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First steps with neural networks and NumPy ยท Martin Pitt
| | algobeans.com
Artificial Neural Networks Introduction (Part II) - Algobeans
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| | In the 2nd part of our tutorial on artificial neural networks, we cover 3 techniques to improve prediction accuracy: distortion, mini-batch gradient descent and dropout.
| | www.analyticsvidhya.com
45 Questions to test a data scientist on Deep Learning
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| | Explore questions on Deep Learning which every data scientist should know. Answer these questions on neural networks and deep learning.
| | programmathically.com
Understanding The Exploding and Vanishing Gradients Problem - Programmathically
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| | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights []
| | adl1995.github.io
An overview of activation functions used in neural networks
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| [AI summary] The article explains various activation functions used in neural networks, their properties, and applications, including binary step, tanh, ReLU, and softmax functions.