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AI got cheap enough that Duolingo’s most expensive plan may not survive it. I read the earnings call transcript and opened the app to see what is actually changing for learners.
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Explore how Google Maps uses graph neural networks to predict estimated arrival times, including graph construction, multi-horizon models, variance reduction, inference, and production engineering.
Explore the future of search with Nils Reimers, discussing RAG, multilingual embedding, complex data search, and Cohere-Rerank. Gain insights on LLMs' impact on search technology.
Explore Flash Attention 2.0 with author Tri Dao, covering its motivation, improvements, and future directions. Gain insights into memory bottlenecks and IO awareness in attention mechanisms for long sequences.
Build and deploy a Python Streamlit web app using the Hugging Face Inference API.
A candid code walkthrough of an unfinished Deep Q-Network project, focusing on debugging reinforcement-learning components, logging, and implementation workflow.
Explains EfficientNetV2’s neural architecture search, fused-MBConv blocks, progressive training, and regularization for smaller models and faster image-model training.
An explanation of MuZero’s model-based reinforcement learning approach, which learns game dynamics and uses tree search to play Atari, Go, chess, and shogi without rules.
A paper explainer on training a robotic hand in simulation to solve a Rubik’s Cube using automatic domain randomization for sim-to-real transfer.
Explains how AlphaGo Zero and AlphaZero master Go, chess, and shogi through self-play reinforcement learning without human game data or domain knowledge.
A detailed walkthrough of AlphaGo’s deep neural networks, reinforcement-learning policies, and Monte Carlo tree search for playing Go.
A detailed walkthrough of the DQN paper, from experience replay and target networks to Atari training results.
A research-oriented guide to getting started with graph machine learning, covering applications, graph embeddings, GNNs, geometric deep learning, resources, and a GAT project.
Walk through a Graph Attention Network implementation, from Cora graph data and edge indices to neighborhood-aware softmax and aggregation.
A visual deep dive into Temporal Graph Networks, explaining how memory, temporal sampling, and graph attention model evolving interactions.
Explains CLIP’s contrastive image-text pretraining and its zero-shot transfer, embedding quality, robustness, and limitations.
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