Distributed Deep Learning Pipelines with PySpark and Keras
Deep learning has achieved great success in many areas recently. It has attained state-of-the-art performance in applications ranging from image classification and speech recognition to time series forecasting. The key success factors of deep learning are – big volumes of data, flexible models and ever-growing computing power. With the increase in the number of parameters and training data, it is observed that the performance of deep learning can be improved dramatically.
