The success of a machine learning (ML) project can often come down to the framework it uses. Different systems fit different situations and users, so selecting the proper library is an important step in delivering the desired results. In that spirit, here’s a comparison of two of the most popular... Read more

Keras is a Python library for deep learning. Deep learning is a sub-branch of artificial intelligence that focuses on solving complex computations by emulating the working process of a human brain. Neural networks, computational graphs composed of nodes representing multiple operators for breaking down the tasks into smaller parts for... Read more

Google’s TensorFlow engine has a unique way of solving problems, allowing us to solve machine learning problems very efficiently. Nowadays, machine learning is used in almost all areas of life and work, with famous applications in computer vision, speech recognition, language translations, healthcare, and many more.  This article is an... Read more

I started working with Deep Learning (DL) in the 2016 – 2017 time frame when the framework ecosystem was much more diverse and fragmented than it is today. Theano was the gold standard at the time, Tensorflow had just been released, and DeepLearning4j was still being built. Both Theano and... Read more

Keras metrics are functions that are used to evaluate the performance of your deep learning model. Choosing a good metric for your problem is usually a difficult task. you need to understand which metrics are already available in Keras and tf.keras and how to use them, in many situations you need to define... Read more

Keras and Tensorflow are two very powerful packages that are normally accessed via python. Since the packages were developed for python they may have the illusion of being out of reach for R users. However, this is not the case as the Keras and Tensorflow packages may be set up... Read more

The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is taking the lead position... Read more

In this article, we’ll walk through building a custom convolutional neural network (CNN) to classify images without relying on pre-trained models. There are a number of popular pre-trained models (e.g. Inception, VGG16, ResNet50) out there that are helpful for overcoming sampling deficiencies; they have already been trained on many images... Read more

For many new data scientists transitioning into AI and deep learning, the Keras framework is an efficient tool. Keras is a powerful and easy-to-use Python library for developing and evaluating deep learning models. In this article, we’ll lay out the welcome mat to the framework. You should walk away with a... Read more

ODSC West 2023 is just a couple of months away, and we couldn’t be more excited to be able to share our Preliminary Schedule with you! You can find the schedule here on our website, but be sure to read on for a breakdown of what you can expect from... Read more