Transfer learning is a deep learning technique useful in training a model with very less data. We will train an image classification model on top of resnet34 architecture using the data that contains digitally recorded heartbeats of human beings in the form of audio (.wav) files. In the process, we will convert each of these audio files into an image by converting them to spectrograms using a popular python audio library called Librosa. In the end, we will examine the model with popular error metrics and check its performance.

Refer the blog here : https://towardsdatascience.com/convolutional-neural-networks-cnns-a-practical-perspective-c7b3b2091aa8