Introduction to Deep Learning
Deep learning has revolutionized the ML domain recently and is constantly outperforming classical statistical approaches, and even humans, in various tasks such as image classification, object detection, segmentation, speech transcription, text translation, text understanding, sales forecasting, and much more. In contrast to classical models, DL models use many millions of parameters, parameter sharing, optimization techniques, and implicit feature extraction to outperform all previously hand-crafted feature detectors and ML models when trained with enough data.
In this section, we will help you understand the basics of neural networks and the path to training deeper models with more parameters, better generalization, and hence better performance. This will help you understand how DL-based approaches work, as well as why and when they make sense for certain domains and datasets. If you are already an expert in DL, feel free to skip this section and...