Transformation

Announcing Enthought Academy

Oct 10, 2022

Dear Students and Friends of Enthought,  I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding in 2001, Enthought has worked in the Scientific Python ecosystem, consulting in both the public and private sectors, solving hard science problems. As the creators of the SciPy package, cofounders…

Read More

Extracting Target Labels from Deep Learning Classification Models

Sep 5, 2022

In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here, we discuss how to make sense of what a neural network actually returns from the output layers. If you are like me, you may have been surprised when you first…

Read More

True DX in the Pharma R&D Lab Defined by Enthought

May 25, 2022

Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders gathering for technological insight and to revitalize market growth. 200 companies exhibited across the 3-day in-person event, which drew over 6,700 attendees. With digital transformation a headline theme, the show…

Read More

Life Sciences Labs Optimize with New Digital Technologies and Upskilling

May 16, 2022

Labs are resetting the trajectory for drug development: reducing timelines from years to months; decreasing costs from billions to millions; and gaining an advantage by delivering drugs to market in months rather than decades. This value combination is a compelling case for investment in digital capability and organizational transformation. A 100x or 1000x advance in…

Read More
Enthought | Configuring a Neural Network

Configuring a Neural Network Output Layer

May 18, 2023

Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just create an instance of the Sequential model class, add the number of desired layers and accompanying layer nodes, define the activation functions to be used by each layer, and compile…

Read More

Webinar Q&A: Accelerating Product Reformulation with Machine Learning

Oct 28, 2021

In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products to market faster. The webinar was well attended by scientists, engineers, and business leaders across the product development spectrum eager to learn how these concepts can be applied to their…

Read More

Scientists Who Code

Oct 6, 2021

Digital skills personas for success in digital transformation The digital skills mix varies widely across companies, from those just starting to invest in digital transformation initiatives, to ones well into their journey. Building a community of people who think digitally and are able to innovate and quickly prototype ideas is key to delivering results.  Author:…

Read More

The Challenges of Scaling Digital Advances in Life Sciences

Oct 6, 2021

Scaling innovations in R&D can be considered challenging in two dimensions: from lab to lab; and from the lab to the end customer. A 100x or 1000x advance in R&D efficiency challenges an organization to adapt, from R&D to engineering, manufacturing, and to the marketplace, where there is a significant business opportunity.   Incremental Advances…

Read More

Giving Visibility to Renewable Energy

Oct 6, 2021

The ultimate project goal of EnergizAIR Infrastructure was to raise individual awareness of the contribution of renewable energy sources, and ultimately change behaviors. Now ten years later, with orders of magnitude more data, AI/machine learning, cloud, and smartphones in the hands of individuals, this is an idea whose time has come. Author: Didrik Pinte, M.S.,…

Read More

Machine Learning in Materials Science

Aug 10, 2021

The process of materials discovery is complex and iterative, requiring a level of expertise to be done effectively. Materials workflows that require human judgement present a specific challenge to the discovery process, which can be leveraged as an opportunity to introduce digital technologies.  In the lab, many tasks require manual data collection and judgment. And…

Read More