Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh

Overview of this book

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Table of Contents (14 chapters)

Feature engineering and representation of audio events

To build a robust classification model, we need robust and good feature representations from our raw audio data. We will leverage some of the techniques learned in the previous section for feature engineering. The code snippets used in this section are also available in the Feature Engineering.ipynb Jupyter Notebook, in case you want to run the examples yourself. We will reuse all the libraries we previously imported and we will also leverage joblib here to save our features to disk:

from sklearn.externals import joblib 

Next, we will load up all our file names and define some utility functions to read in audio data and also enable us to get window indices for audio sub-samples, which we will be leveraging shortly:

# get all file names 
ROOT_DIR = 'UrbanSound8K/audio/' 
files = glob.glob(ROOT_DIR+'/**/*&apos...