The field of natural language processing is shifting from statistical methods to neural network methods. There are still many challenging problems to solve in natural language. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific language problems. It is not just the performance of deep learning models on benchmark problems that is most […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]
Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time, and the task is to predict a category for the sequence. This problem is difficult because the sequences can vary in length, comprise a very large vocabulary of input symbols, and may require the model to learn […]
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 […]
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. […]
Recommender systems may be the most common type of predictive model that the average person may encounter. They provide the basis for recommendations on services such as Amazon, Spotify, and Youtube. Recommender systems are a huge daunting topic if you’re just getting started. There is a myriad of data preparation techniques, algorithms, and model evaluation […]
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most […]
Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms. It is a key foundation to the field of machine learning, from notations used to describe the operation of algorithms to the implementation of algorithms in code. Although linear algebra is integral to the field of machine learning, the tight relationship […]
Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster. In this post, you will discover the […]
Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation. They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. In this post, you will discover the […]
