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
We are often asked how much data one needs when using artificial intelligence/machine learning techniques. Unfortunately, there is no simple answer. It will depend highly on what type of data you have, and what methods you are employing. Is it possible to have too little data? Yes, but there can be some easy fixes. The
HPC stands for High Performance Computing. It can also be used as a synonym for supercomputing. An HPC infrastructure has the following characteristics: A huge amount of computation resources Advanced computation capabilities Fast interconnections Parallel processing CPU/GPU computation Advanced file systems Enhanced securities Large storage. HPC is widely used for scientific computation, application development, machine