In today's summary, we explore the applications of Recurrent Neural Networks in Machine Learning, AI, and Deep Learning. We discuss the Backpropagation Through Time (BPTT) algorithm, text language conversion, and more. Check out the highlighted videos for more insights.

The Power of Recurrent Neural Networks: 🔑Embedding Layers, Training Algorithms & More

Today, we look at Recurrent Neural Networks and their applications in Machine Learning, Artificial Intelligence, and Deep Learning. We have highlighted two videos uploaded in the past 24 hours that discuss the Backpropagation Through Time (BPTT) Algorithm and the Predicting Next Word in a Sequence. In these videos, they explore how to train a Recurrent Neural Network (RNN) and the design criteria for a RNN model to process sequential data. They also discuss the concept of text language conversion to numerical encoding, such as embedding, one-hot-encoding, and Neural network learned embedding.

Key Takeaways:
• Backpropagation Through Time (BPTT) Algorithm for training a RNN model
• Design criteria for a RNN model to process sequential data
• Text language conversion to numerical encoding
• Embedding, one-hot- encoding and Neural network learned embedding
• Exploding gradient and vanishing gradient

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Daily Recurrent Neural Networks Summary: Today, we have explored the applications of Recurrent Neural Networks (RNN) in Machine Learning, Artificial Intelligence, and Deep Learning. We have looked at the Backpropagation Through Time (BPTT) Algorithm and the Predicting Next Word in a Sequence. We have discussed the importance of text language conversion to numerical encoding, such as embedding, one-hot-encoding, and Neural network learned embedding. We invite you to scroll down and view the highlighted videos to learn more.

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Recurrent Neural Network: Part-5 | Backpropagation Through Time (BPTT) Algorithm | Training of RNN

Wed Jul 5 2023 19:15:02 UTC


#backpropagation #BPTT #rnn #trainigrnn #recurrentneuralnetwork #deeplearningtutorial #machinelearning #machinelearningpython #deeplearning #deeplearn #recurrent #rnn #recurrentneural #sequencemodel #neuralnetworks #binaryclassification #artificialintelligence #machinelearning #deeplearningtutorial #deeplearning #lossfunction #Rnnloss #rnnimplemntation #sequences #embedding #encoding #sequencemodel

In this video (Part-5) we will discuss the Backpropagation Through Time (BPTT) algorithm to train the RNN model. How the loss propagated the RNN model will also be highlighted and will compare with traditional Neural Network.

A most important concept of Gradient computing and gradient flow in RNN will also be discussed. Two relevant concepts to gradient, (Exploding gradient and vanishing gradient) will also be explained in simple words.

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Recurrent Neural Network: Part-4 | Predicting Next Word in a Sequence | Sequence Modeling Problem

Wed Jul 5 2023 18:46:46 UTC


#recurrent #rnn #recurrentneural #sequencemodel #neuralnetworks #binaryclassification #artificialintelligence #machinelearning #deeplearningtutorial #deeplearning #lossfunction #Rnnloss #rnnimplemntation #sequences #embedding #encoding #sequencemodel

Predicting Next Word in a Sequence of Words using Recurrent Neural Network, Sequence Modeling Problem

In this video (Part-4) we will discuss some of the important application of sequential processing of data using recurrence neural networks. We will also discuss the design criteria for RNN model to process efficiently the sequential data.

A most important concept of text language conversion to numerical encoding will also be discussed. Embedding, one-hot- encoding and Neural network learned embedding will also be discussed.

A Learning Platform to Improve your Hands on skills in C++ Programming Language Object Oriented Programming Implementation of Data Structure and Algorithms in C++ Python Programming: from Beginners to Expert Artificial Intelligence Practical Data Science Machine Learning Deep Learning
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Unit 8.3 | Introduction to Recurrent Neural Networks | Part 4 | Embedding Layers in PyTorch

Wed Jul 5 2023 17:41:52 UTC


To access additional resources, quizzes and exercises, please visit https://lightning.ai/pages/courses/deep-learning-fundamentals/
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Unit 8.3 | Introduction to Recurrent Neural Networks | Part 3 | Encoding Inputs w/ Embedding Layers

Wed Jul 5 2023 17:41:55 UTC


To access additional resources, quizzes and exercises, please visit https://lightning.ai/pages/courses/deep-learning-fundamentals/
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Unit 8.3 | Introduction to Recurrent Neural Networks | Part 1 | Modeling Sequence Data

Wed Jul 5 2023 17:42:07 UTC


To access additional resources, quizzes and exercises, please visit https://lightning.ai/pages/courses/deep-learning-fundamentals/
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Unit 8.3 | Introduction to Recurrent Neural Networks | Part 2 | Different Sequence Modeling Tasks

Wed Jul 5 2023 17:41:59 UTC


To access additional resources, quizzes and exercises, please visit https://lightning.ai/pages/courses/deep-learning-fundamentals/
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