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Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Guides readers through fundamental concepts of deep learning, including computational graphs, single neurons, and feedforward neural networks, often using TensorFlow for practical implementation. The author emphasizes understanding the mathematical underpinnings of algorithms, discusses optimization techniques like gradient descent and Adam, and addresses critical aspects such as regularization to combat overfitting, metric analysis for model evaluation, and hyperparameter tuning. Chapters further explore advanced network architectures like convolutional and recurrent neural networks, and demonstrate their application in real-world scenarios.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Applied-Deep-Learning-Case-Based-Understanding-ebook/dp/B07H6D9NQ8?&linkCode=ll1&tag=cvthunderx-20&linkId=b12793360955b5dd9433db6c021197b7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
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You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Applied-Deep-Learning-Case-Based-Understanding-ebook/dp/B07H6D9NQ8?&linkCode=ll1&tag=cvthunderx-20&linkId=b12793360955b5dd9433db6c021197b7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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By CyberSecurity Summary
2.2
55 ratings
Guides readers through fundamental concepts of deep learning, including computational graphs, single neurons, and feedforward neural networks, often using TensorFlow for practical implementation. The author emphasizes understanding the mathematical underpinnings of algorithms, discusses optimization techniques like gradient descent and Adam, and addresses critical aspects such as regularization to combat overfitting, metric analysis for model evaluation, and hyperparameter tuning. Chapters further explore advanced network architectures like convolutional and recurrent neural networks, and demonstrate their application in real-world scenarios.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Applied-Deep-Learning-Case-Based-Understanding-ebook/dp/B07H6D9NQ8?&linkCode=ll1&tag=cvthunderx-20&linkId=b12793360955b5dd9433db6c021197b7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Applied-Deep-Learning-Case-Based-Understanding-ebook/dp/B07H6D9NQ8?&linkCode=ll1&tag=cvthunderx-20&linkId=b12793360955b5dd9433db6c021197b7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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