Data Science for Everyone 14-2 Optimization 

#deeplearning #datascience #optimization #lossfunction #gradientdescent #optimization
This section covers how to optimize parameters (i.e. weights and biases) of neural network models. First, let's understand various loss functions, and explore optimization methods: gradient descent and mini-batch learning.
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This course is targeted for managers who are not data scientist but need to manage data analytic projects. This is also targeted for managers who want to introduce data-driven management. So, the knowledge provided in this course is both theoretical and pragmatic, but not includes details of mathematics and coding. However, anyone who are beginners in data science are also welcome because this course can provide you with essentials for learning technical aspects of data science.
Takuma Kimura, Ph.D.
Scientist of Organizational Behavior and Business Analytics
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