Implementing AI - Deep Learning using TensorFlow and Keras
Deep Learning is a branch of machine learning that utilizes neural networks. But how does a neural network work, and how does deep learning solve machine learning problems?
Machine Learning and Functional Programming are both very hot topics these days: they are also both rather intimidating for the beginner. In this workshop, we´ll take a 100% hands-on approach, and learn practical ideas from Machine Learning, by tackling real-world problems and implementing solutions
Increasingly developers are relying on distributed architectures to solve the problems of scaling their applications and their development teams. But that means they now have to consider the problem of getting the parts of their systems to talk to each other.
This two day course is designed for developers who already know the fundamentals of Python. This course will get more "under the hood" and introduce the students to powerful tools and techniques that go beyond the basics.
Ta vårt nettkurs i Git: grunnleggende fra din datamaskin. Lær så mye du vil, når du vil. Du får gratis hjelp. Du får kursbevis. Du får tilgang til alle kurs. Meld deg på her!
Ta vårt nettkurs i Python: grunnleggende fra din datamaskin. Lær så mye du vil, når du vil. Du får gratis hjelp. Du får kursbevis. Du får tilgang til alle kurs. Meld deg på her!
Ta vårt nettkurs i Python: objektorientert programmering fra din datamaskin. Lær så mye du vil, når du vil. Du får gratis hjelp. Du får kursbevis. Du får tilgang til alle kurs. Meld deg på her!
TISIP kan tilby ulike kurs og seminarer av ulik lengde - både noen timer, heldagskurs og 2-3 dager intensivkurs. I noen av kursene reiser vi ut, mens i andre kurs kommer kursdeltakerne til Trondheim.
The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-means clustering, and hierarchical clustering.
This course provides an introduction to SAS Studio for experienced SAS programmers. SAS Studio is an interface that enables users to write and submit SAS programs and use snippets and tasks to generate SAS code.
This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets.
This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning, and artificial intelligence.
This course is for those who need to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data.
This course is for SAS programmers who prepare data for analysis. The comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation.
This course teaches you how to process SAS data using Structured Query Language (SQL).
Learn how to write programs that analyze written language. The course will balance theoretical foundations with practical examples using the Python programming language. No prior experience with libraries such as NLTK or scikit-learn is required for this course.
Python for Data Analysts & Quants. Use Python and its statistical computing libraries to analyse and visualise your financial data and to gather some actionable insights.