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on
1
- START
2
- CLIENT CALL 1
3
- Breakdown Board
4
- MISSION 1
5
- Install and Import Dependencies
6
- Build a Dataloading Function
7
- MISSION 2
8
- Create Tensorflow Dataset
9
- Determine Average Call Length
10
- Build Preprocessing Function
11
- MISSION 3
12
- Create Training and Testing Partitions
13
- Build Deep CNN Model
14
- Classifier Audio Clips
15
- MISSION 4
16
- Build Forest Parsing Function
17
- Predict All Files
18
- MISSION 5
19
- Export Results to CSV
Description:
Learn to build a Deep Audio Classification model using Python and TensorFlow in this comprehensive tutorial. Begin with a client call and project breakdown, then progress through installing dependencies and creating data loading functions. Explore TensorFlow dataset creation, determine average call lengths, and build preprocessing functions. Create training and testing partitions before constructing a Deep CNN model for audio clip classification. Develop a forest parsing function, predict results for all files, and export findings to CSV. Follow along with provided code and data sources, and engage in hands-on missions throughout the tutorial to reinforce learning.

Build a Deep Audio Classifier with Python and Tensorflow

Nicholas Renotte
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