Valerio Maggio

PyScript brings the full PyData stack in the browser, opening up to unprecedented use cases for interactive data-intensive applications. In this scenario, the web browser becomes a ubiquitous computing platform, operating within a (nearly) _zero-installation_ & _server-less_ environment. In this talk, we will explore how to create full-fledged interactive front-end machine learning applications using PyScript. We will dive into the the main features of the PyScript platform (e.g. _built-in Javascript integration_ and _local modules_ ), discussing _new_ data & design patterns (e.g. _loading heterogeneous data in the browser_), required to adapt and to overcome the limitations imposed by the new operating environment (i.e. the browser).

PyScript is the new open source platform that brings Python to web front-end applications. In fact, PyScript makes it possible to inject *standard* Python code into HTML, which is then _interpreted_ and _executed_ directly in the browser. And all that, with **no server-side** technology needed, and **no installation** required (_not even a local Python interpreter!, ed._) 🔮. But there's more! Thanks to its built-in integration with [`pyodide`](https://pyodide.org/en/stable/), PyScript brings the [full](https://pyodide.org/en/stable/usage/packages-in-pyodide.html) PyData stack into the browser, along with a native integration with the Javascript interpreter, then enabling full support for front-end interactivity. As a result, PyScript has the potential to radically change the way in which interactive data-driven web apps could be designed and developed: the seamless bi-directional integration of **Python** and **Javascript** is complemented by the full support to reliable numerical computation, enabled by the Python scientific ecosystem (e.g. `numpy` `scikit-learn`), using the browser as a ubiquitous virtual machine. In this talk, we will explore how PyScript enables the creation of full-fledged font-end _interactive machine learning_ (`ML`) apps using PyScript. Multiple examples of supervised and unsupervised ML apps will be presented, and analysed in details, in order to fully understand how PyScript works, and what key features are provided (e.g. _built-in Javascript integration_; _local modules_ ). Similarly, we will also discuss new_ data & design patterns (e.g. _loading heterogeneous data in the browser_; _multi-core vs multi-threading; _performance considerations_) which are required to adapt to the new _atypical_ environment in which we operate: the **browser**. No specific prior knowledge is required to attend the talk. Familiarity with Python programming, and the main `pydata` packages (i.e. `numpy`, `scikit-learn`, `Matplotlib` ) is desirable, along with a general understanding of how the web DOM works (for the Javascript interaction part) and basic principles of data processing. **Domain** knowledge: _Novice_; **Python** knowledge: _Intermediate_

In this tutorial we will go through the main features of the `PyTorch` framework for Deep Learning. We will start by learning how to build a neural network from the ground up, deep diving into `torch.tensor`, `Dataset` and `optimisers`. We will analyse data cases from different domains (e.g. _numerical_, _images_), introducing different neural network layers and architecture. Last but not least, a few tips from a pure Data science-y perspective will be shared, to appreciate the wonderful integration PyTorch has with the Python Data model!

In this tutorial we will go through the main features of the `PyTorch` framework for Deep Learning. We will start by learning how to build a neural network from the ground up, deep diving into `torch.tensor`, `Dataset` and `optimisers`. We will analyse data cases from different domains (e.g. _numerical_, _images_), introducing different neural network layers and architecture. Last but not least, a few tips from a pure Data science-y perspective will be shared, to appreciate the wonderful integration PyTorch has with the Python Data model!