Why Deep Learning Took Off?!!!

In this episode, we unpack why deep learning suddenly succeeded after decades of limited progress. Although neural networks were invented in the 1940s and refined through the perceptron era, connectionism stalled due to shallow architectures, linear separability limits, scarce data, and insufficient compute. The modern breakthrough emerged only when three factors finally converged: better algorithms, abundant data, and powerful GPU-based computation.We trace this journey from the early perceptron failures and the rise of SVMs, to the shift toward representation learning—where deep networks learn hierarchical features directly from raw data. With stable training made possible by backpropagation refinements, ReLU activations, improved initialization, and layerwise pretraining, deep models became practical just as massive datasets like ImageNet and GPU acceleration became available.The episode then highlights the architectures that solidified deep learning’s dominance—CNNs for vision, ResNets for extreme depth, LSTMs for sequence modeling, and transformers for global context and large-scale language models—and discusses key techniques such as dropout, batch normalization, transfer learning, and the persistent challenge of adversarial fragility.This episode covers:• Why early neural networks failed to scale • The convergence of algorithms, data, and computation • Representation learning and the necessity of depth • How ReLU, initialization, and backprop improvements enabled deep training • Impact of ImageNet, GPUs, and large-scale compute • CNNs, ResNets, LSTMs, and transformers as architectural milestones • Dropout, batch normalization, and transfer learning • Ongoing issues with robustness and adversarial examplesThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingAll referenced materials, recommended readings, and extended resources are available at:https://adapticx.co.uk