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Title: GitHub - isikdogan/deep_learning_tutorials: deep learning: theory + practice

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Description: deep learning: theory + practice. Contribute to isikdogan/deep_learning_tutorials development by creating an account on GitHub.

Open Graph Description: deep learning: theory + practice. Contribute to isikdogan/deep_learning_tutorials development by creating an account on GitHub.

X Description: deep learning: theory + practice. Contribute to isikdogan/deep_learning_tutorials development by creating an account on GitHub.

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Lecture #1: Introductionhttps://youtu.be/nmnaO6esC7c
Lecture #2: Artificial Neural Networks Demystifiedhttps://youtu.be/oS5fz_mHVz0
Lecture #3: Artificial Neural Networks: Going Deeperhttps://youtu.be/_XPkAxm0Yx0
Lecture #4: Overfitting, Underfitting, and Model Capacityhttps://youtu.be/ms-Ooh9mjiE
Lecture #5: Regularizationhttps://youtu.be/NRCZJUviZN0
Lecture #6: Data Collection and Preprocessinghttps://youtu.be/dAg-_gzFo14
Lecture #7: Convolutional Neural Networks Explainedhttps://youtu.be/-I0lry5ceDs
Lecture #8: How to Design a Convolutional Neural Networkhttps://youtu.be/fTw3K8D5xDs
Lecture #9: Transfer Learninghttps://youtu.be/_2EHcpg52uU
Lecture #10: Optimization Tricks: momentum, batch-norm, and morehttps://youtu.be/kK8-jCCR4is
Lecture #11: Recurrent Neural Networkshttps://youtu.be/k97Jrg_4tFA
Lecture #12: Deep Unsupervised Learninghttps://youtu.be/P8_W5Wc4zeg
Lecture #13: Generative Adversarial Networkshttps://youtu.be/7tFBoxex4JE
Lecture #14: Practical Methodology in Deep Learninghttps://youtu.be/9Sl_t_GxX6w
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