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Скачать или смотреть Perceptron Working & Components | Generative Ai | Basic to Advance

  • Learning Logic
  • 2024-09-25
  • 402
Perceptron Working & Components  | Generative Ai | Basic to Advance
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Описание к видео Perceptron Working & Components | Generative Ai | Basic to Advance

Perceptron Working & Components | Generative Ai | Basic to Advance

Check out this course on Euron: generative-ai-basic-to-advance

https://euron.one/course/generative-a...

Unlock Your Potential with Learning Logic , as we dive deep into why Neural Networks are crucial for Big Data. This video will transform your understanding and career by exploring the essential components and intricate workings of neural networks. Discover the truth about why these networks are indispensable when handling vast data sets. From understanding input layers to the power of activation functions, gain insights that are crucial for your tech journey. Explore now and don't miss out on this opportunity to elevate your skills with #AI and #ArtificialIntelligence. Are you ready to transform your projects and take your career to the next level? Watch now, and if you believe in unlocking these insights, share this video to reach as many people as possible. Remember to subscribe, hit the bell icon, and join us for more tech updates and programming tips. #coding #remotework #techjobs #innovation #techupdates #programming #SkillDevelopment #CareerJourneyTips

#remotework #innovation

#dataanalytics #machinelearning #ai #machinelearningfullcourse #unsupervisedlearning

#tensorflow #ai #datascience #unsupervisedlearning #machinelearning

CHAPTERS:
00:00 - Introduction
00:48 - Importance of Neural Networks
02:03 - Understanding Neural Networks
03:51 - Applications of Neural Networks
04:39 - Neural Network Layers
06:09 - Components of a Layer
08:59 - Understanding Bias in Neural Networks
10:52 - Activation Function Explained
14:05 - Learning Layer in Neural Networks
14:55 - Output Layer Overview
16:22 - Overfitting in Neural Networks
17:03 - Hidden Layers in Neural Networks
17:49 - Conclusion


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