What is Kernel in Machine Learning?

Serdar Palaoglu

In the realm of machine learning, kernels hold a pivotal role, especially in algorithms designed for classification and regression tasks like Support Vector Machines (SVMs). The kernel function is the heart of these algorithms, adept at simplifying the complexity inherent in data. It transforms non-linear relationships into a linear format, making them accessible for algorithms that traditionally only handle linear data. This transformation is important for allowing SVMs to unravel and make sense of complex patterns and relationships. Kernels achieve this without the computational intensity of mapping data to higher dimensions explicitly. Their efficiency and effectiveness in revealing hidden patterns make them a cornerstone in modern machine learning. As we explore kernels further, we uncover their significance in enhancing the performance and applicability of SVMs in diverse scenarios.

Ensembles in Machine Learning: Combining Multiple Models

Serdar Palaoglu

In the ever-evolving landscape of machine learning, the quest for improved predictive accuracy has led to the development of ensemble methods. These techniques harness the collective power of multiple models to achieve better performance than any single model could on its own. This article delves into ensemble learning, exploring how the combination of diverse algorithms can lead to more robust, generalizable, and accurate machine learning solutions.

Deep Learning vs Machine Learning: What is the difference?

Serdar Palaoglu

In the realm of artificial intelligence, two fundamental concepts, Machine Learning and Deep Learning, have emerged as key components in the advancement of computer-based learning systems. Machine Learning serves as a foundational principle where computers gain the ability to learn from data without explicit programming. Deep Learning, an evolution within the Machine Learning framework, utilizes artificial neural networks inspired by the human brain to achieve complex data analysis. This article delves into a comprehensive exploration of these domains, elucidating their differences, practical applications, and significance in artificial intelligence.

What is Meta-Learning? Benefits, Applications and Challenges

Jan Macdonald (PhD)

Data-driven algorithms, such as machine learning and particularly deep learning models, have achieved unprecedented successes in diverse application areas, ranging from computer vision to audio and signal processing to natural language processing. Most commonly, machines “learn” to solve a specific task in a supervised manner by observing a large amount of labeled example data. Think of an image classification model that learns to distinguish different animals by being presented with many example images of each different animal type. This differs significantly from the way we humans tend to learn: After having been exposed to recognizing different animals repeatedly throughout our life, we are able to learn the concept of a new type of animal after seeing only very few examples. Incorporating such “adaptive” learning strategies into the field of machine learning is at the core of meta-learning. This was already explored in the 1980s and 1990s, e.g., by Schmidhuber (Schmidhuber, 1987) and Bengio et al. (Bengio et al., 1991). Recently, with the rapid improvements in deep learning, the interest in neural network based meta-learning approaches has increased and a wide range of variants have been proposed and developed. We will take a more detailed look at a selection of them below.

Fairness in Machine Learning

Cornelius Braun

In a previous blog post , we explained the plenitude of human biases that are often present in real-world data sets. Since practitioners may be forced to work with biased data, it is crucial to know about ways in which the fairness of model decisions can nevertheless be guaranteed. Thus, in this post, I explain the most important ideas around fairness in machine learning (ML). This includes a short summary of the main metrics to measure the fairness of your model decisions and an overview of tools that can help you guarantee or improve your model's fairness.

Latest developments in the world of Natural Language Processing: A comparison of different language models

Justus Tschötsch

Natural language processing (NLP) is a rapidly evolving sub-field of artificial intelligence. With ever new developments and breakthroughs, language models are already able to understand and generate human-like language with impressive accuracy. To keep track and catch up, we will compare different language models and have a look at the latest advancements, opportunities, and challenges of natural language processing.

How ChatGPT is fine-tuned using Reinforcement Learning

Thanh Long Phan

At the end of 2022, OpenAI released ChatGPT (a Transformer-based language model) to the public. Although based on the already widely discussed GPT-3, it launched an unprecedented boom in generative AI. It is capable of generating human-like text and has a wide range of applications, including language translation, language modeling, and generating text for applications such as chatbots. ChatGPT seems to be so powerful that many people consider it to be a substantial step towards artificial general intelligence. The main reason for the recent successes of language models such as ChatGPT lies in their size (in terms of trainable parameters). But making language models bigger does not inherently make them better at following a user's intent. A bigger model can also become more toxic and more likely to "hallucinate". To mitigate these issues and to more generally align models to user intentions, one option is to apply Reinforcement Learning. In this blog post, we will present an overview of the training process of ChatGPT, and have a closer look at the use of Reinforcement Learning in language modeling.

Early Classification of Crop Fields through Satellite Image Time Series

Tiago Sanona

In a fast paced and always changing global economy the ability to classify crop fields via remote sensing at the end of a growth cycle does not provide the much needed immediate insight required by decision makers. To address this problem we developed a model that allows continuous classification of crop fields at any point in time and improves predictions as more data becomes available. In practice, we developed a single model capable of delivering predictions about which crops are growing at any point in time based on satellite data. The data available at the time of inference could be a few images at the beginning of the year or a full time series of images from a complete growing cycle. This exceeds the capabilities of current deep learning solutions that either only offer predictions at the end of the growing cycle or have to use multiple models that are specialized to return results from pre-specified points in time. This article details the key changes we employed to the model described in a previous blog post “Classification of Crop fields through Satellite Image Time Series” that enlarges its functionality and performance. The results presented in this article are based on a research paper recently published by dida. For more detailed information about this topic and other experiments on this model please check out the original manuscript: “Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention” .

Leveraging Machine Learning for Environmental Protection

Edit Szügyi

Machine Learning has been solving complex problems for decades. Just think about how Computer Vision methods can reliably predict life-threatening diseases, self-driving cars are on their way to revolutionize traffic safety, or automatic translation gives us the ability to talk to just about anyone on the planet. The power of Machine Learning has been embraced by many branches of industry and science. There are some areas however where the potential of Machine Learning is harder to see and also less utilized. One of these is environmental protection. Protecting the natural environment is one of the biggest challenges our generation is facing, with pressing issues such as climate change, plastic pollution or resource depletion. Let us now look at how Machine Learning has been and can be used as a tool in environmental protection.

Managing layered requirements with pip-tools

Augusto Stoffel (PhD)

When building Python applications for production, it's good practice to pin all dependency versions, a process also known as “freezing the requirements”. This makes the deployments reproducible and predictable. (For libraries and user applications, the needs are quite different; in this case, one should support a large range of versions for each dependency, in order to reduce the potential for conflicts.) In this post, we explain how to manage a layered requirements setup without forgoing the improved conflict resolution algorithm introduced recently in pip. We provide a Makefile that you can use right away in any of your projects!

Collaborative Filtering in Recommender Systems

Konrad Mundinger

In this blog post, I give an overview and provide some Python code for several collaborative filtering techniques. This is the second blog post in a series of articles about recommendation engines. Check out the first article if you want to get an overview of recommendation systems in general or need a refresher on the terminology. The Jupyter notebook I used for creating the plots will be made available soon. The techniques will be illustrated on the famous MovieLens-100K dataset. It contains 100.000 user-movie rating pairs from 943 users on 1682 movies. For most of the algorithms, I have used an existing implementation from the surprise library for Python. Even though it needs some getting used to, I think it is a nice library that you should check out if you are starting to play around with recommendation engines.

An Introduction to Metric Learning

William Clemens (PhD)

Probably the most common form of problem we tackle with machine learning is classification, that is taking new data points and putting them into one of a number of fixed sets or classes. But what if we don’t necessarily know all the classes when we train the model? A good example of this is face recognition where we want a system that can store faces and then identify if any new images it sees contain that face. Obviously, we can’t retrain the model every time we add someone new to the database so we need a better solution. One way to solve this problem is metric learning. In metric learning, our goal is to learn a metric or distance measure between different data points. If we train our model correctly then this distance measure will put examples of the same class close together and different classes further apart.

Recommendation systems - an overview

Konrad Mundinger

Recommendation systems are everywhere. We use them to buy clothes, find restaurants and choose which TV show to watch. In this blog post, I will give an overview of the underlying basic concepts, common use cases and discuss some limitations. This is the first of a series of articles about recommendation engines. Stay tuned for the follow-ups, where we will explore some of the mentioned concepts in much more detail! Already in 2010, 60 % of watch time on Youtube came from recommendations [1] and personalized recommendations are said to increase conversion rates on e-commerce sites by up to 5 times [2]. It is safe to say that if customers are presented with a nice pre-selection of products they will be less overwhelmed, more likely to consume something and have an overall better experience on the website. But how do recommendation engines work? Let's dive right in.

The best (Python) tools for remote sensing

Emilius Richter

An estimated number of 906 Earth observation satellites are currently in orbit, providing science and industry with many terabytes of data every day. The satellites operate with both radar as well as optical sensors and cover different spectral ranges with varying spectral, spatial, and temporal resolutions. Due to this broad spectrum of geospatial data, it is possible to find new applications for remote sensing methods in many industrial and governmental institutions. On our website, you can find some projects in which we have successfully used satellite data and possible use cases of remote sensing methods for various industries . Well-known satellite systems and programs include Sentinel-1 (radar) and Sentinel-2 (optical) from ESA, Landsat (optical) from NASA, TerraSAR-X and TanDEM-X (both radar) from DLR, and PlanetScope (optical) from Planet. There are basically two types of geospatial data: raster data and vector data . Raster data Raster data are a grid of regularly spaced pixels, where each pixel is associated with a geographic location, and are represented as a matrix. The pixel values depend on the type of information that is stored, e.g., brightness values for digital images or temperature values for thermal images. The size of the pixels also determines the spatial resolution of the raster. Geospatial raster data are thus used to represent satellite imagery. Raster images usually contain several bands or channels, e.g. a red, green, and blue channel. In satellite data, there are also often infrared and/or ultraviolet bands. Vector data Vector data represent geographic features on the earth's surface, such as cities, country borders, roads, bodies of water, property rights, etc.. Such features are represented by one or more connected vertices, where a vertex defines a position in space by x-, y- and z-values. A single vertex is a point, multiple connected vertices are a line, and multiple (>3) connected and closed vertices are called polygons. The x-, y-, and z-values are always related to the corresponding coordinate reference system (CRS) that is stored in vector files as meta information. The most common file formats for vector data are GeoJSON, KML, and SHAPEFILE. In order to process and analyze these data, various tools are required. In the following, I will present the tools we at dida have had the best experience with and which are regularly used in our remote sensing projects. I present the tools one by one, grouped into the following sections: Requesting satellite data EOBrowser Sentinelsat Sentinelhub Processing raster data Rasterio Pyproj SNAP pyroSAR Rioxarray Processing vector data Shapely Python-geojson Geopandas Fiona Providing geospatial data QGIS GeoServer Leafmap Processing meteorological satellite data Wetterdienst Wradlib

Project proposals - the first step to a successful ML project

Emilius Richter

Many machine learning (ML) projects are doomed to fail. This can be due to various reasons and often they occur in combination. To avoid failure, all involved stakeholders need to understand the technical and organizational requirements of the project. Besides all preliminary discussions that define the project, it is important to summarize the project-relevant information in a comprehensive proposal. It should cover the technical and organizational requirements, possible problem areas and technical restrictions. In this article, I will describe the most important modules in machine learning project proposals. For a software provider like dida, the project proposal is the first step towards meeting the needs of the customer.

Image Captioning with Attention

Madina Kasymova

One sees an image and easily tells what is happening in it because it is humans’ basic ability to grasp and describe details about an image by just having a glance. Can machines recognize different objects and their relationships in an image and describe them in a natural language just like humans do? This is the problem image captioning tries to solve. Image captioning is all about describing images in natural language (such as English), combining two core topics of artificial intelligence: computer vision and natural language processing . Image captioning is an incredible application of deep learning that evolved considerably in recent years. This article will provide a high-level overview of image captioning architecture and explore the attention mechanism – the most common approach proposed to solve this problem. The most recent image captioning works have shown benefits in using a transformer-based approach, which is based solely on attention and learns the relationships between elements of a sequence without using recurrent or convolutional layers. We will not be considering transformer-based architectures here, instead we will focus only on the attention-based approach.

AI Index Report 2022: key findings about the status quo of AI

David Berscheid

The AI Index Report tracks and collects data regarding the worldwide development of artificial intelligence (AI). This years fifth edition, by the independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), is again aimed at informing relevant stakeholders like policy makers, researcher or related industries about the enormous advances of AI, the technological and societal stages of most prominent AI disciplines, as well as creating awareness for arising problems. In this article, we will discuss a selection of the report’s machine learning (ML)-related key messages as well as respectively add dida’s perspective to the following topics: Research and Development Technical Performance Technical AI Ethics The Economy and Education AI Policy and Governance For the full report please visit the original source here .

Data Privacy: Machine Learning and the GDPR

Ana Guerra

Datasets are essential for the research and development of models in the fields of Natural Language Processing (NLP) and Machine Learning (ML). However, while the use, collection, and storage of data increases, concerns about data privacy intensify as well. To be in line with best practices, it is relevant to understand what data privacy means and how it is regulated. This post will therefore offer a brief overview of how data privacy is regulated within the European Union. Besides following EU regulation, data driven projects have also to be ethically responsible. In consequence, this article ends with some words about ethics while processing personal data.

How to implement a labeling tool for image classification in a Jupyter notebook

'Hotdog' or 'not hotdog'? That could be the question — at least when performing an image classification task. To be able to address this or a similarly important question by means of a machine learning model, we first need to come up with a labeled dataset for training. That is, we sometimes have to manually look at hundreds or even thousands of images that do or do not contain hotdogs, and decide if they do. One way to do that would be to open up one image at a time and keep track of image classes in another file, e.g., a spreadsheet. However, such a heavy-handed approach sounds rather tedious and is likely prone to fat-fingering errors. Wouldn't it be great if there was a streamlined solution that makes this labeling process more efficient, even fun? That is exactly right and also what we set out to do in this article: Create a simple annotation tool to easily assign class labels to a set of images.

Ethics in Natural Language Processing

Marty Oelschläger (PhD)

AI and machine learning have entered significantly into our day-to-day lives. For example, we use search queries and are startled or even angered if the algorithm did not understand what we were actually looking for. Just image what an effort it would be to process all those queries by human individuals. In case you can't imagine, CollegeHumor already prepared a vision of that: Fortunately, we taught machines --- at least to some degree --- to "understand" human language. This branch of machine learning is called natural language processing (NLP). We already gave an introduction , if you want to review the basics. However, since search engines, chat bots, and other NLP algorithms are not humans, we can employ them on large scale, i.e. on global scale. Since there are ubiquitous and used by very different people in various contexts, we want them to be objective and neutral (and not to be an annoyed and skeptical man as in the video above). But what if they are not the neutral number crunchers? What if they are subjective and even carry harmful stereotypes against specific groups?

GPT-3 and beyond - Part 2: Shortcomings and remedies

Fabian Gringel

In the first part of this article I have described the basic idea behind GPT-3 and given some examples of what it is good at. This second and final part is dedicated to the “beyond” in the title. Here you will learn in which situations GPT-3 fails and why it is far from having proper natural language understanding, which approaches can help to mitigate the issues and might lead to the next breakthrough, what alternatives to GPT-3 there are already, and, in case you are wondering, what's the connection between GPT-3 and an octopus. Update February 14th '22: I have also included a section about OpenAI's new InstructGPT.

Data-centric Machine Learning: Making customized ML solutions production-ready

David Berscheid

By 2021, there is little doubt that Machine Learning (ML) brings great potential to today’s world. In a study by Bitkom , 30% of companies in Germany state that they have planned or least discussed attempts to leverage the value of ML. But while the companies’ willingness to invest in ML is rising, Accenture estimates that 80% – 85% of these projects remain a proof of concept and are not brought into production. Therefore at dida, we made it our core mission to bridge that gap between proof of concept and production software, which we achieve by applying data-centric techniques, among other things. In this article, we will see why many ML Projects do not make it into production, introduce the concepts of model- and data-centric ML, and give examples how we at dida improve projects by applying data-centric techniques.

GPT-3 and beyond - Part 1: The basic recipe

Fabian Gringel

GPT-3 is a neural network capable of solving a wide range of natural language processing (NLP) tasks, which has been presented by OpenAI in summer 2020 (upscaling the previous models GPT and GPT-2). For various tasks it has set new state-of-the-art performances and is considered by many as a substantial step into the direction of artificial general intelligence. “General intelligence” refers to the capability of not only behaving intelligently with respect to one set task, but also being able to adapt to and accomplish new, unforeseen tasks. This blog article is the first of a two-article-series on GPT-3. In this first article I will explain how GPT-3 works, what it is good at and why some people think it’s dangerous, and how you can try out a GPT-3-like model for free. The second part will deal with GPT-3’s weaknesses and where to expect the next breakthrough in the future.

Classification of Crop Fields through Satellite Image Time Series

Tiago Sanona

The field of remote sensing has been benefiting from the advancements made in Machine Learning (ML). In this article we explore a state of the art model architecture, the Transformer , initially developed for Natural Language Processing (NLP) problems, which is now widely used with many forms of sequential data. Following the paper by Garnot et al. , we utilize an altered version of this architecture to classify crop fields from time series of satellite images . With this, we achieve better results than traditional methods (e. g. random forests) and with less resources than recurrent networks.

Extracting information from technical drawings

Frank Weilandt (PhD)

Did you ever need to combine data about an object from two different sources, say, images and text? We are often facing such challenges during our work at dida. Here we present an example from the realm of technical drawings. Such drawings are used in many fields for specialists to share information. They consist of drawings that follow very specific guidelines so that every specialist can understand what is depicted on them. Normally, technical drawings are given in formats that allow indexing, such as svg, html, dwg, dwf, etc. but many, especially older ones, only exist in image format (jpeg, png, bmp, etc.), for example from book scans. This kind of drawings is hard to access automatically which makes its use hard and time consuming. In this regard, automatic detection tools could be used to facilitate the search. In this blogpost, we will demonstrate how both traditional and deep-learning based computer vision techniques can be applied for information extraction from exploded-view drawings. We assume that such a drawing is given together with some textual information for each object on the drawing. The objects can be identified by numbers connected to them. Here is a rather simple example of such a drawing: An electric drill machine. There are three key components on each drawing: The numbers, the objects and the auxiliary lines. The auxiliary lines are used to connect the objects to the numbers. The task at hand will be to find all objects of a certain kind / class over a large number of drawings , e.g. the socket with number 653 in the image above appears in several drawings and even in drawings from other manufacturers. This is a typical classification task, but with a caveat: Since there is additional information for each object accessible through the numbers, we need to assign each number on the image to the corresponding object first. Next we describe this auxiliary task can be solved by using traditional computer vision techniques.

Visual Transformers: How an architecture designed for NLP enters the field of Computer Vision

Konrad Mundinger

Since its first introduction in late 2017, the Transformer has quickly become the state of the art architecture in the field of natural language processing (NLP). Recently, researchers started to apply the underlying ideas to the field of computer vision and the results suggest that the resulting Visual Transformers are outperforming their CNN-based predecessors in terms of both speed and accuracy. In this blogpost, we will have a closer look at how to apply transformers to computer vision tasks and what it means to tokenize an image.

CLIP: Mining the treasure trove of unlabeled image data

Fabian Gringel

Digitization and the internet in particular have not only provided us with a seemingly inexhaustible source of textual data, but also of images. In the case of texts, this treasure has been lifted in the form of task-agnostic pretraining by language models such as BERT or GPT-3. Contrastive Language-Image Pretraining (short: CLIP) now does a similar thing with images, or rather: the combination of images and texts. In this blog article I will give a rough non-technical outline of how CLIP works, and I will also show how you can try CLIP out yourself! If you are more technically minded and care about the details, then I recommend reading the original publication , which I think is well written and comprehensible.

21 questions we ask our clients: Starting a successful ML project

Emilius Richter

Automating processes using machine learning (ML) algorithms can increase the efficiency of a system beyond human capacity and thus becomes more and more popular in many industries. But between an idea and a well-defined project there are several points that need to be considered in order to properly assess the economic potential and technical complexity of the project. Especially for companies like dida that offer custom workflow automation software, a well-prepared project helps to quickly assess the feasibility and the overall technical complexity of the project goals -which, in turn, makes it possible to deliver software that fulfills the client's requirements. In this article, we discuss which topics should be considered in advance and why the questions we ask are important to start a successful ML software project.

Enhancing Search with Question Answering

What is called open-domain question answering in machine learning papers is nothing else than answering a question based on a large collection of texts, such as answering the question of a visitor of a large website, using the website's content. Due to recent progress in machine reading comprehension, open-domain question answering systems have drastically improved. They used to rely on redundancy of information but now they are able to “read” more carefully. Modern systems are able to quote a section of text that answers the question or even reformulate it. What is still an aspiration is to generate longer, paragraph-length answers or to use multiple sources to puzzle together an answer. Google recently implemented such a feature into their search engine. If they find a passage that answers the question typed into the search field, the first result shows the corresponding website with the passage highlighted. There are many different systems that tackle open-domain question answering, here I will go into detail on one system in particular, DrQA (by Chen et al. 2017 ). This particular system splits the task into two parts for each of which it is easier to get data than for the combined task. I will also explain how this idea can be used to create a question answering system for a website from an already existing search function.

The best image labeling tools for Computer Vision

Dmitrii Iakushechkin

Creating a high quality data set is a crucial part of any machine learning project. In practice, this often takes longer than the actual training and hyperparameter optimization. Thus choosing an appropriate tool for labeling is essential. Here we will have a closer look at some of the best image labeling tools for Computer Vision tasks: labelme labelImg CVAT Labelbox We will install and configure the tools and illustrate their capabilities by applying them to label real images for an object detection task. We will proceed by looking at the above tools one by one.

Using satellite imagery for greenfield exploration

Fabian Dechent

Unsurprisingly, a major requirement that makes mining endeavours successful is the right location - one where the enterprise knows with confidence that the soil bears high grade minerals of interest. Finding such a site, however, poses a significant challenge. Conventionally, when mining enterprises pursue greenfield exploration, field studies and drillings are conducted. As these are very expensive, they should only serve as a last assurance after potentially interesting regions have been identified. This is where Remote Sensing comes into play. In this article, we will have a look at the possibilities that spaceborne imaging provides for greenfield exploration. Let’s have a satellite scout promising spots.

Understanding graph neural networks by way of convolutional nets

Augusto Stoffel (PhD)

In this article, we will introduce the basic ideas behind graph neural networks (GNNs) through an analogy with convolutional neural networks (CNNs), which are very well known due to their prevalence in the field of computer vision. In fact, we'll see that convolutional nets are an example of GNNs, albeit one where the underlying graph is very simple, perhaps even boring. Once we see how to think of a convolutional net through this lens, it won't be hard to replace that boring graph with more interesting ones, and we'll arrive naturally at the general concept of GNN. After that, we will survey some applications of GNNs, including our use here at dida. But let's start with the basics.

Understanding and converting MGRS coordinates in Python

Tiago Sanona

Working with satellite data , one needs to understand and possibly convert the coordinates the data is given in. Sometimes, especially if released by official bodies, satellite data is provided in MGRS tiles , which are derived from the UTM coordinate system. For example, this is true for Sentinel-2 tiles. I want to answer the following three questions in this post, using the Python libraries mgrs and pyproj : What is the difference between MGRS and UTM? To which MGRS tile does a certain point referenced in latitude and longitude degrees belong to? How can I express a MGRS tile in Lat/Lon coordinates? Before we answer these questions, let's first look into what MGRS is.

Monitoring urban development from space

Johan Dettmar

Urbanisation on a global scale is happening at an ever increasing rate. In the year 2008, more than 50% of the worlds population lived in cities and it is predicted that by 2050 about 64% of the developing world and 86% of the developed world will be urbanised. This trend puts significant stress on infrastructure planning. Providing everything from sanitation, water systems and transportation to adequate housing for more than 1.1 billion new urbanites over the next 10 years will be an extraordinary challenge. In a research project for the European Space Agency's program "AI for social impact", dida assessed the use of state-of-the-art computer vision methods for monitoring urban development over time of three rapidly growing cities in west Africa: Lagos, Accra and Luanda. The population of these cities are expected to grow by 30-55% in size by the end of 2030 which means that in-situ data collection about how these cities develop is almost impossible given the available resources. Instead, we came up with a concept that would rely solely on satellite images and machine learning.

How to identify duplicate files with Python

Ewelina Fiebig

Suppose you are working on an NLP project. Your input data are probably files like PDF, JPG, XML, TXT or similar and there are a lot of them. It is not unusual that in large data sets some documents with different names have exactly the same content, i.e. they are duplicates. There can be various reasons for this. Probably the most common one is improper storage and archiving of the documents. Regardless of the cause, it is important to find the duplicates and remove them from the data set before you start labeling the documents. In this blog post I will briefly demonstrate how the contents of different files can be compared using the Python module filecmp . After the duplicates have been identified, I will show how they can be deleted automatically.

Detecting illegal mines from space

Matthias Werner

Throughout the globe, rain forests and other natural landscapes are endangered by illegal mining, which transforms areas formerly rich in flora and fauna into wasteland. In order for local governments to take countermeasures, they first need to know about the locations of illegal mines. In countries covered by vast areas of impenetrable rain forest, such as Brazil or Congo, obtaining this information is a difficult problem. In this blog post I describe an approach to detect illegal mines based on deep learning and remote sensing, that we have developed to support the conservation efforts of governments and NGOs. In particular, we use a U-Net for semantic segmentation , a branch of computer vision. As part of the project of automatic detection of illegal mines , we were also joined by scientists from the Institute of Mineral Resources Engineering of the RWTH Aachen University, who contributed their mining-specific expertise. The project was funded by the European Space Agency .


Matthias Werner

In our day-to-day work, we at dida found many parallels in the workflow of different projects. In order to speed up the development procedure in future projects and to facilitate adherence to best practices, we started the development of didatools . The package is meant as a collection of templates of code for training machine learning models, deployment and other frequently required patterns. It is easily extendable and the user should feel encouraged to modify the code to their liking. Here I guide you through the first steps of initializing and using didatools .

How to extract text from PDF files

Lovis Schmidt

In NLP projects the input documents often come as PDFs. Sometimes the PDFs already contain underlying text information, which makes it possible to extract text without the use of OCR tools. In the following I want to present some open-source PDF tools available in Python that can be used to extract text. I will compare their features and point out some drawbacks. Those tools are PyPDF2 , pdfminer and PyMuPDF . There are other Python PDF libraries which are either not able to extract text or focused on other tasks. Furthermore, there are tools that are able to extract text from PDF documents, but which are not available in Python. Both will not be discussed here.

What is Reinforcement Learning? (Part 2)

Matthias Werner

In the previous post we introduced the basics of reinforcement learning (RL) and the type of problem it can be applied to. The discussed setting was limited in the sense that we were dealing with a single agent acting in a stationary environment. Now we will take it one step further and discuss Multi-Agent Reinforcement Learning ( MARL ). Here we deal with multiple explicitly modeled agents in the same environment, hence every agent is part of the environment as it is perceived by all others. Since all agents learn over time and start to behave differently, the assumption of a stationary environment is violated.

BERT for question answering (Part 1)

Mattes Mollenhauer (PhD)

In this article, we are going to have a closer look at BERT - a state-of-the-art model for a range of various problems in natural language processing. BERT was developed by Google and published in 2018 and is for example used as a part of Googles search engine . The term BERT is an acronym for the term Bidirectional Encoder Representations from Transformers , which may seem quiet cryptic at first. The article is split up into two parts: In the first part we are going to see how BERT works and in the second part we will have a look at some of its practical applications - in particular, we are going to examine the problem of automated question answering .

What is Reinforcement Learning? (Part 1)

Matthias Werner

Machine Learning concerns itself with solving complicated tasks by having a software learn the rules of a process from data. One can try to discover structure in an unknown data set (unsupervised learning) or one can try to learn a mathematical function between related quantities (supervised learning). But what if you wanted the algorithm to learn to react to its environment and to behave in a particular way? No worries, machine learning’s got you covered! This branch of Machine Learning (ML) is called Reinforcement Learning (RL). In this post we will give a quick introduction to the general framework and look at a few basic solution attempts in more detail. Finally, we will give a visual example of RL at work and discuss further approaches. In the second part of the blog post we will discuss Multi-Agent Reinforcement Learning (MARL).

Can we do without labeled data? (Un)supervised ML

Lorenzo Melchior

It seems to be a common mistake to believe that machine learning is usually an unsupervised task : you have data (without pre-existing labels) that you train e.g. a neural network on for tasks like classification or image segmentation. The truth is that most models in machine learning are supervised , that is, they rely on labeled training data . But labeling often takes a lot of time and can be very tedious. In this blog post I want to find out if I am able to perform the same classification task once with labels, once without. For this task I will use the famous MNIST data set , which contains 60,000 training and 10,000 validation images of handwritten digits, all of them labeled. Every image consists of 28x28 greyscale pixels and contains only one digit, located in the center of the image. To make things easier, I use the CSV version of the data set.

The best free labeling tools for text annotation in NLP

Fabian Gringel

In this blog post I'm going to present the three best free text annotation tools for manually labeling documents in NLP ( Natural Language Processing ) projects. You will learn how to install, configure and use them and find out which one of them suits your purposes best . The tools I'm going to present are brat , doccano , INCEpTION . The selection is based on this comprehensive scientific review article and our hands-on experience at dida. I will discuss the tools one by one. For each of them, I will first give a general overview about what the tool is suited for, and then provide details (or links) regarding installation, configuration and usage.

How to recognise objects in videos with PyTorch

William Clemens (PhD)

Self-driving cars still have difficulties in detecting objects in front of them with sufficient reliability. In general, though, the performance of state-of-the-art object detection models is already very impressive - and they are not too difficult to apply. Here I will walk you through streaming a YouTube video into Python and then applying a pre-trained PyTorch model to it in order to detect objects. We'll be applying a model pre-trained on the object detection dataset COCO . (In reality, the model would of course be fine tuned to the task at hand.)

Digital public administration: intuitive online access through AI

Jona Welsch

The following article describes how AI can help to establish digital public administration services. To begin with, a fundamental problem is described that AI can solve at this point: Authorities often speak a language that is very different from the colloquial language. Using the example of business registrations and the AI model "BERT", a possible solution is explained and ideas for further areas of application are shown.

What is Bayesian Linear Regression? (Part 1)

Matthias Werner

Bayesian regression methods are very powerful, as they not only provide us with point estimates of regression parameters, but rather deliver an entire distribution over these parameters. This can be understood as not only learning one model, but an entire family of models and giving them different weights according to their likelihood of being correct. As this weight distribution depends on the observed data, Bayesian methods can give us an uncertainty quantification of our predictions representing what the model was able to learn from the data. The uncertainty measure could be e.g. the standard deviation of the predictions of all the models, something that point estimators will not provide by default. Knowing what the model doesn't know helps to make AI more explainable. To clarify the basic idea of Bayesian regression, we will stick to discussing Bayesian Linear Regression (BLR). BLR is the Bayesian approach to linear regression analysis. We will start with an example to motivate the method. To make things clearer, we will then introduce a couple of non-Bayesian methods that the reader might already be familiar with and discuss how they relate to Bayesian regression. In the following I assume that you have elementary knowledge of linear algebra and stochastics. Let's get started!

Beat Tracking with Deep Neural Networks

Julius Richter

This is the last post in the three part series covering machine learning approaches for time series and sequence modeling. In the first post , the basic principles and techniques for serial sequences in artificial neural networks were shown. The second post introduced a recent convolutional approach for time series called temporal convolutional network (TCN), which shows great performance on sequence-to-sequence tasks ( Bai, 2018 ). In this post, however, I will talk about a real world application which employs a machine learning model for time series analysis. To this end, I will present a beat tracking algorithm, which is a computational method for extracting the beat positions from audio signals. The presented beat tracking system ( Davies, 2019 ) is based on the TCN architecture which captures the sequential structure of audio input.

Comparison of OCR tools: how to choose the best tool for your project

Fabian Gringel

Optical character recognition (short: OCR) is the task of automatically extracting text from images (coming as typical image formats such as PNG or JPG, but possibly also as a PDF file). Nowadays, there are a variety of OCR software tools and services for text recognition which are easy to use and make this task a no-brainer. In this blog post, I will compare four of the most popular tools: Tesseract OCR ABBYY FineReader Google Cloud Vision Amazon Textract I will show how to use them and assess their strengths and weaknesses based on their performance on a number of tasks. After reading this article you will be able to choose and apply an OCR tool suiting the needs of your project. Note that we restrict our focus on OCR for document images only, as opposed to any images containing text incidentally. Now let’s have a look at the document images we will use to assess the OCR engines.

Temporal convolutional networks for sequence modeling

Julius Richter

This blog post is the second in a three part series covering machine learning approaches for time series. In the first post , I talked about how to deal with serial sequences in artificial neural networks. In particular, recurrent models such as the LSTM were presented as an approach to process temporal data in order to analyze or predict future events. In this post, however, I will present a simple but powerful convolutional approach for sequences which is called Temporal Convolutional Network (TCN). The network architecture was proposed in ( Bai, 2018 ) and shows great performance on sequence-to-sequence tasks like machine translation or speech synthesis in text-to-speech (TTS) systems. Before I describe the architectural elements in detail, I will give a short introduction about sequence-to-sequence learning and the background of TCNs.

Machine Learning Approaches for Time Series

Julius Richter

This post is the first part of a series of posts that are linked together as they all deal with the topic of time series and sequence modeling, respectively. In order to give a comprehensive piece of content easy to grasp, the series is segmented into three parts: How to deal with time series and serial sequences? A recurrent approach. Temporal Convolutional Networks (TCNs) for sequence modeling. Beat tracking in audio files as an application of sequence modeling.

How to distribute a Tensorflow model as a JavaScript web app

Johan Dettmar

Anyone wanting to train a Machine Learning (ML) model these days has a plethora of Python frameworks to choose from. However, when it comes to distributing your trained model to something other than a Python environment, the number of options quickly drops. Luckily there is Tensorflow.js , a JavaScript (JS) subset of the popular Python framework with the same name. By converting a model such that it can be loaded by the JS framework, the inference can be done effectively in a web browser or a mobile app. The goal of this article is to show how to train a model in Python and then deploy it as a JS app which can be distributed online.

Detecting clouds in satellite images using convolutional neural networksd

William Clemens (PhD)

Here I’m going to walk through how we approached the problem of detecting convective clouds in satellite data including what we are looking for (and why!) and the machine learning approach we used. This post will consist of four sections: First we will introduce convective clouds and give a brief overview of the problem. In section 2 we will discuss the satellite data we are working with. In section 3 we discuss how we go about manually labelling the data, which is a particularly difficult task requiring the use of some external data. Finally, in section 4 we will give a brief overview of the neural network architecture that we use, the U-Net, and how we go about training it. You can also have a look at my talk at 2020's Applied Machine Learning Days in Lausanne, Switzerland:

How Google Cloud facilitates Machine Learning projects

Johan Dettmar

Since not only the complexity of Machine Learning (ML) models but also the size of data sets continue to grow, so does the need for computer power. While most laptops today can handle a significant workload, the performance is often simply not enough for our purposes at dida. In the following article, we walk you through some of the most common bottlenecks and show how cloud services can help to speed things up.

Data Augmentation with GANs for Defect Detection

Lorenzo Melchior

In Machine Learning, an insufficient amount of training data often hinders the performance of classification algorithms. Experience shows that shortage of training data is rather the rule than the exception, which is why people have come up with clever data augmentation methods. In this blog post I demonstrate how you can create new images of a distribution of images with a Generative Adversarial Network ( GAN ). This can be applied as a data augmentation method for problems such as defect detection in industrial production.

Pattern Recognition in Medical Imaging

Matthias Werner

Artificial intelligence (AI) and in particular computer vision promise to be valuable aids for diagnosing diseases based on medical imaging techniques . For humans, it takes years of academic and on-the-job training to e.g. perform medical diagnosis from X-ray images. As we will see, it is also quite a challenge for intelligent algorithms. At this year's KIS-RIS-PACS and DICOM convention organized by the Department of Medicine at the University of Mainz, Germany, researchers from radiology and adjacent fields gathered to discuss the state-of-the-art of AI in their field. Philipp Jackmuth from dida was the speaker of choice for this topic and here we will discuss key points of his talk.

What is Natural Language Processing (NLP)?

Fabian Gringel

Natural Language Processing (short: NLP , sometimes also called Computational Linguistics ) is one of the fields which has undergone a revolution since methods from Machine Learning (ML) have been applied to it. In this blog post I will explain what NLP is about and show how Machine Learning comes into play. In the end you will have learned which problems NLP deals with, what kinds of methods it uses and how Machine Learning models can be adapted to the specific structure of natural language data.

Semantic segmentation of satellite images

Nelson Martins (PhD)

This post presents some key learnings from our project on identifying roofs on satellite images . Our aim was to develop a planing tool for the placement of solar panels on roofs. For this purpose we set up a machine learning model that accurately partitions those images into different types of roof parts and background. We learned that the UNet model with dice loss enforced with a pixel weighting strategy outperforms cross entropy based loss functions by a significant margin in semantic segmentation of satellite images. The following idealized pipeline illustrates the functionality of the planning tool:

Extracting information from documents

Frank Weilandt (PhD)

There is a growing demand for automatically processing letters and other documents. Powered by machine learning, modern OCR (optical character recognition) methods can digitize the text. But the next step consists of interpreting it. This requires approaches from fields such as information extraction and NLP (natural language processing) . Here we go through some heuristics how to read the date of a letter automatically using the Python OCR tool pytesseract . Hopefully, you can adapt some ideas to your own project.