Deep Learning

Deep learning ( DL ) algorithms use multiple layers to extract information from raw input . This improves language modeling , parsing , and question answering . New techniques process text , time series , and sequence data , and innovators start to use recurrent neural networks and 1D converts .

Neural Networks

Recurrent neural networks , convolutional neural networks , and recursive neural networks take the world by storm . Known as RNNs , recurrent neural networks allow coders to use previous outputs as hiddenstate inputs . The benefits ? Model size doesn ’ t increase with the size of input , allowing programs to process input of any length .

Unsupervised Learning Algorithms

Pre-trained language models require little more than unlabelled text , since they can learn from data that hasn ’ t been hand-annotated by humans . Though the results are still less accurate than supervised training , we can now train algorithms with a vast amount of raw Web data .

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