Googlielmo's blog

  Faker is an Open Source Python package that generates synthetic data that could be used for many things such as populating a database, do load testing or anonymize production data for development or ML purposes. Generating fully random data isn't a good choice: with Faker you can drive the generation process and tailor the generated data to your specific needs: this is the greatest value provided by Faker. This package comes with 23 built-in data providers, some other providers are available from the community. The available data providers cover majority of data types and cases, but it is possible any way make the generated data more meaningful by implementing a custom provider. Faker supports Python 3.6+ and it is available for installation through PyPI or Anaconda.  Here's a code example that shows how to implement a custom provider to generate synthetic data following the structure and constraints as for this Kaggle  dataset related to a restaurant data with consumer...

 In my career I have noticed that often organizations are reluctant on providing Engineering teams with licenses for software to draw diagrams. In the best case scenarios MS Visio is usually the only option available, which isn't the best experience when trying to draw modern software architectures. Several online options are available, but they require to share project details that cannot leave your organization network, so they couldn't be taken into account often. Also, while treating everything as code, it would be nice to have also diagrams as code. All these needs can be satisfied by adopting Diagrams . It is an Open Source Python package that allows you draw cloud system architecture diagrams programmatically and then put them under version control, (as at the end they are regular Python files). It supports cloud (AWS, Azure, GCP, Alibaba, Oracle) and on-prem system architecture diagrams. The Diagrams nodes include also Kubernetes, programming languages and frameworks. ...

 Photo by Dinu J Nair on Unsplash Today I want to introduce  TagUI , an RPA (Robotic Process Automation) Open Source tool I am using to automate test scenarios for web applications. It is developed and maintained by the AI Singapore national programme. It allows writing flows to automate repetitive tasks, such as regression testing of web applications. Flows are written in natural language : English and other 20 languages are currently supported. Works on Windows, Linux and macOS. The TagUI official documentation can be found  here . The tool doesn't require installation: just go the official GitHub repository and download the archive for your specific OS (ZIP for Windows, tar.gz for Linux or macOS). After the download is completed, unpack its content in the local hard drive. The executable to use is named  tagui  (.cmd in Windows, .sh for other OS) and it is located into the  <destination_folder>/tagui/src  directory. In order to ...

After a 7 months hiatus I have decided to go back posting on this blog. Lot of things happened across 2020 and 2021 that left me with little or no time at all to share my thoughts and findings. In this long period of time I have been involved in challenging ML/AI projects, managing them and interacting with people 100% remotely because of the COVID-10 pandemic, had a chance to experiment with many and in some cases successfully applications of new DL architectures and Python Open Source libraries, but also tune mine and my family personal life among all the style changes imposed by the pandemic. The reasons that led me to restart the blog are mostly the following: I have accumulated tons of technical topics that are worth to share with a wider audience. During the past months I have shared some through social networks such as LinkedIn and Twitter or in few virtual meetups or conferences, but they need more deep dive. This week I gave a workshop at the ODSC Europe 2021 conference and I...

 I am back to the blog after some months where I have been pretty busy with interesting and challenging projects and coping with the new "normal" of the COVID-19 era.  Lot of things happened and I have several stuff to share in the upcoming months. Today I am going to speak at the Big Things 2020, the Data and AI conference. This year it moved virtual. Registration is still open and for free. I hope you have a chance to attend my talk at 7:45 PM GMT+1. I am going to discuss about Adversarial Attacks to Computer Vision systems and mitigation strategies. I hope to meet you there and do also some networking in the dedicated chat area. Follow-ups for this topic will be shared in this blog in the upcoming weeks.

The Spark + AI Summit North America edition 2020 is going virtual and access to keynotes, sessions and virtual events is for free. You have to pay only to attend pre-conference and conference training, AMA sessions, VIP sessions and certification exams. The Summit will happen from June 22nd to 26th 2020. This year's keynote speakers include Francois Chollet, the creator of Keras . All details in the official website: https://databricks.com/sparkaisummit/north-america-2020

In part 1 of this series a definition of CBIR has been given. Let's now understand what's the typical flow for it. The diagram in figure 1 shows that there are two parts, one that happens offline and another which is online: Figure 1 Starting from an image storage, a preliminary trained Deep Neural Network is used to extract the features from images. Extracted features are then stored in a feature database. This happens offline any time new images are added to the storage. What happens online is the search process itself. Any time a user uploads a query image, the same Deep Neural Network used for feature extraction is used to extract the input image features. Then the distance from the query image features and the features in the database are computed. The closer the distance, the higher the relevance is. The closest features are then sorted and the corresponding images are returned as results. Basic implementation: data set preparation To make things more clear,...