By: Alyssa Jones In a world of ever-growing interconnectivity, where industries intertwine and borders blur, the maritime sector emerges as a towering sentinel, a vital cornerstone of global trade and transportation. From cargo ships to offshore platforms, vessels fuel international commerce. However, as technology continues to revolutionize the maritime sector, a new set of challenges
Written by: Abby Jonah, Brigitta Zhao, and Tyson Cato H2O Conference Recap What defines the ocean sector? The ocean sector can be a scary term, but in hindsight it’s not scary at all! The ocean sector simply includes anyone learning about, working with, or interested in the ocean! When we, as DeepSense interns, were invited
Every February 11th, we celebrate International Day of Women and Girls in Science (IDWGIS) to help celebrate those working in science, technology, engineering, and mathematics (STEM) fields around the world. IGWGIS was created to bring light to the gender gap that exists in the STEM world, where even though we have made great strides, women
My name is Sakshi, and I am currently pursuing my second master’s at Dalhousie University. I am in the final year of my Master of Information program. I love my program as it is a combination of management and technology. Prior to this, I completed my master’s in business administration in finance and marketing from
Mentoring Matters: STEM Connector By Laura Eamon and Sarah Kilford Meet Sarah and Laura, two of the three co-op students gaining work experience in Halifax’s ocean industry this summer. Having already completed a Bachelor of Arts in Sustainability, Sarah is now a computer science student at Dalhousie University. Laura is an economics student at Saint
Happy World Ocean Day, Ocean Week Canada, Ocean Festival, and Ocean Decade! Annually on and around June 8, millions of people from 150+ countries participate in countless ways to take action on behalf of our shared ocean, such as organizing school events, aquatic cleanups, and community engagement through science and art. What’s happening globally
This it the final part of a three part series of how to build a dataset ofr machine learning. Find the previous parts below: Part 1: https://deepsense.ca/2022/02/09/blog-building-your-own-dataset-for-machine-learning/ Part 2: https://deepsense.ca/2022/02/17/part-2-acquiring-external-data/ Watch Amit’s Discovery Session on how to build an image based dataset: https://www.youtube.com/watch?v=3TT4hySJVvA ~~~~~~~ How much data is enough? Web scrapping is actually quite fun and
Find Part 1 of the blog series here: https://deepsense.ca/2022/02/09/blog-building-your-own-dataset-for-machine-learning/ Watch Amit’s Discovery Session on how to build an image based dataset: https://www.youtube.com/watch?v=3TT4hySJVvA Here are a few of the most relevant libraries for web scrapping: Requests: a python library for making http request such as GET, POST etc. It is much simpler and easy to use than
The following blog is Part 1 of a 3 Part Blog Series! Find Part 2 here: https://deepsense.ca/2022/02/17/part-2-acquiring-external-data/ Watch Amit’s Discovery Session on how to build an image based dataset: https://www.youtube.com/watch?v=3TT4hySJVvA We live in the information age. It is said to be a period in human history characterized by a shift from industrial production to one based
Before I joined IBM, I was working in a different field (DevOps, System Administration, Server Administration) which suited my passions well. But once I joined IBM, I was first asked to take up a project completely different from what I have done so far, a very intimidating situation. But, through the guidance and motivation from those
‘Would you be interested in writing a short blog we can post, to help other students try new things?’ ‘Yes, definitely’ It has been some days after the first Ocean of Data Challenge came to an end. For a quick recap, the challenge was to use open-source ocean data to explore the opportunities with the
One of the most common challenges anyone working on a machine learning or data science project faces is the computing time and memory problem of using a vast amount of data. Huge data requires huge computing resources to save time and memory. We worked on a project related to fish tracking. A fish tag,
Keras is an open-source framework that provides high level APIs for large machine learning applications such as neural networks. These APIs can run on top of the backend engines such as TensorFlow, CNTK (Cognitive Toolkit), Theano, MXNet and PlaidML. Its simple and easy-to-use architecture facilitates fast development of models. It is most suitable for Rapid
A crucial step in data management is the creation of a data dictionary. In general, a data dictionary is a central location where information about data is stored. This can be defined quite formally, but its implementation doesn’t need to be. Really, you just want a place to store information for future users of data.
GitHub is a code hosting platform that helps programmers and developers to keep their files at remote repositories and could work together on the same project from any location. It is a critical interesting concept to know for those who want to pursue their career in IT industry or who already are in IT whether they are programmers, developers, testers or data scientists. GitHub provides the following features: Version Control
Artificial Intelligence (AI) helps a computer to mimic humans with the help of Machine Learning (ML) algorithms. ML is the branch of AI that enables the machine to make predictions or classifications based on the data it has been trained on. Deep learning (DL) is the subset of machine learning that uses neural networks, works
Do you have data, but are unsure if it is suitable for AI/ML? There are many different types of data, but they generally boil down to a few categories. Typically, your data will be: numerical categorical time series text Numerical Numerical data can also be called quantitative. It can be a continuous variable, like temperature
One big hurdle that we often run into when trying to work with a company, is that they may not know what to do with the data they are collecting. Often, they will be collecting data over time, but when they need to make a decision, they may only use the real time data, without
I recently completed an MSc in Computer Science at Dalhousie University. For my thesis, I analyzed sets of AIS* data I had not used before starting my Master’s degree. I want to share some lessons I learned about how to start working with unfamiliar data. *AIS: Automatic identification system is an automatic tracking system for
Graphical Processing Units or GPUs are popular among data scientists for use in training machine/deep learning models. But what is the difference between GPUs and their companions, central processing units (CPUs)? There are several aspects of GPUs that differ from CPUs. GPUs and modern CPUs have various cores, rather than one. A core is