Deep Learning - Recurrent Neural Networks with TensorFlow
Learn how to use TensorFlow 2 to build recurrent neural networks (RNNs)
Created by Lazy Programmer
Explore how to build powerful recurrent neural networks with TensorFlow 2 and apply them to real-world sequence data. You'll learn about RNN architectures like Elman units, GRUs, and LSTMs, and discover practical techniques for time series forecasting, text classification, and stock prediction.
Packt | Feb 2023 | 246 min
What You Will Learn
You'll combine clear explanations of RNN concepts with hands-on coding exercises in TensorFlow 2. By working through practical projects, you'll see how to preprocess data, design models, and evaluate results. Each step helps you build confidence in applying RNNs to tasks like forecasting and NLP.
Key Features
- Build and train RNNs, GRUs, and LSTMs using TensorFlow 2 for sequence data tasks
- Apply RNNs to time series forecasting, stock prediction, and natural language processing
- Understand best practices and common pitfalls when working with sequential models
Target Audience
Ideal for data scientists, machine learning engineers, or developers with solid Python skills and experience using TensorFlow for feedforward networks. If you want to deepen your understanding of sequential models and apply them to time series or text data, you'll find practical guidance and real examples here.
