Link: Ask HN: Daily practices for building AI/ML skills? | Hacker News

The Hacker News discussion emphasizes the importance of practical, hands-on experience for software engineers developing AI/ML skills with limited time. Suggestions for projects include training models on specific tasks like classifying digits or tuning language models.

Participants also suggested learning through implementing research papers and engaging in Kaggle competitions, which offer exposure to robust modeling techniques. Resources such as Papers With Code and courses from fast.ai and Stanford were highlighted as helpful.

There's a debate between focusing on fundamental ML concepts and diving into the latest advancements in the field. While strong statistical foundations are valued, some advocate for a more application-focused approach using existing libraries.

The roles of mathematics and practical implementation were also discussed, underscoring the varying needs between ML research and engineering. Research demands a deeper mathematical understanding, whereas engineering benefits more from software skills and practical application.

With the constraint of learning ML in bout one hour per day, efficient use of time is crucial. Engaging with AI/ML communities and taking advantage of weekends for extended learning periods were suggested strategies.

Career development through projects and possibly Kaggle competitions was also discussed, with continued learning tailored to individual goals in either ML engineering or research. The importance of persistence and consistent effort in learning AI/ML was emphasized, alongside the use of accessible resources like PyTorch and TensorFlow. #

Summary of Recommendations:

• Prioritize hands-on experience: Building projects and implementing papers are considered the most effective ways to learn.
• Start with a specific area of interest: Focus on a particular area within AI/ML, like computer vision or natural language processing, to avoid getting overwhelmed.
• Balance fundamentals and latest advancements: While a solid foundation in core ML concepts is important, don't get bogged down in theory; learn the fundamentals as needed while working on practical applications.
• Leverage existing tools and libraries: Don't waste time building everything from scratch; use libraries like PyTorch or TensorFlow.
• Engage with the community: Participate in forums and communities to learn from others and stay updated.
• Consider a structured learning path: Courses like fast.ai can provide a good starting point, followed by more specialized resources.
• Learn to use LLMs effectively: Mastering prompt engineering and leveraging existing LLMs can be a valuable skill.
• Tailor your learning to your goals: Whether you aim for ML engineering or research, adjust your learning path accordingly.
• Be patient and persistent: Learning AI/ML takes time and effort, especially with limited daily commitment. Consistency is key.

Noteworthy Resources Mentioned:

• Courses: fast.ai, Stanford CS231n/CS234, Karpathy's "Zero to Hero", Andrew Ng's courses
• Websites: Papers With Code, Hugging Face, Kaggle
• Books: "An Introduction to Statistical Learning", "Elements of Statistical Learning", "Deep Learning Book"
• Blogs: Karpathy's blog, Lilian Weng's blog, Jay Alammar's blog
• Tools: PyTorch, TensorFlow, JAX, Google Colab, Jupyter Notebook

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Yoooo, this is a quick note on a link that made me go, WTF? Find all past links here.