Posts

Find your model's optimal hyperparameters with Hyperopt

While checking out some tools for automated hyperparameter optimization, I came across a quite popular library called Hyperopt. It provides an implementation for Random Search and Tree-of-Parzen-Estimators (TPE). Unfortunately, most examples out there us a dummy function to replace the model, but I could not find any example that uses TensorFlow. That's why I wanted to provide a basic simple Hyperopt example with TensorFlow. This example can be found on my Github.
Do you have any experiences with other libraries for hyperparameter optimization? I would be happy if you share your experiences? For instance, I have read that a Sacred extension called Labwatch also allows to define a search space for algorithmic hyperparameter optimization, but comes with different algorithms.

Better IDE support for Python with Type Hints

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About 12 years ago, I started to learn programming with Java and C#. Both languages are type-safe and have therefore a great support when using an IDE like Eclipse, IntelliJ or Visual Studio. But throughout my software development journey, I also made my hands dirty with other dynamically typed languages like JavaScript or Python. While checking out Angular2 more than a year ago, I used TypeScript for my first time, because it was recommended by the Angular team. I quickly realized how much painless it was to develop a web application with TypeScript instead of using classic JavaScript. The ability to strictly assigning a data type to a variable allows any IDE to offer much better tooling support, such as IntelliSense. At least in my point of view, programming in TypeScript felt much more like programming in C# than in JavaScript, where I always felt like I had to know every possible function name by heart. Which was especially difficult when you use JavaScript on a very non-regular …

Why should I install TensorFlow from Source?

There are various ways to install TensorFlow. For instance, you can install it using a Docker image or Python's package manager pip. But since the version 1.0 release of TensorFlow, you probably might have faced the following warnings each time you run a TensorFlow session:

2017-05-29 11:50:22.977500: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-29 11:50:22.977513: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-29 11:50:22.977517: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-29 11:50:22.977519: W tensorflow/co…

Python + Matplotlib = Must Have on Every System

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In the last few weeks, I had to visualize some data from time to time. And for me, it turned out that the Python library Matplotlib is one of the best tools to do some quick plots. I cannot imagine that I have never installed Python on the Windows partition of my laptop, but only on my Linux partition. And I can really recommend to have Python and Matplotlib installed on every device, so that you have these tools at hand whenever you need to visualize some data. In this short post, I would like to write down the few simple steps you should do...

1. Install Python from Python Software Foundations
Make sure you add python to your PATH, as well as select pip to be installed as well

2. Start your terminal, cmd or PowerShell

3. Install Matplotlib using the pip > pip install matplotlib

4. Start the python console
> python

5. Import matplotlib.pyplot and make a plot with just a few lines of code
> import matplotlib.pyplot as plt
> plt.plot([8,4,2,1,0,1,2,4,8])
> plt.show()

This results …

Deep Learning Meetup 2017-1 in Munich

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I could check out another deep learning meetup. This time it was hosted at Google's Isar Valley here in Munich. The three interesting walks were about the following topics:
Visual Sentiment Analysis with Deep Convolutional Neural Networks
(by Dr. Damien Borth, DFKI)Strategies for AI Deployment
(by Henrik Klagges, TNG Consulting GmbH)DGX-1 and SATURNV: The World’s Most Efficient Supercomputer for AI and Deep Learning
(by Ralph Hinsche, Nvidia) In the third talk of Nvidia, we were also able to hold a test sample of the latest Tesla P100 in our hands, which is one of the building blocks of Nvidia's deep learning super computers called DGX-1. This is a nice super toy that every AI-researcher would like to have under the Christmas tree. Unfortunately, a single device costs more than 100.000 US-Dollar.

Intel AI Days 2017

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I had the pleasure to check out Intel's first AI days in Europe. At ICM in Munich, Intel presented their latest advancements in Artificial Intelligence and Deep Learning in both hardware and software. As one of the biggest player in the hardware industry, they talked a lot about the next wave Xeon CPUs called Lake Crest, that is optimized for Deep Learning. Furthermore, a representative of Nervana Systems introduced their deep learning platform, which has been acquired by Intel for more than 400 Mio. US-Dollar in October 2016.

Additionally, they talked a lot about low-level optimizations that they have done in order to accelerate many deep learning using Intel hardware, such as Intel Math Kernel Library (MKL). In some examples, they shows amazing improvements by a factor of up to 400. This sounds to good to be true in my ears, but even half of that is more than welcome! They presented their Neon framework, which feels to be in between TensorFlow and Keras, as well as a high-level…

Universal App: PriceChecker

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Not sure whether the product you would like to purchase is an awesome deal or just another rip-off price? Well, my new PriceChecker app for Windows 10 might be the perfect match for you! Simply scan the barcode on the price tag and compare the consumer reviews and price on Amazon.

The app is free of change and contains no adverts. So, what are you waiting for? Check it out and download it from the Windows Store...