Improving Deep Neural Networks Hyperparameter Tuning/ Regularization and Optimization
About Course
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization – Free Coursera Course
This course is part of the Deep Learning Specialization on Coursera and is completely free to take. In this course, you will delve deeper into deep learning and learn how to systematically improve the performance of your neural networks. You will discover best practices for training and testing, analyze bias/variance, and master techniques like:
* **Initialization**
* **L2 and Dropout Regularization**
* **Hyperparameter Tuning**
* **Batch Normalization**
* **Gradient Checking**
* **Optimization Algorithms:** Mini-batch Gradient Descent, Momentum, RMSprop, Adam
* **TensorFlow Implementation**
This course will equip you with the essential skills to build robust deep learning applications, and is a great stepping stone towards a career in AI. This course is offered by Coursera, but you can access it for free through Theetay.
Course Content
01_practical-aspects-of-deep-learning
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A Message from the Professor
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01_train-dev-test-sets.mp4
12:04 -
02_bias-variance.mp4
08:46 -
03_basic-recipe-for-machine-learning.mp4
06:21 -
04_regularization.mp4
09:42 -
05_why-regularization-reduces-overfitting.mp4
07:09 -
06_dropout-regularization.mp4
09:25 -
07_understanding-dropout.mp4
07:04 -
08_other-regularization-methods.mp4
08:23 -
09_normalizing-inputs.mp4
05:30 -
10_vanishing-exploding-gradients.mp4
06:07 -
11_weight-initialization-for-deep-networks.mp4
06:11 -
12_numerical-approximation-of-gradients.mp4
06:35 -
13_gradient-checking.mp4
06:34 -
14_gradient-checking-implementation-notes.mp4
05:18 -
15_yoshua-bengio-interview.mp4
25:48
02_optimization-algorithms
03_hyperparameter-tuning-batch-normalization-and-programming-frameworks
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