1.3 Repeatability results | End to End Machine Learning

Autoplay
Autocomplete

Previous Lesson Complete and Continue

314. Neural Network Optimization

Let's Get Started

In Our Last Episode...
Cottonwood Code Map

1. Single Hyperparameter Optimization

1.1 What is hyperparameter optimization? (3:50)
1.2 Code for testing repeatability (4:24)
1.3 Repeatability results (5:33)
1.4 Optimize a single hyperparameter (14:05)

2. Multi-Hyperparameter Optimization

2.1 Optimize two hyperparameters at once (12:09)
2.2 Refactor to separate out the optimization code (5:46)
2.3 Refactor to make optimization code object oriented (3:54)
2.4 Random search (6:54)

3. Evolutionary Powell's Method: An Experimental Optimizer

3.1 How Evolutionary Powell's method works
3.2 Evolutionary Powell's method in code (14:45)
3.3 Evolutionary Powell's' method results (9:26)

4. Code Optimization

4.1 Profiling (3:27)
4.2 Elimination and modification (5:35)
4.3 Cacheing and acceleration (5:27)
4.4 Pre-loading (4:42)
4.5 Parallelization (8:54)

5. Optimize the compressor

5.1 Hyperparameter optimization criterion: Compressed file size (6:31)
5.2 Neural architecture search setup (3:55)
5.3 Neural architecture search results (7:40)
5.4 Learning rate and initializer optimization (8:53)

6. Create an image compressor tool

6.1 API design for complete beginners (4:54)
6.2 API design for the image compressor (3:55)
6.3 API functions and arguments for the image compressor (9:18)
6.4 Image compressor tool code walkthrough, Part I (10:12)
6.5 Image compressor tool code walkthrough, Part II (5:44)
6.6 Test run the compressor and decompressor (4:50)
6.7 Inspect the results (5:16)
6.8 Improve the image quality (5:41)
6.9 Publish the Martian images case study (9:25)

1.3 Repeatability results

Complete and Continue

Discussion

0 comments

Lesson Content Locked

Your session expired. Please sign in again to continue.

Login