Introduction to deep learning
DL 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, clever weight 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 why and when DL models make sense for certain domains and datasets. If you are already an expert in DL, feel free to skip this section and go directly to the more practical sections. However, if you are new to DL, I strongly encourage you to stay for this section in order to understand the practical and business need for larger, more capable models, as...