PyCon DE 2018

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The annual PyCon in Germany. This year it was in Karlsrurh. It was a large well organized, well funded, and well run conference with lots of great talks.

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1. Measuring the hay in the haystack: quantifying hidden variables using Bayesian Inference
2. Strongly typed datasets in a weakly typed world
3. Cloud chat bot for lazy people
4. Driving simulation and data analysis of magnetic nanostructures through Jupyter Notebook
5. Germany's next topic model
6. Active Learning - Building Semi-supervised Classifiers when Labeled Data is not Available
7. Reproducibility, and Selection Bias in Machine Learning
8. Testing in Python - The Big Picture
9. Beyond Jupyter Notebooks - Building your own Data Science platform with Python & Docker
10. PyLadies - Jessica Greene and PyLadies Berlin
11. Python Dependency Management
12. Python on the blockchain: Triumphs and tribulations in a crypto startup
13. Creating an inclusive corporate culture
14. Put your data on a map
15. Case Study in Travel Business - Understanding agent connections using NetworkX
16. Python Birdies: Codegolfing for better understanding (and fun) Codegolfing means taking a programming task and trying to answer it witha byte-minimal correct solution
17. Jupyter Notebook Best Practices
18. Python And PostgreSQL
19. Building your own conversational AI with open source tools
20. Keynote: Learning programming & science with Scientific Python
21. Stretchy - NoSQL Database behind REST API
22. Script, Library, or Executable: You can have it all!
23. Experiences from applying Convolutional Neural Networks for classifying 2D sensor data
24. How to make your (digital) Communication strong & future ready
25. ZODB: The Graph Database for PythonDevelopers
26. Python with and without Pants
27. Closing Session
28. Prophet Fon Time Series: Do You Use It?
29. Selinon - dynamic distributed task flows
30. Enabling the chip technologies of tomorrow – how Python helps us
31. Solving Data Science Problems using a Jupyter Notebook and SAP HANA's in-database Machine Learning Libraries
32. Your first NLP project: peaks and pitfalls of unstructured data
33. Deep Learning with PyTorch for more Fun and Profit (Part II)
34. Processing Geodata using Python
35. Introduction and practical experience about Quantum Computing using the Python libraries from IBM and Google
36. Salabim, Discrete Event Simulation In Python
37. From exploration to deployment - combining PyTorch and TensorFlow for Deep Learning
38. Concurrency in Python - concepts, frameworks and best practices
39. Prototyping to tested code
40. Interactive Visualization of Traffic Data using Bokeh
41. Let Me Take A Quick Look Into The Data
42. Pyccel, a Fortran static compiler for scientific High-Performance Computing
43. From Wittgenstein to TensorFlow: The role of Domain Specific Languages and Language Design in Machine Learning
44. Satellite data is for everyone: insights into modern remote sensing research with open data and Python
45. Bonobo, Airflow and Grafana to visualize your business
46. Keynote: Looking backward, looking forward
47. Advanced Analytics Today: From Open Source Integration to the Operationalization of the Analytic Lifecycle
48. Data science complexity and solutions in real industrial projects
49. Binder - lowering the bar to sharing interactive software
50. Achieving Resilient Code with Integration Tests
51. PyTorch as a scientific computing library: past, present and future
52. Microservices from the trenches: how we delivery fancy sofas across Europe
53. Developing ecommerce platform with Django Oscar
54. reticulate: R interface to Python
55. Satellite Image Segmentation Photovoltaic Potential Estimation
56. Where the heck is my memory?
57. How to teach space invaders to your computer
58. Keynote: Digital Cultural Techniques
59. Fulfilling Apache Arrow's Promises: Pandas on JVM memory without a copy
60. Introduction to Docker for Pythonistas
61. About going Open-Source
62. Build text classification models ( CBOW and Skip-gram) with FastText in python
63. How type annotations make your code better
64. Distributed Hyperparameter search with sklearn and kubernetes
65. Data Science meets Data Protection: Keeping your data secure while learning from it.
66. Machine Learning as a Service: How to deploy ML Models as APIs without going nuts
67. A Day Has Only 24±1 Hours: import pytz
68. Scalable Scientific Computing using Dask
69. Cython to speed up your Python code
70. Python Decorators: Gift or Poison?
71. What's new in Python 3.7?
72. Big Data Systems Performance: The Little Shop of Horrors
73. Productionizing your ML code seamlessly