Welcome to our comprehensive course on Deep RNN (Recurrent Neural Networks)! Delve into the realm of deep learning with a focus on RNN architecture, significance, and practical implementation. Starting with an introduction to Deep RNNs, we explore their foundational concepts, importance, and operational mechanisms. Our journey continues with an in-depth analysis of architecture, weight initialization techniques, and essential hyperparameters crucial for optimizing RNN performance. You'll gain insights into various activation functions, loss functions, and training methodologies like Gradient Descent and Adam. Practical sessions cover data explanation, numerical examples, and implementation in both MATLAB and Python, ensuring a holistic understanding of Deep RNNs for real-world applications.
Why RNN is important?
How do RNN works?
Architecture based on the input size
Xavier/Glorot Initialization
He Initialization
Scaled Random Initialization
Learning rate
Sigmoid
tanh
ReLU
MSE
RMSE
Entropy
MAE
Huber Loss
Gradient descent
Adam
SGDM
Data explanation
Python
Dr. Amol Dhumane serves as an Associate Professor at the Symbiosis Institute of Technology, located in Lavale, Pune. With an impressive academic career spanning over two decades, he has accumulated a wealth of experience and knowledge in his field. His educational journey began with a Ph.D. in computer engineering from Savitribai Phule Pune University, Pune, where he laid the foundation for his future research and academic endeavors. Throughout his career, Dr. Amol Dhumane has been actively involved in research, contributing significantly to the academic community. He has authored a total of 48 research papers, which have collectively garnered 510 citations, reflecting the impact and relevance of his work. In addition to his research papers, Dr. Amol Dhumane has also made notable contributions to innovation and technology. He holds three patents, showcasing his ability to translate theoretical knowledge into practical applications. Furthermore, he has been awarded a design patent, highlighting his creativity and ingenuity in developing new designs and solutions. Dr. Amol Dhumane's commitment to advancing knowledge and fostering innovation is evident in his extensive body of work. As an Associate Professor, he continues to mentor and guide the next generation of engineers and researchers, sharing his expertise and passion for the field. His dedication to education and research has not only enriched his career but has also had a lasting impact on the academic community and the field of computer engineering.
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This course includes:
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Validity:60 days
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Validity:60 days
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Category:Deep Learning
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Lessons:12
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Students:1908
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Language:English
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Certificates:Yes
Payment details
Deep Recurrent Neural Network
Deep dive into theory, numerical examples and case studies.
