27-29 November, Vilnius
Conference about Big Data, High Load, Data Science, Machine Learning & AI
Early Bird Ends In:
Introduction to Deep Learning with TensorFlow
Deep Learning is a special and most promising variant of Supervised Machine Learning. Most recent break-throughs have been fueled by instead of programming a system, you instead use known data to train a system, like you do in deep learning. We will touch classic Neural Networks, Convolutional Neural Networks (CNNs) for image processing, and Recurrent Neural Networks (RNNs) for processing of texts and other sequences.
We will use TensorFlow with Keras-style Layers and provide notebooks hosted on Google’s Colab, that allow them to run on GPU. Thus there will be no need for any installation, all you need is a browser. We will use Python as our language, but you do not need any knowledge of it. Knowledge of any Object oriented language is sufficient.
- Intro to time series analysis:
– What is a time series
– Time series decomposition
– Stationarity testing
- Intro to Pyflux library
– Basic concepts and general overview of its functionalities
– Exploring models and their characteristics, assumptions and parameters
- Hands-on example
Create several forecasting models (ARIMA, VAR, GARCH,…)
- divide a set into train and test sets
- define a model
- fit a model
- extract model results and estimate model perfomance
- comparative analysis of different models.
The main goal of this workshop is to introduce participants with main concepts of time series analysis, as well as with forecasting methods available in the PyFlux library.
The target audience includes those interested in time series forecasting techniques, examples and applications. Everybody who loves programming as well as data science is invited to come, and learn something new.
– Anaconda environment with Python 2.7, and Pyflux library installed on it.
– Technical knowledge:
– Basic programming in Python, or some other programming language
– Basic math knowledge.