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Gated Recurrent Unit GRU Explained in detail

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Lessons List | 11 Lesson

What is Recurrent Neural Network in Deep Learning RNN

What is Recurrent Neural Network in Deep Learning RNN

Types of Recurrent Neural Networks RNNs

Types of Recurrent Neural Networks RNNs

RNN Model Details Recurrent Neural Networks Deep Learning

RNN Model Details Recurrent Neural Networks Deep Learning

LSTM Recurrent Neural Network RNN Explained in Detail

LSTM Recurrent Neural Network RNN Explained in Detail

Backpropagation in RNN Backpropagation through time

Backpropagation in RNN Backpropagation through time

Word Embedding Python RNN Detailed Explanation

Word Embedding Python RNN Detailed Explanation

Part 1 Training Word Embeddings Word2Vec

Part 1 Training Word Embeddings Word2Vec

Part 2 Python Training Word Embeddings Word2Vec

Part 2 Python Training Word Embeddings Word2Vec

Emoji Prediction LSTM in Tensorflow Implementation

Emoji Prediction LSTM in Tensorflow Implementation

Gated Recurrent Unit GRU Explained in detail

Gated Recurrent Unit GRU Explained in detail

Bidirectional RNN Deep Learning In depth Explanation Equations

Bidirectional RNN Deep Learning In depth Explanation Equations

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Course Description

Recurrent neural networks in this course we will learn about Recurrent Neural Networks (RNNs), a powerful class of neural networks designed to handle sequential data. We'll explore how RNNs process input where the current step depends on previous ones, making them ideal for tasks like language modeling, time series forecasting, and speech recognition. The course covers core RNN architectures including Vanilla RNNs, LSTMs, and GRUs, highlighting their strengths and limitations. You'll also learn how to implement RNNs in Python using frameworks like TensorFlow or PyTorch, how to prepare and feed sequential data, and how to evaluate performance. We will dive into real-world applications such as sentiment analysis and machine translation. You will gain hands-on experience through coding exercises and projects, enabling you to build, train, and optimize your own RNN models. By the end of this course, you will have a deep understanding of how RNNs work and how to apply them effectively in deep learning tasks. Learn With Jay

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