Advanced RNN Concepts and Projects

Overview

This advanced course on Recurrent Neural Networks (RNNs) addresses key challenges like the vanishing gradient problem and provides solutions such as Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks. You'll start with an overview of improved RNN modules and delve into bidirectional RNNs and attention models, establishing a strong foundation in advanced RNN concepts. Practical implementation using TensorFlow is emphasized, with projects like text generation and stock price prediction to solidify your learning. This course ensures you gain the skills necessary to tackle real-world AI problems confidently. Through video tutorials, real-world projects, and hands-on exercises, you'll acquire the advanced knowledge and skills needed to excel in AI. By the end, you'll develop and apply advanced RNN models, understand and implement GRUs, LSTMs, and attention mechanisms, utilize TensorFlow for RNN models, and apply these models to projects like text generation and stock price prediction. Designed for data scientists, machine learning engineers, and AI enthusiasts with a solid understanding of basic RNNs and neural networks, the course combines in-depth theoretical lessons with extensive practical applications.

Syllabus

Vanishing Gradients in RNN

In this module, we will address the vanishing gradient problem in Recurrent Neural Networks and explore various solutions. You'll learn about Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks, including their mathematical foundations. Additionally, we will cover bidirectional RNNs and the attention model, providing a comprehensive approach to improving RNN performance.

TensorFlow

In this module, we will introduce you to TensorFlow, a powerful framework for building and training deep learning models. You will learn how to implement TensorFlow in practical applications, focusing on a text classification example using RNNs. Additionally, we'll compare TensorFlow with other popular deep learning frameworks to highlight its strengths and unique features.

Project 1: Book Writer

In this module, we will guide you through your first project: creating a book writer using RNNs. You will learn to map data, prepare the RNN architecture, and train the model using TensorFlow. By the end, you'll be able to generate coherent text and complete an activity to build a word-level text generator.

Project 2: Stock Price Prediction

In this module, we will tackle the stock price prediction project. You will learn to define the problem, create and prepare a dataset, and train an RNN model. Through practical exercises, you will gain experience in evaluating the model's performance and implementing an artificial neural network for stock prediction.

Further Reading and Resources

In this module, we will provide you with further reading and resources to expand your knowledge beyond the course. You'll have access to curated materials that will support your continued learning and mastery of Recurrent Neural Networks and their applications.