Sponsored Post Sign up for Doug Turnbull’s exclusive live cohort, starting October 11. Previous Sphere cohorts have had students from Apple, Amazon, Spotify, Microsoft, Twitter, Shopify, Glassdoor, and more. Doug leads the entire Search Relevance practice at Shopify. He has spent the last 10+ years writing industry-leading books such as “Relevant Search” (2016) & […]
A Tour of Attention-Based Architectures
As the popularity of attention in machine learning grows, so does the list of neural architectures that incorporate an attention mechanism. In this tutorial, you will discover the salient neural architectures that have been used in conjunction with attention. After completing this tutorial, you will gain a better understanding of how the attention mechanism is […]
The Attention Mechanism from Scratch
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 of the encoded input vectors, with the most […]
What is Attention?
Attention is becoming increasingly popular in machine learning, but what makes it such an attractive concept? What is the relationship between attention as applied in artificial neural networks, and its biological counterpart? What are the components that one would expect to form an attention-based system in machine learning? In this tutorial, you will discover an […]
A Bird’s Eye View of Research on Attention
Attention is a concept that is scientifically studied across multiple disciplines, including psychology, neuroscience and, more recently, machine learning. While all disciplines may have produced their own definitions for attention, there is one core quality they can all agree on: attention is a mechanism for making both biological and artificial neural systems more flexible. In […]
How to Calculate Precision, Recall, F1, and More for Deep Learning Models
Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset. This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at solving the problem. The Keras deep learning API model is […]
Last call: Stefan Krawcyzk’s ‘Mastering MLOps’ Live Cohort
Sponsored Post This is your last chance to sign up for Stefan Krawczyk’s exclusive live cohort, starting next week (August 22nd). We already have students enrolled from Apple, Amazon, Spotify, Nubank, Workfusion, Glassdoor, ServiceNow, and more. Stefan Krawczky has spent the last 15+ years working on MLOps at companies like Stitch Fix, Nextdoor, and […]
How to Make Predictions with Keras
Once you choose and fit a final deep learning model in Keras, you can use it to make predictions on new data instances. There is some confusion amongst beginners about how exactly to do this. I often see questions such as: How do I make predictions with my model in Keras? In this tutorial, you […]
Why Initialize a Neural Network with Random Weights?
The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand the role of nondeterministic and randomized algorithms as well as […]
When to Use MLP, CNN, and RNN Neural Networks
What neural network is appropriate for your predictive modeling problem? It can be difficult for a beginner to the field of deep learning to know what type of network to use. There are so many types of networks to choose from and new methods being published and discussed every day. To make things worse, most […]