Recurrent Neural Networks

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Kumaran Ponnambalam

1:07:07

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01.01-getting_started_with_rnns.mp4

00:58

01.02-scope_and_prerequisites_for_the_course.mp4

02:04

01.03-setting_up_exercise_files.mp4

02:35

02.01-a_review_of_deep_learning.mp4

02:15

02.02-why_sequence_models.mp4

02:04

02.03-a_recurrent_neural_network.mp4

02:33

02.04-types_of_rnns.mp4

02:11

02.05-applications_of_rnns.mp4

01:35

03.01-training_rnn_models.mp4

01:25

03.02-forward_propagation_with_rnn.mp4

02:16

03.03-computing_rnn_loss.mp4

01:06

03.04-backward_propagation_with_rnn.mp4

01:28

03.05-predictions_with_rnn.mp4

00:51

04.01-a_simple_rnn_example_predicting_stock_prices.mp4

01:16

04.02-data_preprocessing_for_rnn.mp4

01:03

04.03-preparing_time_series_data_with_lookback.mp4

02:25

04.04-creating_an_rnn_model.mp4

01:37

04.05-testing_and_predictions_with_rnn.mp4

01:38

05.01-the_vanishing_gradient_problem.mp4

02:04

05.02-the_gated_recurrent_unit.mp4

02:58

05.03-long_short-term_memory.mp4

01:50

05.04-bidirectional_rnns.mp4

02:57

06.01-forecasting_service_loads_with_lstm.mp4

01:26

06.02-time_series_patterns.mp4

01:56

06.03-preparing_time_series_data_for_lstm.mp4

01:14

06.04-creating_an_lstm_model.mp4

00:41

06.05-testing_the_lstm_model.mp4

01:30

06.06-forecasting_service_loads_predictions.mp4

02:11

07.01-text_based_models_challenges.mp4

01:41

07.02-intro_to_word_embeddings.mp4

03:58

07.03-pretrained_word_embeddings.mp4

01:50

07.04-text_preprocessing_for_rnn.mp4

01:39

07.05-creating_an_embedding_matrix.mp4

01:13

08.01-spam_detection_example_for_embeddings.mp4

01:03

08.02-preparing_spam_data_for_training.mp4

01:29

08.03-building_the_embedding_matrix.mp4

01:40

08.04-creating_a_spam_classification_model.mp4

00:57

08.05-predicting_spam_with_lstm_and_word_embeddings.mp4

00:50

09.01-next_steps.mp4

00:40

Description

Get started with recurrent neural network (RNN) concepts in a simplified way and build simple applications with RNNs and Keras. RNN is a fast-growing domain within the AI world. Popular groundbreaking applications like language translation, speech synthesis, question answering, and text generation use RNNs as their base technology. Studying this technology, however, has several challenges. Most learning resources are math heavy and are difficult to navigate without good math skills. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, Kumaran Ponnambalam provides a simplified path to studying the basics of recurrent neural networks, allowing you to become productive quickly. Kumaran starts with a simplified introduction of RNN before walking through the process of building a model. He then covers the popular building blocks of RNN with GRUs, LSTMs, word embeddings, and transformers.

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Neural Networks

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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