article explains the concept of abstraction in neural networks and its connection to generalization. It also discusses how different components in neural networks contribute to abstraction and reveals an interesting duality between abstraction and generalization.
  7. Stay informed about the latest artificial intelligence (AI) terminology with this comprehensive glossary. From algorithm and AI ethics to generative AI and overfitting, learn the essential AI terms that will help you sound smart over drinks or impress in a job interview.
  8. This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
  9. Kolmogorov-Arnold Networks (KANs) and explains how to apply them for time series forecasting using Python. Basics of KANs and their connection to deep learning models such as the multilayer perceptron (MLP), which is used in state-of-the-art forecasting models.
  10. This article discusses the latest open LLM (large language model) releases, including Mixtral 8x22B, Meta AI's Llama 3, and Microsoft's Phi-3, and compares their performance on the MMLU benchmark. It also talks about Apple's OpenELM and its efficient language model family with an open-source training and inference framework. The article also explores the use of PPO and DPO algorithms for instruction finetuning and alignment in LLMs.

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