Sequence Models
Work with RNNs, LSTMs, and the attention mechanism.
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After this skill you can…
- Implement an LSTM text generator
- Explain vanishing gradients in RNNs
- Describe the attention mechanism intuitively
Prerequisites
Watch (10 videos)
Stock Price Prediction using GRU | Deep Learning Project in Tamil | Gated Recurrent Unit
→ Build a stock price prediction model with GRU→ Implement time series forecasting with deep learning
Cryptocurrency-predicting RNN intro - Deep Learning w/ Python, TensorFlow and Keras p.8
→ Build a cryptocurrency-predicting RNN→ Train a deep learning model with Keras
Pytorch Seq2Seq Tutorial for Machine Translation
→ Build a Seq2Seq model→ Train a machine translation model
What is Recurrent Neural Network (RNN)? Deep Learning Tutorial 33 (Tensorflow, Keras & Python)
→ Build RNNs for sequence modeling→ Implement RNNs in Tensorflow and Keras
Normalizing and creating sequences Crypto RNN - Deep Learning w/ Python, TensorFlow and Keras p.9
→ Build a crypto RNN using TensorFlow and Keras→ Normalize and create sequences for RNN input
Balancing RNN sequence data - Deep Learning w/ Python, TensorFlow and Keras p.10
→ Implement a recurrent neural network→ Balance sequence data
Building Deep Learning Models for Sentiment Analysis | DataHour by Prashant Sahu
→ Build deep learning models for sentiment analysis→ Apply Word2Vec and GloVe for document vectorization
TensorFlow Tutorial 6 - RNNs, GRUs, LSTMs and Bidirectionality
→ Implement RNNs in TensorFlow→ Use GRUs and LSTMs for sequence modeling
Day 10- LSTM Practical Implementation In NLP Application|Krish Naik
→ Implement LSTM in NLP→ Use TensorFlow for sequence modeling
Day 9-Word Embedding Layer And LSTM Practical Implementation In NLP Application|Krish Naik
→ Implement word embedding layer→ Use LSTM in NLP applications
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