advanced supervised deep learning models dense layer embedding layer lstm-based classification model lstm cell architecture data flow model performance metricslstm most_common(countfunction norm_train_reviews and norm_test_reviews pad_index parametersembedding layer rnn and lstm unitsstructure text sentiment class labels tokenized_train corpus word embeddings generation afinn lexicon - algorithmic trading annotations anomaly detection applied computer sciencesee practical computer science area under curve (auc) array elements advanced indexing basic indexing and slicing - boolean indexing integer array indexing linear algebra - operations - artificial intelligence (aidefined major facets nlp objectives text analytics artificial neural networks (anns) - artistic style transfercnns in action - background - custom optimizer - loss functions content loss overall loss function - style loss total variation loss preprocessing - association rule-mining method autoencoders auto feature generation auto regressive integrated moving average (arimamodel - axis controls adjust axis log scale tick range - -axis backpropagation algorithm backward elimination bagging methods bag of -grams model bag of words model numeric vector visual - bar plots - batch learning methods bayes theorem bias and variance generalization error overfitting tradeoff - underfitting bike sharing dataset eda correlations - distribution and trends (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python