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<h3>Introduction to Neural Networks</h3>
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<a href="01. Neural Network Intuition.html">01. Neural Network Intuition</a>
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<a href="07. Linear to Logistic Regression.html">07. Linear to Logistic Regression</a>
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<a href="14. Why Neural Networks.html">14. Why "Neural Networks"?</a>
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<a href="28. Multi-Class Cross Entropy.html">28. Multi-Class Cross Entropy</a>
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<a href="36. Neural Network Architecture.html">36. Neural Network Architecture</a>
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<a href="37. Feedforward.html">37. Feedforward</a>
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<h1 style="display: inline-block">13. Perceptrons II</h1>
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<h1 id="perceptron">Perceptron</h1>
<p>Now you've seen how a simple neural network makes decisions: by taking in input data, processing that information, and finally, producing an output in the form of a decision! Let's take a deeper dive into the university admission example to learn more about processing the input data.</p>
<p>Data, like test scores and grades, are fed into a network of interconnected nodes. These individual nodes are called <a href="https://en.wikipedia.org/wiki/Perceptron" target="_blank">perceptrons</a>, or artificial neurons, and they are the basic unit of a neural network. <em>Each one looks at input data and decides how to categorize that data.</em> In the example above, the input either passes a threshold for grades and test scores or doesn't, and so the two categories are: yes (passed the threshold) and no (didn't pass the threshold). These categories then combine to form a decision -- for example, if both nodes produce a "yes" output, then this student gains admission into the university.</p>
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Part 01-Module 03-Lesson 01-Introduction to Neural Networks
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2023-12-31
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Part 01-Module 03-Lesson 01-Introduction to Neural Networks （280个子文件） 
codecogseqn-43.gif 8KB perceptron-equation-2.gif 6KB heaviside-step-function-2.gif 3KB weight-label-reference.gif 3KB codecogseqn-49.gif 2KB sigmoid-derivative.gif 2KB hidden-layer-weights.gif 2KB codecogseqn-58.gif 919B
13. Perceptrons II.html 24KB 38. Multilayer Perceptrons.html 24KB 15. Perceptrons as Logical Operators.html 20KB 30. Gradient Descent.html 17KB 17. Perceptron Algorithm.html 16KB 36. Neural Network Architecture.html 13KB 22. Softmax.html 13KB 31. Gradient Descent The Code.html 13KB 39. Backpropagation.html 13KB 16. Perceptron Trick.html 13KB 27. Cross-Entropy 2.html 12KB 24. Maximum Likelihood.html 11KB 21. Discrete vs Continuous.html 11KB 25. Maximizing Probabilities.html 11KB 29. Logistic Regression.html 11KB 20. Log-loss Error Function.html 10KB 37. Feedforward.html 10KB 08. Classification Problems 1.html 10KB 05. Quiz Housing Prices.html 10KB 11. Higher Dimensions.html 10KB 28. Multi-Class Cross Entropy.html 10KB 12. Perceptrons.html 10KB 10. Linear Boundaries.html 10KB 04. A Note on Deep Learning.html 10KB 07. Linear to Logistic Regression.html 10KB 32. Perceptron vs Gradient Descent.html 9KB 01. Neural Network Intuition.html 9KB 26. Cross-Entropy 1.html 9KB 41. Create Your Own NN.html 9KB 40. Further Reading.html 9KB 09. Classification Problems 2.html 9KB 33. Continuous Perceptrons.html 9KB 14. Why Neural Networks.html 9KB 18. Non-Linear Regions.html 9KB 35. Non-Linear Models.html 9KB 23. One-Hot Encoding.html 9KB 19. Error Functions.html 9KB 34. Non-linear Data.html 9KB 03. Starting Machine Learning.html 9KB 06. Solution Housing Prices.html 9KB 02. Introduction to Deep Learning.html 9KB 42. Summary.html 9KB index.html 7KB
perceptron-graphics.001.jpeg 233KB
1-4-introduction-to-neural-networks2x.jpg 119KB miniflow.jpg 51KB
02. Module Introduction-uyLRFMI4HkA.mp4 15MB 03. ND013 01 L Intro To Neural Networks-UIycORUrPww.mp4 9.51MB 20. Error Functions-jfKShxGAbok.mp4 7.21MB 27. CrossEntropy V1-1BnhC6e0TFw.mp4 6.61MB 39. Backpropagation V2-1SmY3TZTyUk.mp4 6.52MB 01. 03 Deep Learning A01 Neural Network Intuition-UKEIHK5IifI.mp4 6.22MB 24. Maximum Likelihood 1-1yJx-QtlvNI.mp4 5.75MB 39. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.69MB 21. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.35MB 37. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.33MB 12. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.13MB 29. Error Function-V5kkHldUlVU.mp4 4.84MB 36. Combinando modelos-Boy3zHVrWB4.mp4 4.73MB 26. Cross Entropy 1-iREoPUrpXvE.mp4 4.22MB 28. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.14MB 22. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.01MB 10. Linear Boundaries-X-uMlsBi07k.mp4 3.85MB 24. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 3.85MB 30. Gradient Descent-rhVIF-nigrY.mp4 3.76MB 16. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.66MB 19. Error Functions-YfUUunxWIJw.mp4 3.54MB 39. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.31MB 32. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.2MB 36. Layers-pg99FkXYK0M.mp4 3.11MB 25. Quiz - Cross 1--xxrisIvD0E.mp4 3.02MB 17. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87MB 38. Multilayer perceptrons-Rs9petvTBLk.mp4 2.85MB 36. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.83MB 15. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.68MB 11. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59MB 21. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.26MB 34. Non-Linear Data-F7ZiE8PQiSc.mp4 2.14MB 27. Formula For Cross 1-qvr_ego_d6w.mp4 2.08MB 08. Classsification Example-Dh625piH7Z0.mp4 2.07MB 22. DL 18 S Softmax-n8S-v_LCTms.mp4 1.95MB 16. Perceptron Algorithm--zhTROHtscQ.mp4 1.92MB 36. Multiclass Classification-uNTtvxwfox0.mp4 1.88MB 25. Quiz Cross Entropy-njq6bYrPqSU.mp4 1.86MB 22. Quiz - Softmax-NNoezNnAMTY.mp4 1.73MB 37. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.72MB 23. One-Hot Encoding-AePvjhyvsBo.mp4 1.65MB 09. Classification Example-46PywnGa_cQ.mp4 1.62MB 29. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.49MB 05. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.48MB 39. Chain Rule-YAhIBOnbt54.mp4 1.46MB 42. Conclusion-m8xslYUBXYo.mp4 1.43MB共 280 条
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