“Free Curriculum for Learning AI in 2024”
Introduction: Are you interested in learning AI, but unsure of where to begin or how to structure your learning path? This article provides a detailed free curriculum designed specifically for hackers and programmers seeking to gain proficiency in AI.
The curriculum emphasizes a top-down approach, focusing on practical application rather than theoretical knowledge. It encourages participants to engage in numerous activities such as joining hackathons, contributing to online communities, and participating in side projects. Additionally, the curriculum suggests staying active on Twitter to network and share insights gained during the learning process.
Mathematics: Three core branches of mathematics serve as essential tools for effective machine learning: linear algebra, calculus, and probability and statistics. Resources provided include a series on Math for ML from a programmer’s perspective, Computational Linear Algebra, and various lecture materials.
Python: Two recommended courses for mastering Python are Practical Python Programming and Advanced Python Mastery.
PyTorch: Essential resources for learning PyTorch include official documentation, tutorials, examples, and puzzle-solving exercises.
Machine Learning: Writing algorithms from scratch, competing in challenges, engaging in side projects, deploying models, and maintaining awareness of industry updates are crucial aspects of this curriculum.
Deep Learning: The curriculum incorporates a 100-page ML book, implementing algorithms from scratch, participating in competitions, and studying winning solutions.
Model evaluation, monitoring, deployment, experiment tracking, and managing data and models are covered extensively throughout the curriculum.
Resources for specialized areas within deep learning include computer vision, reinforcement learning, natural language processing, and large language models.
Supplementary Books: Fluent Python, 2nd Edition, and Programming PyTorch for Deep Learning offer valuable support.
Course Recommendations: fast.ai, Full Stack Deep Learning, UNIGE 14×050 – Deep Learning, and labml.ai provide comprehensive guidance on deep learning.
Additionally, the curriculum highlights the importance of attending conferences, networking events, and hackathons to foster collaboration and exchange ideas.
Conclusion: By diligently following this curriculum and actively engaging in the suggested activities, individuals can successfully develop their AI expertise.
