The Recurrent Neural Networks (RNNs) Basics course is designed to introduce learners to one of the most impactful architectures in deep learning—RNNs.
Recurrent Neural Networks (RNNs) Basics
The Recurrent Neural Networks (RNNs) Basics course is designed to introduce learners to one of the most impactful architectures in deep learning—RNNs. Unlike traditional feedforward neural networks, RNNs have the unique ability to process sequential data by retaining memory of previous inputs. This makes them ideal for applications such as time series forecasting, natural language processing (NLP), speech recognition, and more.
This course provides a hands-on, foundational approach to understanding how RNNs function, how to build them, and how they are applied in real-world scenarios. Whether you're diving into AI or aiming to specialize in neural networks, this course will guide you step by step through the theory and practical implementation of RNNs using modern tools and frameworks.
This course is ideal for students, AI enthusiasts, data scientists, and software engineers who want to understand how sequential data is processed using artificial intelligence techniques. It's especially relevant for those interested in building intelligent systems for speech, text, and time-series data. While a basic understanding of machine learning and Python is recommended, the course is crafted to be accessible for motivated beginners eager to explore the recurrent network in neural network models.
Explain the basic structure and function of Recurrent Neural Networks (RNNs).
Understand the role of time steps and memory in RNNs.
Differentiate between types of RNNs such as LSTMs and GRUs.
Train, tune, and validate RNN models for various tasks.
Apply RNNs to real-world applications like text generation and speech recognition.
Use RNN frameworks and libraries like TensorFlow and PyTorch.
Identify limitations of standard RNNs and explore enhancement techniques.
Complete a capstone project to demonstrate practical understanding.
-
Understand what RNNs are, why they matter, and how they differ from traditional neural networks.
-
Explore the core structure of RNNs, including input, hidden, and output layers and the concept of time steps.
-
Dive into advanced RNN types like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) for handling long-range dependencies.
-
Learn how to train RNNs using backpropagation through time (BPTT), prevent vanishing gradients, and evaluate model performance.
-
Apply RNNs to real-life problems in NLP, time-series prediction, music generation, and more.
-
Hands-on guidance using TensorFlow, Keras, and PyTorch to build and test RNN models.
-
Explore the known challenges of RNNs and how techniques like bidirectional RNNs and attention mechanisms address them.
-
Complete a project involving sequential data, and study real-world use cases to solidify your understanding.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
Sylvester John.
Fibre Wire Technicia...It effectively builds a strong foundation for understanding time-dependent machine learning models.
Yes! Each module blends conceptual understanding with hands-on exercises using Python and popular deep learning libraries.
No, only a basic understanding of Python and general machine learning concepts is recommended. We guide you through RNNs step by step.
You'll work on a capstone project involving sequential data—such as time series forecasting or text generation—using real-world datasets.
A recurrent neural network (RNN) is a type of AI model that remembers previous inputs while making predictions—perfect for data that comes in sequences like speech or text.
“Recurrent” refers to the network’s ability to use its own output from a previous time step as input for the next—creating a feedback loop that models time and memory.
A neural network mimics how human brains process data using layers of interconnected nodes (neurons) to recognize patterns and make predictions or classifications.
Key Aspects of Course
Study at your own
No deadlines or time restrictions
