Normalization Before we learn about Batch normalization lets see what is Normalization. It is technique of transforming the data to

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## Adaptive Learning Rate in Deep Learning

Adaptive learning rate algorithms monitor the performance of the training and based on monitoring they adjust learning rate. They provide

Continue reading## Regularization in Deep Learning

What is Regularization Regularization is a technique that helps reduce overfitting or reduce variance in Neural network by penalizing for complexity.

Continue reading## Bias and Variance in Machine Learning

Bias Bias is error of Machine learning model. It is inability for a Machine learning algorithm to capture true relationship.

Continue reading## Difference Between Loss and Cost Function in Deep Learning

Loss function The Loss function computes the error for a single training example. Cost function The Cost function is the

Continue reading## Why Deep Learning is Better then Other Machine Learning Algorithms

Deep Learning is better then other Traditional Machine learning algorithms because it gives better precision when it use large amount

Continue reading## Gradient Descent and its Variants in Deep Learning

1. Gradient Descent Gradient descent is an optimization algorithm used for finding weights (parameters) in Deep learning that will produce

Continue reading## Tensors in Deep Learning

1. What is Tensor Tensors are primary data structures used by Artificial Neural Networks. All inputs and outputs are represented

Continue reading## How GPU can Powerfully Speed Up Training in Deep Learning

1. What is GPU GPU (Graphics Processing Unit) is chip designed to handle Graphics in computing environments. GPU is designed

Continue reading## Learning Rate Schedules in Deep Learning

Learning rate schedules are techniques used to adjust Learning rate during training by predefined schedule. See what is Learning rate

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