Before Transformers: How CNNs & RNNs Process Text (PyTorch)

Wondering how AI processed natural language before Transformers took over? In this video, we dive into the foundational architectures of sequence modeling: TextCNNs and RNNs.

We’ll break down how 1D Convolutional Neural Networks detect n-gram patterns in parallel, and how Recurrent Neural Networks maintain memory to capture word order. From tackling the infamous vanishing gradient problem with LSTMs and GRUs to writing the actual code in PyTorch, this is your complete guide to the "pre-transformer" era.

Plus, we'll explain the sequential bottlenecks that led to the rise of Attention, and why these classic models are still crucial today for real-time streaming and constrained hardware (Edge AI).

💡 Key Takeaways:

TextCNNs use parallel 1D filters to quickly detect local patterns (n-grams).

RNNs process text sequentially, carrying a hidden state to remember past inputs.

LSTMs & GRUs solved the vanishing gradient problem, allowing networks to learn much longer dependencies.

While Transformers rule massive datasets, CNNs and RNNs remain highly efficient for low-latency, constrained hardware environments.
#NLP #DeepLearning #PyTorch #MachineLearning #RNN #CNN #LSTM #NeuralNetworks #ArtificialIntelligence #DataScience

Видео Before Transformers: How CNNs & RNNs Process Text (PyTorch) канала Engineering Insider