Thread by @selcukorkmaz

🧵1/9 A deep dive into the history of #Backpropagation: A key technique in training multilayer architectures for neural networks. This powerful method revolutionized the way we train AI systems, leading to major breakthroughs in various domains. 🤖#DataScience #DeepLearning #AI

🧵2/9 #Backpropagation is based on a simple concept: use gradient descent to optimize multilayer networks. By applying the chain rule for derivatives, it computes gradients efficiently, leading to optimized weight configurations in each layer of the network. #DataScience #AI

🧵3/9 The shift to Rectified Linear Units (ReLU) accelerated learning in deep networks, allowing training without unsupervised pre-training. This non-linear activation function proved more effective than its smoother predecessors like tanh(z) or 1/(1+exp(−z)). #ReLU #DataScience

🧵4/9 Contrary to earlier beliefs that gradient descent would get trapped in poor local minima, recent findings show that local minima are rarely an issue in large networks. The landscape is filled with saddle points, but their objective function values are quite similar.

🧵5/9 Interest in deep feedforward networks was reignited around 2006, thanks to unsupervised learning procedures for creating layers of feature detectors without labeled data. This "pre-training" approach was essential for tasks with limited labeled data. #DataScience

🧵6/9 The first significant application of this pre-training method was in #SpeechRecognition. Fast GPUs enabled researchers to train networks much faster, achieving record-breaking results on standard benchmarks, and eventually being deployed in Android phones. #DeepLearning

🧵7/9 For smaller datasets, unsupervised pre-training helps prevent overfitting, leading to better generalization. However, as deep learning evolved, pre-training was only necessary for small data sets. #DeepLearning #DataScience

🧵8/9 One type of deep feedforward network that's easier to train and generalizes better than fully connected networks is the Convolutional Neural Network (ConvNet). It's gained widespread adoption in the computer vision community. #ConvNet 🖼️ #DeepLearning #DataScience

🧵9/9 In conclusion, #Backpropagation has been a game-changer in deep learning, enabling the development of complex AI models. The future of AI is even more promising as we continue to refine these techniques and push the boundaries of what's possible. 🚀

Paper to read: LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). doi.org/10.1038/nature…