Written by Denis Krompass and Sigurd Spieckermann, founders of creaidAI

Walk-through using the AI Blueprint Engine to generate code for an end-to-end machine learning pipeline including data loading, pre-processing and a deep neural network for the titanic passenger survival prediction project.

In our previous articles we motivated GUI-driven design and subsequent code generation for Deep-Learning-based machine learning and introduced our AI Blueprint Engine — a purpose-built tool we are developing to address this task. In this article, we will use the first public beta release of the AI Blueprint Engine to exemplify its utility in the “Titanic: Machine Learning from DisasterKaggle competition. In this context, we will design a suitable neural network using the graphical user interface (GUI) that predicts the survival probability of a passenger on the Titanic conditioned on various features of heterogeneous data types. Subsequently, we will set up an execution environment from the generated project, train the model using the competition’s training set, and predict survival probabilities of passengers using the competition’s test set. Developing purpose-built solutions to complex real-world machine learning problems often requires customization whose diversity is difficult to adequately cover by a graphical higher level of abstraction. …

Written by Denis Krompass and Sigurd Spieckermann, founders of creaidAI

Today, we are releasing the public beta release of our AI Blueprint Engine which is a code generator for Deep-Learning-based machine learning. Previously, we motivated code generation for Deep Learning, which we invite you to read first before continuing with this article.

The scope of this article is to cover the current capabilities of the AI Blueprint Engine, to outline future improvements, and to preview upcoming features. …

Written by Denis Krompass and Sigurd Spieckermann, founders of creaidAI

Back in the days, …

… when we were using Theano to build machine learning models, we often faced implementation issues which were tedious and time-consuming to resolve. While having a clear idea of the implementation goal, symbolic programming and cryptic error messages of an incorrectly constructed computation graph significantly slowed down our progress. Tons of documentation, and as a last resort Theano’s source code itself, likely contained all the knowledge needed to get things to work, but this development mode is just ridiculously inefficient.

Ooops, something went wrong while running some Theano code. Happy debugging …

StackOverflow to the rescue! Unfortunately, the exact same problem rarely occurs twice, or it’s nontrivial to boil the problem down to the minimum to get to the bottom of it. As a result, we, and many others, spent too much time with trial and error and with integrating information from various sources to finally turn an idea into reality. …



Bridging the gap between ease of use and flexibility in artificial intelligence development — https://creaidAI.com

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