Book Image

Hands-On Transfer Learning with Python

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

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, 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)

Building a deep learning audio event identifier

We will now look at a strategy using which we can build an actual audio event identifier by leveraging the classification model we built in the previous section. This will enable us to take any new audio file and predict which category it might belong to by making use of the entire workflow we defined in this chapter, starting from building the base feature maps, extracting features using the VGG-16 model, and then leveraging our classification model to make a prediction. The code snippets used in this section are also available in the Prediction Pipeline.ipynb Jupyter Notebook in case you want to run the examples yourself. The Notebook contains the AudioIdentifier class, which we have created by reusing all the components we have built in the previous sections of this chapter. Do refer to the Notebook to access the full code for...