Blog - Articles around AI, ML, DL and more
Tools for Deep Learning, importance of machine learning, Java and artificial intelligence - these and much more are topics our blog is dedicated to. We encounter new challenges every day and want to share our experience and insights with you. On this overview page you can see teasers of all previous blog posts. Feel free to click through, browse our current series of topics on MLOps and get excited about the posts to come! If you have any feedback, experiences, or topic requests, please feel free to contact us - we look forward to hearing from you.
Git as a management tool for training data and experiments in ML
In this part of the series of articles on MLOps, we start with information that will be familiar to most of you: With the basics of Git. However, to give a different perspective on the well-known tool, these basics provide the basis to highlight the function and benefits of Git for machine learning (ML) and the difference in managing training data.
MLOps: Establishment and operation of an AI
With Machine Learning Operations (MLOps) we ensure that data is efficiently and strategically integrated into business processes through regular and automated training, thus contributing to increased revenue. The challenge is to establish and maintain these automated processes.
Types of Artificial Neural Networks
In our real-world example, we used a “feed-forward neural network” to recognise handwritten numbers. This is probably the most basic form of a NN. In reality, however, there are hundreds of types of mathematical formulas that are used – beyond addition and multiplication – to compute steps in a neural network, many different ways to arrange the layers, and many mathematical approaches to train the network.
Amazon DJL - a new DL framework for Java
Developers who wanted to explore neural networks and deep learning using the JVM, and especially Java, had little choice so far. Those who wanted to focus exclusively on Java could not get around DL4J until now. If it had to be the JVM, but not necessarily Java, the MXNet Scala Frontend was also an option. Finally, if a little Python didn’t scare you, you could try a hybrid solution, combining TensorFlow and Java just like we already explained in previous articles.
Text comprehension and automated text generation with NLP, NLU and NLG
So far, we have generally steered clear of the areas of text comprehension and text generation by ML in our practical examples for the basic understanding of AI. For good reason, we have focused primarily on two types of problems: classification of images and prediction of numerical values.
The five most common mistakes with Neural networks
AI and especially Neural Networks or Deep Learning have been the technological hype topic for some years now. However, since the subject is quite abstract – one could say it is uncharted territory for most people – we want to clear up some mistakes that we often encounter in our work.
What are Neural Networks and how do they work?
In our past articles we mainly covered the basics of current AI research and tried to shed some light on them in a way that is understandable for non-IT scientists. We are now proceeding to the probably “hottest” current AI topic: Neural Networks (NN).
Deep Java Learning - NDManager & NDArray
After our first presentation of Amazon’s new Deep Learning Framework for Java, DJL, we now want to introduce the basics of Deep Learning under Java with DJL step by step in a series of beginner posts. This is not about quickly copying code snippets, but about really understanding the framework and the concepts.
Deep Fakes - How to spot faked Images
A (fairly) new kind of neural networks, so-called Generative Adversarial Networks or GANs, are nowadays capable of generating deceptively real images of people that do not actually exist. These fake images are indistinguishable from real photos at first glance. Fortunately, you might still uncover them if you look closely – if you know what to look for!
Recap: ML Conference 2019 in Munich
On 17.06. another round of the semi annual ML Conference started in Munich. As usual, it started with a day-long workshop with joint live coding, giving the participants an approachable introduction into Machine Learning and Deep Learning.