Code readability may be defined as the convenience with which the source code is understood and modified. Code readability is of vital importance when maintaining larger legacy codebase and working in teams. If the code is easy to read, it would be easy to debug, refactor, enhance and maintain, thereby reducing the lifecycle cost of software product. As readability directly impacts software quality, researchers have proposed adding a separate phase during development focused on improving code readability. Although, code readability is subjective to human judgement, static analysis tools can provide a fair estimate on various factors impacting readability such as indenting, naming and commenting. As a programmer, follow the tips provided below to create self-documenting software program, which can be understood conveniently by other programmers during it's lifetime.
AI is finding it's way in a wide variety of applications pertaining to almost every industry. This AI driven rapidly evolving landscape has created a demand for a unique blend of technical, creative, and interpersonal skills highly sought-after by employers. Listed below are some specialized AI-related skills that are becoming increasingly valuable in the modern times. 1. AI Models Development Understanding how AI and ML work including the underlying algorithms, and learning to develop ML powered apps using tools like TensorFlow or PyTorch is a highly desirable skill to master in the age of AI. Furthermore, the skills in fine-tuning and adapting large pre-trained models (like GPT, BERT, or Vision Transformers) to specific use cases are also useful, allowing you to create specialized applications without starting from scratch. Leveraging pre-trained models and adapting them to new tasks with limited data is particularly useful in NLP and computer vision. 2. AI Models Deployment Pr