Part 9/12:
The period around 2014-2015 marked a renaissance for neural networks and deep learning, propelled by breakthroughs like TensorFlow. This era reshaped the industry’s perception of data science—shifting focus from traditional models to complex neural architectures capable of solving previously intractable problems, such as image and speech recognition.
While deep learning solutions demonstrated impressive results—like transaction enrichment—they are computationally intensive and require substantial resources. The industry experienced a hype cycle, with excitement about capabilities that, while powerful, are not a panacea. The fundamental principles of data science—like understanding your data and problem—remain critical.