Skip to content

Commit ab292a5

Browse files
authored
Added ch15/readme (#95)
* Added ch15/readme * Fixed the notebook links
1 parent e8a52dc commit ab292a5

File tree

1 file changed

+60
-0
lines changed

1 file changed

+60
-0
lines changed

ch15/README.md

Lines changed: 60 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,60 @@
1+
Python Machine Learning - Code Examples
2+
3+
4+
## Chapter 15: Classifying Images with Deep Convolutional Neural Networks
5+
6+
7+
### Chapter Outline
8+
9+
- The building blocks of CNNs
10+
- Understanding CNNs and feature hierarchies
11+
- Performing discrete convolutions
12+
- Discrete convolutions in one dimension
13+
- Padding inputs to control the size of the output feature maps
14+
- Determining the size of the convolution output
15+
- Performing a discrete convolution in 2D
16+
- Subsampling layers
17+
- Putting everything together – implementing a CNN
18+
- Working with multiple input or color channels
19+
- Regularizing an NN with dropout
20+
- Loss functions for classification
21+
- Implementing a deep CNN using TensorFlow
22+
- The multilayer CNN architecture
23+
- Loading and preprocessing the data
24+
- Implementing a CNN using the TensorFlow Keras API
25+
- Configuring CNN layers in Keras
26+
- Constructing a CNN in Keras
27+
- Gender classification from face images using a CNN
28+
- Loading the CelebA dataset
29+
- Image transformation and data augmentation
30+
- Training a CNN gender classifier
31+
- Summary
32+
33+
### A note on using the code examples
34+
35+
The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
36+
37+
![](../ch02/images/jupyter-example-1.png)
38+
39+
40+
41+
Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
42+
43+
conda install jupyter notebook
44+
45+
Then you can launch jupyter notebook by executing
46+
47+
jupyter notebook
48+
49+
A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
50+
51+
**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
52+
53+
**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch15_part1.ipynb`](ch15_part1.ipynb) and [`ch15_part2.ipynb`](ch15_part2.ipynb))**
54+
55+
In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
56+
57+
![](../ch02/images/jupyter-example-2.png)
58+
59+
60+
When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.

0 commit comments

Comments
 (0)