Was sind die besten Möglichkeiten, um Lernen und Arbeit in einer ML-Rolle in Einklang zu bringen?

Balancing learning and working in an ML role is all about setting priorities. Focus on key work tasks and carve out time for learning, breaking it into small, manageable chunks. Try to apply what you’re learning to your daily projects—this helps reinforce new skills and keeps things relevant. Set aside dedicated time each week for study, like any other work task, and stick to it. Don’t forget to celebrate small wins along the way to stay motivated. Most importantly, make learning enjoyable by exploring topics that genuinely interest you!

If you are working in the R&D department of startup that is growing now, I think you can learn from your work. There won't necessarily be difference always between working with ML and learning ML. You can learn through your work in ML.

Clarity in Learning Objectives: Define specific goals rather than vague aspirations. Example: Set goals like completing a course on convolutional neural networks. Measurable Targets: Specify actions and outcomes, such as applying knowledge to a project. Time-bound: Establish deadlines, like finishing by the end of the month. Motivation and Progress Tracking: Concrete goals enhance focus, motivation, and facilitate tracking progress and achievements.

While choosing the right resources is crucial, genuine interest and strong why in any field, not just machine learning, is equally important. This interest should guide your selection of mentors and learning materials. Reviews, peer recommendations, and self-research can all be valuable tools in this process.

I am a research assistant in an ML group at a University. I acknowledge that staying on top of the daily published papers can be overwhelming. Ways I try to manage this is participating in our weekly organized reading groups where someone presents a paper. I also subscribe to #machinelearning weekly newsletters for regular updates. Additionally, I follow researchers on social media who I share similar interest with.

Some crucial things to do, which not only can help you in your work but also keep you focused on your individual learning and development - Staying Updated with Industry Trends: Staying in know of latest advancements and trends in machine learning by regularly reading papers, following key researchers and practitioners on social media, and attending conferences. Engaging in Continuous Practice: Dedicate time to work on practical projects. Participating in Kaggle competitions, or contributing to open-source projects can go a long way!

Learning shouldn't feel like a chore! Experimentation is the key to truly grasping new concepts. Dive into online challenges (*i.e Kaggle) – they're a fun and practical way to test your skills and discover fresh approaches.

Online courses are great to learn ML. But discipline and dedication are needed to complete them. We may be more likely to complete an online course if: - it is paid rather than free, - there are deadlines. Only learning is not enough. We also need to apply the knowledge in projects, and keep revising, otherwise we may forget the concepts over time. Also, various things (e.g. work, learning, exercise, socializing, chores, etc.) are important. But due to limited time, we can not do everything. E.g., for evenings or weekends, if you are not busy in work, then you have to choose from taking online courses, or exercising in the gym, or socializing with friends, or relaxing, or household chores. Each option has advantages and disadvantages.