Softmax Regression - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and
Link to this course:
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Softmax Regression - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Deep Learning Specialization
This course will teach you the magic of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
After 3 weeks, you will:
Understand industry best-practices for building deep learning applications.
Be able to effectively use the common neural network tricks, including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
Be able to implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization.
Hyperparameter, Tensorflow, Hyperparameter Optimization, Deep Learning
After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.,Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow Thanks.
Softmax Regression - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
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