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➡️ Machine Learning from Scratch, part 42: why GPU and not CPU. A neural network spends almost all of its time multiplying numbers and adding them up. In every layer each neuron receives the whole input vector, multiplies each number by its own weight and adds them up, so with the weights of all neurons as the rows of one table, the layer becomes one matrix times the input vector. In training a whole mini batch goes in at once, every layer becomes a matrix times a matrix, and the backward pass f
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A model can score 99% on its training data and still fall apart on real data. That's overfitting: instead of learning the general pattern, the model memorizes the specific examples it was trained on, noise and quirks included. The only honest test is data it has never seen, which is why machine learning holds back a separate test set, and why more varied data, simpler models and regularization help. Students hit the same trap when they memorize practice answers instead of the idea behind them. #
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Researchers used fMRI and EEG scans to observe the brain activity of 63 healthy adults after taking a standardized dose of psilocybin. By applying machine learning, they found that rather than simply scrambling neural networks, the drug reorganized brain activity into structured patterns. These patterns closely tracked the participants' immediate sensory experiences, particularly for those who reported a dissolving sense of self. Read more here: https://www.psypost.org/beneath-psilocybins-appare
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1. Ingénieur IA / Machine Learning 2. AI Product Manager 3. Ingénieur Data / Data Engineer 4. Data Scientist 5. Ingénieur Cybersécurité 6. Cloud Engineer / Architecte Cloud 7. DevOps / Platform Engineer 8. Ingénieur Robotique 9. Ingénieur en semi-conducteurs 10. Ingénieur en énergies renouvelables 11. Ingénieur batteries & stockage d’énergie 12. Expert FinOps / optimisation des coûts Cloud 13. Spécialiste Gouvernance & Risques IA 14. Architecte Solutions IA 15. Ingénieur systèmes autonomes 16. P
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