Deep Learning with Python Tap the Power of TensorFlow and Keras, Develop Your First Model, Achieve state-of-the-Art Results Why Are Deep Learning Models So Powerful? …the secret is “Representation Learning“ Deep learning techniques are so powerful because they learn the best way to represent the problem while learning how to solve the problem. This is […]
There are so many deep learning libraries to choose from. Which are the good professional libraries that are worth learning and which are someones side project and should be avoided. It is hard to tell the difference. In this post you will discover the top deep learning libraries that you should consider learning and using […]
Need Help Getting Started with Applied Machine Learning? These are the Step-by-Step Guides that You’ve Been Looking For! What do you want help with? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: Step 1: Adjust Mindset. […]
We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus to the details of the Transformer architecture itself to discover how self-attention can be implemented without relying on the use of recurrence and convolutions. In this tutorial, […]
The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all the encoded input vectors, with the most relevant […]
Adding noise to an underconstrained neural network model with a small training dataset can have a regularizing effect and reduce overfitting. Keras supports the addition of Gaussian noise via a separate layer called the GaussianNoise layer. This layer can be used to add noise to an existing model. In this tutorial, you will discover how […]
Activity regularization provides an approach to encourage a neural network to learn sparse features or internal representations of raw observations. It is common to seek sparse learned representations in autoencoders, called sparse autoencoders, and in encoder-decoder models, although the approach can also be used generally to reduce overfitting and improve a model’s ability to generalize […]
Grid searching is generally not an operation that we can perform with deep learning methods. This is because deep learning methods often require large amounts of data and large models, together resulting in models that take hours, days, or weeks to train. In those cases where the datasets are smaller, such as univariate time series, […]
Configuring neural networks is difficult because there is no good theory on how to do it. You must be systematic and explore different configurations both from a dynamical and an objective results point of a view to try to understand what is going on for a given predictive modeling problem. In this tutorial, you will […]
There are not many books on deep learning at the moment because it is such a young area of study. There are a few books available though and some very interesting books in the pipeline that you can purchase by early access. In this post, you will discover the books available right now on deep […]
