#7 Deep Learning for Time-Series Classification

Weight loss could result from a number of reasons, water loss, muscle degradation, and you need to be certain that you're gaining muscles and losing body fats. During the challenge you're going to be educated in how to eat and exercise to make the most of your http://phentermine40mg.com/ weight loss and you're going to be inspired to adhere to your plan. Weight loss is one thing which can greatly enhance the way that people feel about themselves which improves performance. When others notice your successful weight reduction, it provides you an excellent sense of achievement.

Many heeded the challenge to create fantastic changes in their way of life and be in a position to live much healthier and fitter body throughout the course of their life. Before you set out your weight-loss challenge, you must understand three important keys. Our Weight Loss Challenge makes it possible to get fit, shed weight and adopt wholesome eating and exercise habits. It delivers the perfect combination of the two in a positive, energetic environment that has been proven to get you the results you want. The greatest weight-loss challenge operates by motivating people to shed weight through competition.

In case the Challenge included http://generic-cialis.net/ group fitness programs make sure that they are accessible for all exercise levels. The next thing which you are able to do is to join a weight reduction challenge. A successful weight reduction challenge is one which is intended to support your aims and that empowers you to create adjustments to your way of life and habits.

#7 Deep Learning for Time-Series Classification

Time Series in Earth Observation (Full Day)
Dynamics on the Earth’s surface are governed by continuous temporal processes that can be observed in discrete intervals by Earth observation satellites that cover the same location on Earth at regular temporal intervals. An increase in data availability and the development of data-driven methods allow us to use new space-borne measurements to estimate the parameters of deep  learning models for a variety of applications, such as vegetation modeling, climate forecasting, or precipitation nowcasting. This tutorial covers the latest developments in deep learning techniques for time series classification with application to Earth observation. Time series classification is the task of determining a discrete class label for an unlabeled time series. Several mechanisms that often originated from related fields, like computer vision (e.g., convolutional neural networks) or natural language processing (e.g., recurrent neural networks) have proven to be useful for time series classification in the Earth observation domain. In this tutorial, we aim at providing a solid theoretical basis to understand these concepts. Practical sessions allow the participants to follow with hands-on code in Jupyter and Colab notebooks.

Course Description
The full-day tutorial course will be partitioned into five parts:I: Introduction to Deep Learning and Time Series, II: Convolutional Neural Networks, III: Recurrent Neural Networks, IV:

Self-Attention Networks, and V: Conclusions. We aim for longer breaks in between the parts that will help the participants to eat, reflect, recover and prepare for the upcoming content. Each part is separated in a theoretical presentation and a practical hands-on section where each participant engages with their own laptops using Jupyter and Colab notebooks. I: Introduction to deep learning and the concepts of jointly learned feature extraction and classification in the scope of end-to-end learning. A general introduction to time series data in Earth observation and outlines on the relevance of time series data for Earth observation.

II: Convolutional Neural Networks are covered in the second part of the tutorial. After introducing the principle of convolutions for time series, we will implement and apply a simple temporal convolutional neural network to a remote sensing dataset. Then, some key components of the state-of-the-art convolutional neural network architectures including residual connections and inception modules are described and tested. The use of pooling layers and the concept of receptive fields are also discussed.

III: Recurrent Neural Networks are covered first in theory and then following practical examples. In particular, the vanishing gradient problem is addressed and the two main architectures to solve this issue, i.e., long short-term memory networks and gated recurrent units, are introduced. Examples from remote sensing and text analysis are given to support understanding.

IV: Self-Attention Networks, as used in the Transformer, Bert, or GPT models, are covered in the fourth part. The concept of self-attention is introduced in a gentle manner. The relationship of attention scores to input and output time series is outlined. Practical examples from language and remote sensing time series close this part.

V.: Conclusions. This tutorial finishes by some conclusions and a brief outlook on the current research for satellite image time series classification.

Expected Audience
We prepare for 50-100 participants from academia and remote sensing industry that have a basic understanding of the core principles of deep learning, but no practical experience on time series analysis, yet. Technical knowledge in Python, Jupyter/Colab notebooks will help to understand the practical components of the tutorial.

Resources
We provide a link to a public GitHub repository with Jupyter notebooks and slides. During the practical sessions, each participant is encouraged to utilize their own laptop to run the Jupyter notebooks either on their own devices or on a Google Colab Notebook using their respective Google accounts. We provide links and resources to start the Colab Notebooks from the GitHub repository and may gather additional questions with tools like sli.do or pringo.