Course · Intermediate
Optimizing Network Parameters
In this course, you'll deepen your understanding of backpropagation while building a basic deep learning framework. You'll optimize network parameters and apply regularization to enhance model performance. This course is the third step in a series of courses that will take you on a journey from beginner to advanced deep learning practitioner.
- Intermediate friendly
- 0 hrs
- 2 lessons
- Free
Course overview
Optimize deep learning models by tuning network parameters, applying backpropagation, and using regularization to improve performance.
What's inside
2 lessons
- 01 120 minBackpropagation
<p><span style="color: rgb(31,35,40);background-color: rgb(255,255,255);font-size: 16px;font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji;">So far, we've taken a loose look at backpropagation to let us focus on understanding neural network architecture. In this lesson, we'll build a miniature version of PyTorch, and use it to understand backpropagation better.</span> </p>
- 02 120 minOptimizers
<p>Optimizers adjust neural network parameters to try to get loss to (hopefully) a global minimum value. In this lesson, we'll learn more about optimizers. We'll first go into more depth on gradient descent and discuss batch size, learning rate schedules, weight decay, and momentum. We'll then discuss the Adam optimizer, which is a popular optimizer that extends the idea of momentum.</p>
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