Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course

 

 

 

What's the future like?

Based on multi-layer nonlinear neural networks, deep learning can learn directly from raw data, automatically extract and abstract features from layer to layer, and then achieve the goal of regression, classification, or ranking. Deep learning has made breakthroughs in computer vision, speech processing and natural language, and reached or even surpassed human level. The success of deep learning is mainly due to the three factors: big data, big model, and big computing.

In the past few decades, many different architectures of deep neural networks have been proposed, such as

  1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
  2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
  3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.