Lightning Fabric is a new, open-source library that allows you to quickly and easily scale models while maintaining full control over your training loop. In the past, getting PyTorch code to run efficiently on GPUs and scaling it up to many machines and large datasets was possible with PyTorch Lightning. As time went on, however, we became aware of the need to provide a scaling option that landed somewhere between a raw deep learning framework like PyTorch on the one hand, and a high-level, feature-rich framework like PyTorch Lightning. Lightning Fabric is just that. While PyTorch Lightning provides many features to save time and improve readability and collaboration, there are complex use cases where full control over the training loop is needed.

In the first part of this series, we explored the capabilities of ChatGPT, a state-of-the-art language model developed by OpenAI, in assisting data scientists with tasks such as data cleaning, preprocessing, and code generation. In this second part, we will delve deeper into what ChatGPT generated and why it didn't work. We will discuss the specific challenges that come with using AI-generated code, and how to effectively address these issues to ensure the reliability and accuracy of the final product. Whether you're a data scientist or a developer, this post will provide valuable insights into how to use ChatGPT to improve your workflow and streamline your development process.

Reading code is one effective way to get professional in TensorFlow (TF). In this article, we reuse the examples in the TensorFlow tutorial but we keep them condense and take away codes that are for tracing or demonstration purposes. We also keep our discussion minimum so you can browse through as many examples as possible to get a complete picture. If you have problems to follow, in particular after reading the first example, please read the articles in this series first. Yet, in most examples, we keep all the boilerplate codes required by the examples.

Object-Oriented Programming or OOP can be a tough concept to understand for beginners. And that's mainly because it is not really explained in the right way in a lot of places. Normally a lot of books start by explaining OOP by talking about the three big terms -- Encapsulation, Inheritance and Polymorphism. But the time the book can explain these topics, anyone who is just starting would already feel lost. So, I thought of making the concept a little easier for fellow programmers, Data Scientists and Pythonistas.

Over the years I've gotten to work with a lot of different programming languages. I grew up on Basic and 6502 machine code. I learned Pascal in middle school and C in high school. I learned Perl, Scheme, Cobol, and Fortran in college. I've written books on x86 assembly language, JavaScript, and PHP.

Ever wanted to do a hacky computer vision project? But you don't want to invest time on learning/using complicated deep learning libraries like PyTorch or TensorFlow? I have been in the above situation a lot of times. Even if you're familiar with these deep learning libraries, there's no way to escape boilerplate code. I figured that I'd have the boilerplate code in a python package which has super simple interface.

The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment management, hyper-parameter optimization, verification of data integrity and visualization of data and results. Additionally, the code of DeepDIVA is well-documented and supported by several tutorials that allow a new user to quickly familiarize themselves with the framework.

The most important subpackages provide implementations of boilerplate code that is relevant to machine-learning problems. These include computer vision, natural language processing, and speech processing. Other subpackages may be smaller and focus on more specific problems or even specific data sets.