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Udemy - Python neural networks from scratch. Build them step by step

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Added 4 years ago by freecoursewb in Other
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Udemy - Python neural networks from scratch. Build them step by step (Size: 807.1 MB)
  001 Adding Hidden Layers.mp4 135.3 MB
  001 Adding Hidden Layers_en.srt 17.6 KB
  001 Gradient Descent.mp4 47.9 MB
  001 Gradient Descent_en.srt 11.3 KB
  001 Linear Regression.mp4 48.1 MB
  001 Linear Regression_en.srt 14 KB
  001 Mini Batch Gradient Descent.mp4 52.6 MB
  001 Mini Batch Gradient Descent_en.srt 8.2 KB
  001 Real Data.mp4 34.9 MB
  001 Real Data_en.srt 6 KB
  001 Softmax.mp4 95.7 MB
  001 Softmax_en.srt 21.4 KB
  001 What can you expect from this course.mp4 18.7 MB
  001 What can you expect from this course_en.srt 9.6 KB
  001 What is a neural network.mp4 10.2 MB
  001 What is a neural network_en.srt 4.4 KB
  002 Classification Introduction.mp4 10.2 MB
  002 Classification Introduction_en.srt 5.7 KB
  002 Cost functions.mp4 52 MB
  002 Cost functions_en.srt 15.1 KB
  002 Non Linear Data.mp4 27.2 MB
  002 Non Linear Data_en.srt 3.7 KB
  002 Recap.mp4 10.3 MB
  002 Recap_en.srt 3.2 KB
  002 Recognizing Handwritten Digits.html 0 B
  002 Second Input.mp4 63.5 MB
  002 Second Input_en.srt 10 KB
  002 Who are you and what do you need.mp4 7.8 MB
  002 Who are you and what do you need_en.srt 4.1 KB
  003 Activation.mp4 59.1 MB
  003 Activation_en.srt 9.5 KB
  003 Bias.mp4 54.3 MB
  003 Bias_en.srt 8.9 KB
  003 Conclusion.mp4 10.5 MB
  003 Conclusion_en.srt 3 KB
  003 Normalise Data.mp4 29.2 MB
  003 Normalise Data_en.srt 5.5 KB
  003 Random Weights.mp4 39.3 MB
  003 Random Weights_en.srt 5.5 KB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  external-assets-links.txt 102.4 B
  ▲ 44 total files

Description


Python neural networks from scratch. Build them step by step
https://DevCourseWeb.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 20 lectures (3h 10m) | Size: 594.1 MB

Understand linear regression, gradient descent and many more by building neural networks without libraries or frameworks

What you'll learn
Understand the ideas behind neural networks.
Learn how to use plain Python to create neural networks.
Learn concepts like feed forward, backward propagation, gradient descent, regression step by step.
Understand how Softmax, ReLU and Sigmoid allow you to approximate complex non-linear prediction functions.
Realise that neural networks are not magic and can be implemented without using libraries, in any language you desire.

Requirements
You have an interest in neural networks.
You have some programming experience in Python or another language.
There will be no exercises in this course. Feel free to write along with the code examples.

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