Sequence-to-sequence

CellStrat > Research/Blog > Sequence-to-sequence
19
May
Transformers for NLP

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Last Saturday, our AI Lab Researcher Indrajit Singh presented an exhaustive webinar on Transformers, which are used in NLP. Introduction To combine the advantages from both CNNs and RNNs, [Vaswani et al., 2017] designed a novel architecture using the attention mechanism. This architecture, which is called as Transformer, achieves parallelization by […]

Posted in: Natural Language Processing,
Tags: AddNorm, deep learning, encoder-decoder, Multi-head attention, MXNet, natural language processing, NLP, position-wise FFN, positional encoding, Residual block, self-attention, seq2seq, Sequence-to-sequence, skip connection, tensorflow, transformer, transformerdecoder, transformerencoder,
07
May
Sequence to Sequence Learning and Attention in Neural Networks

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Last Saturday 2 May 2020, our AI Lab Researcher Indrajit Singh presented a fabulous session on “Sequence to Sequence Learning and Attention in Neural Networks“. Sequence to Sequence Model :- Sequence-to-sequence learning (Seq2Seq) is about training models to convert sequences from one domain (e.g. sentences in English) to sequences in another […]

Posted in: Natural Language Processing,
Tags: Attention is All You Need, attention model, Attentional Interfaces, encoder-decoder, Long Short-term memory models, LSTM, machine translation, Multi-head attention, NLP, NLP natural language processing, Recurrent Neural Network, RNN, S2S Model, Scaled Dot-Product Attention, Sequence-to-sequence,
24
Oct
Meeting Minutes from AI Lab Hands-On Workshop on Saturday 19th Oct in Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our AI Lab Team Leads Abdul Azeez and Indrajit Singh conducted a superb workshop on developing Question-Answer systems (QAS) with Natural Language Processing or NLP. The Question and Answering systems are going to change the world. Understanding human language interactively, accurately and faster is the need of the century. There […]

Posted in: Deep Learning, Natural Language Processing,
Tags: AI lab, artificial intelligence, deep learning, Disrupt4.0, disruption, EM score, encoder-decoder, exact-match, GLoVe, GLoVe embeddings, Long Short-term memory models, LSTMs, natural language processing, NLP, QAS, Question-Answer Model, RNNs, Sequence-to-sequence, SQuaD, Stanford Question Answering Dataset, Stanford SQuaD,