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perceptron

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  1. Introduction (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  2. Linear Binary Classification (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  3. Sentiment Analysis and Basic Feature Extraction (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  4. Basics of Learning, Gradient Descent (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  5. Perceptron (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  6. Perceptron as Minimizing Loss (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  7. Logistic Regression (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  8. Sentiment Analysis (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  9. Optimization Basics (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  10. Multiclass Classification (Natural Language Processing at UT Austin)Natural Language Processing at UT Austin, 2021-2022 version (Greg Durrett)Notes
  11. Machine Learning Basics - Xavier Giró - UPC TelecomBCN Barcelona 2020DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  12. ee53 lec01 The human brainNeural Networks for Signal Processing – INotes
  13. ee53 lec02 Introduction to Neural NetworksNeural Networks for Signal Processing – INotes
  14. ee53 lec03 Models of a neuronNeural Networks for Signal Processing – INotes
  15. ee53 lec04 Feedback and network architecturesNeural Networks for Signal Processing – INotes
  16. ee53 lec05 Knowledge representationNeural Networks for Signal Processing – INotes
  17. ee53 lec06 Prior information and invariancesNeural Networks for Signal Processing – INotes
  18. ee53 lec07 Learning processesNeural Networks for Signal Processing – INotes
  19. ee53 lec08 Perceptron 1Neural Networks for Signal Processing – INotes
  20. ee53 lec10 Batch perceptron algorithmNeural Networks for Signal Processing – INotes
  21. ee53 lec11 Perceptron and Bayes classifierNeural Networks for Signal Processing – INotes
  22. PixelCNN, Wavenet, Normalizing Flows - Santiago Pascual - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  23. Variational Autoencoders VAE - Santiago Pascual - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  24. Generative Adversarial Networks GAN - Santiago Pascual - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  25. Lifelong / Incremental Deep Learning - Ramon Morros - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  26. Transfer Learning & Domain Adaptation - Ramon Morros - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  27. Deep Reinforcement Learning: MDP & DQN - Xavier Giro-i-Nieto - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  28. The Perceptron - Xavier Giro - UPC Barcelona 2018DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  29. Attention-based models (2/2) - Marta R. Costa Jussà (UPC DLAI D8L2 2018)DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  30. Attention-based mechanisms - Marta R. Costa Jussa (UPC IDL D4L1 2018)DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  31. PixelCNN, Wavenet & Variational Autoencoders - Santiago Pascual - UPC 2017DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  32. Unsupervised Learning - Xavier Giro-i-Nieto - UPC 2017DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  33. Lecture 11: Autoencoder for Representation Learning and MLP InitializationDeep Learning For Visual ComputingNotes
  34. Lec12 MNIST handwritten digits classification using auto encoders (Hands on)Deep Learning For Visual ComputingNotes
  35. Lec13 Fashion MNIST classification using auto encodersDeep Learning For Visual ComputingNotes
  36. Lec14 ALL-IDB Classification using auto encodersDeep Learning For Visual ComputingNotes
  37. Lec15 Retinal Vessel Detection using auto encoders (Hands on)Deep Learning For Visual ComputingNotes
  38. Lec06 Introduction to Deep Learning with Neural Networks (Part 1)Deep Learning For Visual ComputingNotes
  39. Lec07 Introduction to Deep Learning with Neural Networks (Part 2)Deep Learning For Visual ComputingNotes
  40. Lec08 Multilayer Perceptron and Deep Neural Networks (Part 1)Deep Learning For Visual ComputingNotes
  41. Lec09 Multilayer Perceptron and Deep Neural Networks (Part 2)Deep Learning For Visual ComputingNotes
  42. Lec10 Classification with Multilayer Perceptron (Hands on)Deep Learning For Visual ComputingNotes
  43. Multilayer Perceptron (DLAI D2L1 2017 UPC Deep Learning for Artificial Intelligence)DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  44. Deep Reinforcement Learning - Xavier Giro-i-Nieto- UPC Barcelona 2017DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes
  45. The Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intelligence)DLAI - Deep Learning for Artificial Intelligence @ UPC BarcelonaNotes

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