Category : Deep Learning – mlfromscratch.com

Towards really understanding neural networks — One of the most recognized concepts in Deep Learning (subfield of Machine Learning) is neural networks. Something fairly important is that all types of neural networks are different combinations of the same basic principals. When you know the basics of how neural networks work, new architectures are just small […]Read More

During the calculations of the values for activations in each layer, we use an activation function right before deciding what exactly the activation value should be. From the previous activations, weights and biases in each layer, we calculate a value for every activation in the next layer. But before sending that value to the activations […]Read More

Picking the right optimizer with the right parameters, can help you squeeze the last bit of accuracy out of your neural network model. In this article, optimizers are explained from the classical to the newer approaches. This post could be seen as a part three of how neural networks learn; in the previous posts, we […]Read More

TensorFlow is inevitably the package to use for Deep Learning, if you are doing any sort of business. Keras is the standard API in TensorFlow and the easiest way to implement neural networks. Deployment is much easier, compared to PyTorch – so unless you are doing research, TensorFlow is most likely the way to go. […]Read More

This article was first published by IBM Developer at developer.ibm.com, but authored by Casper Hansen. Here is the Direct link. Creating complex neural networks with different architectures in Python should be a standard practice for any Machine Learning Engineer and Data Scientist. But a genuine understanding of how a neural network works is equally as […]Read More

This article was first published by IBM Developer at developer.ibm.com, but authored by Casper Hansen. Here is the Direct link. Disclaimer: I am not a medical, radiology or epidemiology professional. This article was an experiment, from an engineering and data scientist perspective, and should be regarded as such. To apply Deep Learning for COVID-19, we […]Read More