Introduction to RNN for Time Series Forecast with TensorFlow | CodeSignal Learn

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Introduction to RNN for Time Series Forecast with TensorFlow

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4 courses

62 practices

4 hours

Learn to preprocess, model, and forecast time series data using RNNs, LSTMs, and GRUs in TensorFlow. Build, evaluate, and optimize models for univariate and multivariate forecasting and classification tasks.

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125 learners

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Verified skills you'll gain

INTERMEDIATE

Coding and Data Algorithms

DEVELOPING

Data Cleaning and Preprocessing

INTERMEDIATE

Machine Learning and Predictive Modeling

Tools you'll use

Python

TensorFlow

Course 1

Introduction to RNNs for Time Series Analysis

4 lessons

15 practices

Learn how to build and evaluate RNN, LSTM, and GRU models for time series forecasting. This hands-on course covers univariate and multivariate data, classification, and advanced deep learning techniques for improved accuracy.

Course 2

Handling Multivariate Time Series with RNNs

4 lessons

15 practices

This course extends the concepts from the first course by introducing multiple time series inputs. It covers how to preprocess, structure, and train RNN models using **two related time series features** from the **Air Quality dataset**. It also includes model evaluation techniques to assess forecasting accuracy.

Course 3

Time Series Forecasting with LSTMs

4 lessons

16 practices

This course focuses on LSTM networks, a powerful extension of RNNs that handle long-term dependencies better than simple RNNs. Learners will build, optimize, and evaluate LSTM models for time series forecasting.

Course 4

Time Series Forecasting with GRUs

4 lessons

16 practices

This course explores Gated Recurrent Units (GRUs) in TensorFlow for multivariate time series forecasting. We will build, evaluate, and apply advanced GRU techniques like Bidirectional GRUs, Attention Mechanisms, and Hybrid GRU-CNN models to improve forecasting accuracy.

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