Abstract page for arXiv paper 2101.04354: Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks

Time Series with Deep Learning Quick Bite

Learning phase has high cost whereas forecasting phase produces results very quickly. Larger epoch produces the better results bur increment will cause to be taken longer time. That's why, picking up very large epoch value would not be applicable for online transaction if learning is implemented instantly.

Writeup for my first major machine learning project.

Python, neural network, MNIST

Recurrent Neural Network(RNN) is a type of Neural Network where the output from previous step are fed as input to the current step

# Recurrent Neural Network (RNN) Recurrent Neural Networks (RNNs) are a type of artificial neural network that has a chain-like structure especially well-suited to operate on sequences and lists. RNNs are applied to a wide variety of problems where text, audio, video, and time series data is present. This may include speech recognition, detection of stock trading patterns, analysis of DNA sequences, language modeling, translation, image captioning, and more. ### How RNNs Differ from Vanilla Feed-forward N

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This paper investigates how structured interregional connectivity and chaotic local dynamics interact to selectively route neural signals, balancing complexity and stability

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