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About machine learning, its practical applications and some theoretical considerations

Featured

March 14th

2023

Leveraging Machine Learning for Environmental Protection

Written by Edit Szügyi

Protecting the natural environment is arguably the biggest challenge of our generation. I show how and where Machine Learning can help.

January 13th

2023

Managing layered requirements with pip-tools

September 26th

2022

An Introduction to Metric Learning

August 29th

2022

Recommendation systems - an overview

August 2nd

2022

The best (Python) tools for remote sensing

Computer Vision

Teaching Machines to see

Introductions

Leveraging Machine Learning for Environmental Protection

By Edit Szügyi • March 14th, 2023

Protecting the natural environment is arguably the biggest challenge of our generation. I show how and where Machine Learning can help.
Computer Vision

An Introduction to Metric Learning

By William Clemens (PhD) • September 26th, 2022

Introduction to metric learning: Expand your knowledge of metric learning, common distance measures & loss functions such as the triplet loss.
Introductions

Recommendation systems - an overview

By Konrad Mundinger • August 29th, 2022

Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.
Computer Vision

The best (Python) tools for remote sensing

By Emilius Richter • August 2nd, 2022

Python tools for remote sensing using machine learning: Comparison of Python software for data retrieval and processing of satellite data read here.
Computer Vision

Image Captioning with Attention

By Madina Kasymova • May 31st, 2022

An application of deep learning: Read here about image labeling algorithms & an approach to image labeling - the attention mechanism.
Computer Vision

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

By Felix Brunner • March 21st, 2022

Read here how to get a labeling tool for image classification working in a Jupyter notebook & what options there are for extending it.
Computer Vision

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

By David Berscheid • October 6th, 2021

Read here about model- and data-centric machine learning & how we at dida improve machine learning projects by using data-centric techniques.
Computer Vision

Classification of Crop Fields through Satellite Image Time Series

By Tiago Sanona • August 19th, 2021

Read here how machine learning is applied in remote sensing & how grain fields are classified from time series of satellite images.
Computer Vision

Extracting information from technical drawings

By Frank Weilandt (PhD) • July 16th, 2021

Read here about deep learning based computer vision techniques for information extraction from .xploded-view technical drawings-
Computer Vision

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

By Konrad Mundinger • July 5th, 2021

Visual Transformers: Learn the application of an NLP architecture within computer vision & what it means to tokenize an image.
Computer Vision

CLIP: Mining the treasure trove of unlabeled image data

By Fabian Gringel • June 21st, 2021

Contrastive language image pretraining (CLIP): Read about the functionality as well as applications of the CLIP model, a zero-shot image classifier.
Computer Vision

Migrating labels from Planet Scope to Sentinel-2

By Migrating labels from Planet Scope to Sentinel-2 • June 4th, 2021

In this blog article, I want to briefly describe the process of migrating labels from Planet Scope to Sentinel-2 images.
Projects

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

By Emilius Richter • May 21st, 2021

Read about the 21 relevant questions that should be considered & answered upfront to start a successful machine learning software project.
Tools

The best image labeling tools for Computer Vision

By The best image labeling tools for Computer Vision • April 8th, 2021

Read here about the best image annotation tools for computer vision tasks. Everything from the installation to illustration with an example.
Natural Language Processing

dida's tech stack

By Fabian Gringel • March 29th, 2021

This blog article provides an overview of our tech stack at dida. I will describe the tools that shape our software development process and our favourite Python libraries for machine and deep learning.
Computer Vision

Using satellite imagery for greenfield exploration

By Fabian Dechent • March 19th, 2021

Learn more about opportunities offered by remote sensing using satellite-based imaging & machine learning for greenfield exploration.
Tools

Deploying software with Docker containers

By Fabian Gringel • February 26th, 2021

Read this blog article about building a Python application with a web API using Docker containers for software deployment.
Computer Vision

Understanding graph neural networks by way of convolutional nets

By Augusto Stoffel (PhD) • February 5th, 2021

Understand graph neural networks (GNNs) by analogy to convolutional neural networks (CNNs) & learn more about the application of GNNs here.
Computer Vision

Understanding and converting MGRS coordinates in Python

By Tiago Sanona • January 18th, 2021

Learn here how MGRS (and UTM) work and how to use Python's MGRS library to convert MGRS coordinates to latitude/longitude.
Computer Vision

Monitoring urban development from space

By Johan Dettmar • October 12th, 2020

An application of machine learning: Read about monitoring urban development of cities using satellite imagery & computer vision here.
Computer Vision

Detecting illegal mines from space

By Detecting illegal mines from space • September 1st, 2020

Read about a U-net for semantic segmentation. An approach to illegal mine detection based on deep learning (DL) and remote sensing here.
Introductions

What is Reinforcement Learning? (Part 2)

By What is Reinforcement Learning? (Part 2) • August 3rd, 2020

Learn more about multi-agent reinforcement learning (MARL), its capabilities, limitations, and how MARL can be viewed as a problem solver.
Computer Vision

Pretraining for Remote Sensing

By William Clemens (PhD) • May 11th, 2020

Learn more about the benefits of pretraining a machine learning model in preparation for real training, using remote sensing as an example.
Introductions

What is Reinforcement Learning? (Part 1)

By What is Reinforcement Learning? (Part 1) • April 27th, 2020

Read about a branch of machine learning (ML) called reinforcement learning (RL) in this article. Reinforcement learning explanation & example here.
Introductions

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

By Lorenzo Melchior • April 14th, 2020

Read this blog article about the different results of a machine learning algorithm trained with unlabeled & labeled data.
Computer Vision

How to recognise objects in videos with PyTorch

By William Clemens (PhD) • March 16th, 2020

Read this blog entry about applying a pre-trained PyTorch model to a YouTube video to illustrate object detection with Python.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 2)

By What is Bayesian Linear Regression? (Part 2) • March 4th, 2020

Learn more about Bayes' theorem, its application & about the mathematics behind Bayesian Linear Regression in this article.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 1)

By What is Bayesian Linear Regression? (Part 1) • February 17th, 2020

Read the professional explanation of Bayesian linear regression (BLR) with an example & learn more about Bayes' theorem here.
Theory & Algorithms

Beat Tracking with Deep Neural Networks

By Beat Tracking with Deep Neural Networks • January 31st, 2020

Read here about the application of a machine learning model for time series analysis using the example of a beat tracking algorithm based on a TCN.
Theory & Algorithms

Machine Learning Approaches for Time Series

By Machine Learning Approaches for Time Series • December 18th, 2019

Expand your machine learning knowledge of time series, serial sequences & recurrent neural networks (RNN) with this professional introduction.
Software Development

How to distribute a Tensorflow model as a JavaScript web app

By Johan Dettmar • December 2nd, 2019

Learn here how to train a TensorFlow model in Python & then deploy it as a JavaScript web app, using handwriting recognition as an example.
Computer Vision

Detecting clouds in satellite images using convolutional neural networks

By William Clemens (PhD) • November 11th, 2019

Learn how to apply ConvNets and use machine learning to detect convective clouds from satellite data using convolutional neural networks here.
Tools

How Google Cloud facilitates Machine Learning projects

By Johan Dettmar • October 25th, 2019

Read here how Google Cloud services can accelerate machine learning projects and learn more about the phases of machine learning at dida.
Computer Vision

Data Augmentation with GANs for Defect Detection

By Lorenzo Melchior • September 24th, 2019

In Machine Learning, an insufficient amount of training data often hinders the performance of classification algorithms. In this blog post I demonstrate how you can create new images of a distribution of images using a Generative Adversarial Network (GAN).
Introductions

Pattern Recognition in Medical Imaging

By Pattern Recognition in Medical Imaging • August 27th, 2019

Learn how machine learning algorithms such as Convolutional Neural Networks (CNN) are applied in pattern recognition of medical images.
Introductions

What is AI?

By What is AI? • July 15th, 2019

Read the explanation of artificial intelligence (AI), machine learning (ML) & deep learning terms and their use in computer vision here.
Computer Vision

Semantic segmentation of satellite images

By Nelson Martins (PhD) • May 24th, 2019

The creation of the machine learning model for semantic segmentation of satellite images has produced interesting results, which you can read here.

Natural Language Processing

Making sense of text, speech and structured documents

Introductions

Recommendation systems - an overview

By Konrad Mundinger • August 29th, 2022

Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.
Computer Vision

Image Captioning with Attention

By Madina Kasymova • May 31st, 2022

An application of deep learning: Read here about image labeling algorithms & an approach to image labeling - the attention mechanism.
Natural Language Processing

OpenAI Codex: Why the revolution is still missing

By Fabian Gringel • February 18th, 2022

Learn more about how OpenAI's codex language model works and how it differs from GPT-3. Codex software explanation and experiences here.
Introductions

Ethics in Natural Language Processing

By Marty Oelschläger (PhD) • December 20th, 2021

Learn more about the ethics in natural language processing (NLP), the societal impact of machine learning (ML) & why caution should be exercised.
Natural Language Processing

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

By Fabian Gringel • October 24th, 2021

Expand your knowledge about GPT-3 and read here about opportunities, weaknesses & troubleshooting as well as alternatives of the AI-based language model.
Computer Vision

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

By David Berscheid • October 6th, 2021

Read here about model- and data-centric machine learning & how we at dida improve machine learning projects by using data-centric techniques.
Natural Language Processing

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

By Fabian Gringel • September 27th, 2021

Read here about how GPT-3 works, as well as its dangers & applications, and learn how you can try a GPT-3-like model for free.
Computer Vision

CLIP: Mining the treasure trove of unlabeled image data

By Fabian Gringel • June 21st, 2021

Contrastive language image pretraining (CLIP): Read about the functionality as well as applications of the CLIP model, a zero-shot image classifier.
Projects

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

By Emilius Richter • May 21st, 2021

Read about the 21 relevant questions that should be considered & answered upfront to start a successful machine learning software project.
Introductions

Enhancing Search with Question Answering

By Angela Maennel • April 26th, 2021

Read here about how open domain question answering systems work & how they can improve your website's search function.
Natural Language Processing

dida's tech stack

By Fabian Gringel • March 29th, 2021

This blog article provides an overview of our tech stack at dida. I will describe the tools that shape our software development process and our favourite Python libraries for machine and deep learning.
Tools

Deploying software with Docker containers

By Fabian Gringel • February 26th, 2021

Read this blog article about building a Python application with a web API using Docker containers for software deployment.
Computer Vision

Understanding graph neural networks by way of convolutional nets

By Augusto Stoffel (PhD) • February 5th, 2021

Understand graph neural networks (GNNs) by analogy to convolutional neural networks (CNNs) & learn more about the application of GNNs here.
Natural Language Processing

How to identify duplicate files with Python

By Ewelina Fiebig • September 28th, 2020

Learn how to identify duplicate files with Python and then delete them automatically. To find the duplicates, filecmp is used.
Natural Language Processing

How to extract text from PDF files

By How to extract text from PDF files • August 17th, 2020

Comparison of the open source Python PDF text extraction tools PyPDF2, pdfminer & PyMuPDF. Read about tools for extracting text from PDF files here.
Natural Language Processing

BERT for question answering (Part 1)

By Mattes Mollenhauer (PhD) • July 22nd, 2020

BERT, a deep learning model for language representation: Read this article to learn more about how & how to use the BERT model.
Natural Language Processing

BERT for question answering (Part 2)

By Mattes Mollenhauer (PhD) • July 2nd, 2020

Expand your knowledge of natural language processing (NLP) with an application example of the BERT model: automated question answering in biomedicine.
Tools

The best free labeling tools for text annotation in NLP

By Fabian Gringel • March 30th, 2020

Find out about the best free labeling tools for natural language processing (NLP) text annotation, installation & configuration.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 2)

By What is Bayesian Linear Regression? (Part 2) • March 4th, 2020

Learn more about Bayes' theorem, its application & about the mathematics behind Bayesian Linear Regression in this article.
Natural Language Processing

Digital public administration: intuitive online access through AI

By Jona Welsch • March 2nd, 2020

The following article describes how AI can help to establish digital governance services, using the example of business registrations and the AI model "BERT".
Theory & Algorithms

What is Bayesian Linear Regression? (Part 1)

By What is Bayesian Linear Regression? (Part 1) • February 17th, 2020

Read the professional explanation of Bayesian linear regression (BLR) with an example & learn more about Bayes' theorem here.
Theory & Algorithms

Beat Tracking with Deep Neural Networks

By Beat Tracking with Deep Neural Networks • January 31st, 2020

Read here about the application of a machine learning model for time series analysis using the example of a beat tracking algorithm based on a TCN.
Tools

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

By Fabian Gringel • January 20th, 2020

Read about the comparison of OCR tools for text recognition and find out which the best text recognition software for your project is.
Theory & Algorithms

Temporal convolutional networks for sequence modeling

By Temporal convolutional networks for sequence modeling • January 6th, 2020

Learn more about temporal convolutional networks, a convolutional approach to sequences: Model explanation, structure & implementations of TCNs here.
Theory & Algorithms

Machine Learning Approaches for Time Series

By Machine Learning Approaches for Time Series • December 18th, 2019

Expand your machine learning knowledge of time series, serial sequences & recurrent neural networks (RNN) with this professional introduction.
Software Development

How to distribute a Tensorflow model as a JavaScript web app

By Johan Dettmar • December 2nd, 2019

Learn here how to train a TensorFlow model in Python & then deploy it as a JavaScript web app, using handwriting recognition as an example.
Tools

How Google Cloud facilitates Machine Learning projects

By Johan Dettmar • October 25th, 2019

Read here how Google Cloud services can accelerate machine learning projects and learn more about the phases of machine learning at dida.
Introductions

What is Natural Language Processing (NLP)?

By Fabian Gringel • August 12th, 2019

Professional explanation of natural language processing. Read here about application areas & methods of NLP as well as the connection to Machine Learning.
Introductions

What is AI?

By What is AI? • July 15th, 2019

Read the explanation of artificial intelligence (AI), machine learning (ML) & deep learning terms and their use in computer vision here.
Natural Language Processing

Extracting information from documents

By Frank Weilandt (PhD) • April 23rd, 2019

Learn how extracting information from documents, also called information extraction, becomes a breeze when using a Python OCR tool.

Introductions

Learn about machine learning from scratch

Introductions

Leveraging Machine Learning for Environmental Protection

By Edit Szügyi • March 14th, 2023

Protecting the natural environment is arguably the biggest challenge of our generation. I show how and where Machine Learning can help.
Introductions

Managing layered requirements with pip-tools

By Augusto Stoffel (PhD) • January 13th, 2023

Learn more about pip tools here, especially how to manage layered requirements using Python & pip tools.
Introductions

Collaborative Filtering in Recommender Systems

By Konrad Mundinger • November 21st, 2022

Read this article for an explanation of collaborative filtering as well as Python code for various collaborative filtering techniques.
Computer Vision

An Introduction to Metric Learning

By William Clemens (PhD) • September 26th, 2022

Introduction to metric learning: Expand your knowledge of metric learning, common distance measures & loss functions such as the triplet loss.
Introductions

Recommendation systems - an overview

By Konrad Mundinger • August 29th, 2022

Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.
Introductions

Project proposals - the first step to a successful ML project

By Emilius Richter • July 18th, 2022

For a software provider, the project proposal is the first step toward meeting the needs of the customer. In this article, I will describe the most important modules in machine learning project proposals.
Natural Language Processing

OpenAI Codex: Why the revolution is still missing

By Fabian Gringel • February 18th, 2022

Learn more about how OpenAI's codex language model works and how it differs from GPT-3. Codex software explanation and experiences here.
Introductions

Ethics in Natural Language Processing

By Marty Oelschläger (PhD) • December 20th, 2021

Learn more about the ethics in natural language processing (NLP), the societal impact of machine learning (ML) & why caution should be exercised.
Natural Language Processing

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

By Fabian Gringel • October 24th, 2021

Expand your knowledge about GPT-3 and read here about opportunities, weaknesses & troubleshooting as well as alternatives of the AI-based language model.
Natural Language Processing

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

By Fabian Gringel • September 27th, 2021

Read here about how GPT-3 works, as well as its dangers & applications, and learn how you can try a GPT-3-like model for free.
Computer Vision

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

By Konrad Mundinger • July 5th, 2021

Visual Transformers: Learn the application of an NLP architecture within computer vision & what it means to tokenize an image.
Projects

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

By Emilius Richter • May 21st, 2021

Read about the 21 relevant questions that should be considered & answered upfront to start a successful machine learning software project.
Introductions

Enhancing Search with Question Answering

By Angela Maennel • April 26th, 2021

Read here about how open domain question answering systems work & how they can improve your website's search function.
Computer Vision

Using satellite imagery for greenfield exploration

By Fabian Dechent • March 19th, 2021

Learn more about opportunities offered by remote sensing using satellite-based imaging & machine learning for greenfield exploration.
Introductions

What is Reinforcement Learning? (Part 2)

By What is Reinforcement Learning? (Part 2) • August 3rd, 2020

Learn more about multi-agent reinforcement learning (MARL), its capabilities, limitations, and how MARL can be viewed as a problem solver.
Natural Language Processing

BERT for question answering (Part 1)

By Mattes Mollenhauer (PhD) • July 22nd, 2020

BERT, a deep learning model for language representation: Read this article to learn more about how & how to use the BERT model.
Natural Language Processing

BERT for question answering (Part 2)

By Mattes Mollenhauer (PhD) • July 2nd, 2020

Expand your knowledge of natural language processing (NLP) with an application example of the BERT model: automated question answering in biomedicine.
Introductions

What is Reinforcement Learning? (Part 1)

By What is Reinforcement Learning? (Part 1) • April 27th, 2020

Read about a branch of machine learning (ML) called reinforcement learning (RL) in this article. Reinforcement learning explanation & example here.
Introductions

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

By Lorenzo Melchior • April 14th, 2020

Read this blog article about the different results of a machine learning algorithm trained with unlabeled & labeled data.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 2)

By What is Bayesian Linear Regression? (Part 2) • March 4th, 2020

Learn more about Bayes' theorem, its application & about the mathematics behind Bayesian Linear Regression in this article.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 1)

By What is Bayesian Linear Regression? (Part 1) • February 17th, 2020

Read the professional explanation of Bayesian linear regression (BLR) with an example & learn more about Bayes' theorem here.
Introductions

Pattern Recognition in Medical Imaging

By Pattern Recognition in Medical Imaging • August 27th, 2019

Learn how machine learning algorithms such as Convolutional Neural Networks (CNN) are applied in pattern recognition of medical images.
Introductions

What is Natural Language Processing (NLP)?

By Fabian Gringel • August 12th, 2019

Professional explanation of natural language processing. Read here about application areas & methods of NLP as well as the connection to Machine Learning.
Introductions

What is AI?

By What is AI? • July 15th, 2019

Read the explanation of artificial intelligence (AI), machine learning (ML) & deep learning terms and their use in computer vision here.

Software Development

Everything related to coding

Introductions

Managing layered requirements with pip-tools

By Augusto Stoffel (PhD) • January 13th, 2023

Learn more about pip tools here, especially how to manage layered requirements using Python & pip tools.
Introductions

Project proposals - the first step to a successful ML project

By Emilius Richter • July 18th, 2022

For a software provider, the project proposal is the first step toward meeting the needs of the customer. In this article, I will describe the most important modules in machine learning project proposals.
Computer Vision

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

By David Berscheid • October 6th, 2021

Read here about model- and data-centric machine learning & how we at dida improve machine learning projects by using data-centric techniques.
Natural Language Processing

dida's tech stack

By Fabian Gringel • March 29th, 2021

This blog article provides an overview of our tech stack at dida. I will describe the tools that shape our software development process and our favourite Python libraries for machine and deep learning.
Tools

Deploying software with Docker containers

By Fabian Gringel • February 26th, 2021

Read this blog article about building a Python application with a web API using Docker containers for software deployment.
Software Development

How to distribute a Tensorflow model as a JavaScript web app

By Johan Dettmar • December 2nd, 2019

Learn here how to train a TensorFlow model in Python & then deploy it as a JavaScript web app, using handwriting recognition as an example.
Tools

How Google Cloud facilitates Machine Learning projects

By Johan Dettmar • October 25th, 2019

Read here how Google Cloud services can accelerate machine learning projects and learn more about the phases of machine learning at dida.

Theory & Algorithms

For those not afraid of formulas

Introductions

Collaborative Filtering in Recommender Systems

By Konrad Mundinger • November 21st, 2022

Read this article for an explanation of collaborative filtering as well as Python code for various collaborative filtering techniques.
Computer Vision

An Introduction to Metric Learning

By William Clemens (PhD) • September 26th, 2022

Introduction to metric learning: Expand your knowledge of metric learning, common distance measures & loss functions such as the triplet loss.
Introductions

Recommendation systems - an overview

By Konrad Mundinger • August 29th, 2022

Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.
Natural Language Processing

OpenAI Codex: Why the revolution is still missing

By Fabian Gringel • February 18th, 2022

Learn more about how OpenAI's codex language model works and how it differs from GPT-3. Codex software explanation and experiences here.
Natural Language Processing

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

By Fabian Gringel • October 24th, 2021

Expand your knowledge about GPT-3 and read here about opportunities, weaknesses & troubleshooting as well as alternatives of the AI-based language model.
Computer Vision

Classification of Crop Fields through Satellite Image Time Series

By Tiago Sanona • August 19th, 2021

Read here how machine learning is applied in remote sensing & how grain fields are classified from time series of satellite images.
Computer Vision

Extracting information from technical drawings

By Frank Weilandt (PhD) • July 16th, 2021

Read here about deep learning based computer vision techniques for information extraction from .xploded-view technical drawings-
Computer Vision

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

By Konrad Mundinger • July 5th, 2021

Visual Transformers: Learn the application of an NLP architecture within computer vision & what it means to tokenize an image.
Computer Vision

CLIP: Mining the treasure trove of unlabeled image data

By Fabian Gringel • June 21st, 2021

Contrastive language image pretraining (CLIP): Read about the functionality as well as applications of the CLIP model, a zero-shot image classifier.
Computer Vision

Understanding graph neural networks by way of convolutional nets

By Augusto Stoffel (PhD) • February 5th, 2021

Understand graph neural networks (GNNs) by analogy to convolutional neural networks (CNNs) & learn more about the application of GNNs here.
Introductions

What is Reinforcement Learning? (Part 2)

By What is Reinforcement Learning? (Part 2) • August 3rd, 2020

Learn more about multi-agent reinforcement learning (MARL), its capabilities, limitations, and how MARL can be viewed as a problem solver.
Natural Language Processing

BERT for question answering (Part 1)

By Mattes Mollenhauer (PhD) • July 22nd, 2020

BERT, a deep learning model for language representation: Read this article to learn more about how & how to use the BERT model.
Natural Language Processing

BERT for question answering (Part 2)

By Mattes Mollenhauer (PhD) • July 2nd, 2020

Expand your knowledge of natural language processing (NLP) with an application example of the BERT model: automated question answering in biomedicine.
Introductions

What is Reinforcement Learning? (Part 1)

By What is Reinforcement Learning? (Part 1) • April 27th, 2020

Read about a branch of machine learning (ML) called reinforcement learning (RL) in this article. Reinforcement learning explanation & example here.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 2)

By What is Bayesian Linear Regression? (Part 2) • March 4th, 2020

Learn more about Bayes' theorem, its application & about the mathematics behind Bayesian Linear Regression in this article.
Theory & Algorithms

What is Bayesian Linear Regression? (Part 1)

By What is Bayesian Linear Regression? (Part 1) • February 17th, 2020

Read the professional explanation of Bayesian linear regression (BLR) with an example & learn more about Bayes' theorem here.
Theory & Algorithms

Beat Tracking with Deep Neural Networks

By Beat Tracking with Deep Neural Networks • January 31st, 2020

Read here about the application of a machine learning model for time series analysis using the example of a beat tracking algorithm based on a TCN.
Theory & Algorithms

Temporal convolutional networks for sequence modeling

By Temporal convolutional networks for sequence modeling • January 6th, 2020

Learn more about temporal convolutional networks, a convolutional approach to sequences: Model explanation, structure & implementations of TCNs here.
Theory & Algorithms

Machine Learning Approaches for Time Series

By Machine Learning Approaches for Time Series • December 18th, 2019

Expand your machine learning knowledge of time series, serial sequences & recurrent neural networks (RNN) with this professional introduction.

Tools

Useful tools for facilitating machine learning projects

Introductions

Managing layered requirements with pip-tools

By Augusto Stoffel (PhD) • January 13th, 2023

Learn more about pip tools here, especially how to manage layered requirements using Python & pip tools.
Computer Vision

The best (Python) tools for remote sensing

By Emilius Richter • August 2nd, 2022

Python tools for remote sensing using machine learning: Comparison of Python software for data retrieval and processing of satellite data read here.
Computer Vision

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

By Felix Brunner • March 21st, 2022

Read here how to get a labeling tool for image classification working in a Jupyter notebook & what options there are for extending it.
Tools

The best image labeling tools for Computer Vision

By The best image labeling tools for Computer Vision • April 8th, 2021

Read here about the best image annotation tools for computer vision tasks. Everything from the installation to illustration with an example.
Natural Language Processing

dida's tech stack

By Fabian Gringel • March 29th, 2021

This blog article provides an overview of our tech stack at dida. I will describe the tools that shape our software development process and our favourite Python libraries for machine and deep learning.
Tools

Deploying software with Docker containers

By Fabian Gringel • February 26th, 2021

Read this blog article about building a Python application with a web API using Docker containers for software deployment.
Computer Vision

Understanding and converting MGRS coordinates in Python

By Tiago Sanona • January 18th, 2021

Learn here how MGRS (and UTM) work and how to use Python's MGRS library to convert MGRS coordinates to latitude/longitude.
Natural Language Processing

How to identify duplicate files with Python

By Ewelina Fiebig • September 28th, 2020

Learn how to identify duplicate files with Python and then delete them automatically. To find the duplicates, filecmp is used.
Natural Language Processing

How to extract text from PDF files

By How to extract text from PDF files • August 17th, 2020

Comparison of the open source Python PDF text extraction tools PyPDF2, pdfminer & PyMuPDF. Read about tools for extracting text from PDF files here.
Tools

The best free labeling tools for text annotation in NLP

By Fabian Gringel • March 30th, 2020

Find out about the best free labeling tools for natural language processing (NLP) text annotation, installation & configuration.
Tools

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

By Fabian Gringel • January 20th, 2020

Read about the comparison of OCR tools for text recognition and find out which the best text recognition software for your project is.
Tools

How Google Cloud facilitates Machine Learning projects

By Johan Dettmar • October 25th, 2019

Read here how Google Cloud services can accelerate machine learning projects and learn more about the phases of machine learning at dida.