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Switch to Computer Vision & Deep Learning: Roadmap, Kaggle Projects, Mentors & Interview Prep
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Switch to Computer Vision & Deep Learning: Roadmap, Kaggle Projects, Mentors & Interview Prep
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Episode Overview
How do you switch into computer vision and deep learning from a non-industry background — and build a portfolio that lands interviews? In this episode, Tatiana Gabruseva, a Computer Vision/Deep Learning engineer and Kaggle Competitions Master now working as a Senior ML Engineer at Cork University Hospital, maps a practical career-change roadmap. Drawing on her move from a physics PhD during maternity leave, Tatiana covers learning paths (Python, ML/DL courses, SQL, algorithms, system design), hands-on projects.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:57 - Career origin: physics PhD to computer vision deep learning
- 2:32 - Transition catalyst: maternity leave, online courses and internship
- 4:20 - Career-change summary: sharing a Twitter thread of practical lessons
- 5:47 - Network makeover: building supportive data science circles
- 7:50 - Overcoming fears and age stereotypes in career change
- 8:53 - Eliminating distractions: focused time management during maternity leave
- 10:49 - Impostor syndrome remedy: interviews and mock interviewing practice
- 14:52 - Selective attention: focusing on positive signals and mentors
- 15:56 - Team building: finding teammates for Kaggle competitions and papers
- 21:04 - Prioritization: Pareto principle, outsourcing and avoiding perfectionism
- 23:45 - Mental rehearsal: initial creation, visualization and Sankalpa technique
- 28:08 - Mentorship strategies: finding and nurturing long-term mentors
- 31:42 - Boundary setting: learning to say no and protect your time
- 34:25 - Embracing failure: treating setbacks as growth opportunities
- 37:30 - Self-care tactics: sleep, support systems and avoiding burnout
- 42:34 - Kaggle vs internships and Omdena-style projects: pros and cons
- 46:40 - End-to-end pet projects: data collection, labeling, deployment and Docker
- 49:29 - Learning roadmap: Python, ML/DL courses, SQL, algorithms and system design
- 53:40 - Starting Kaggle with minimal Python: practical beginner advice
- 54:44 - Improving focus: meditation, analytical practice and achieving flow
- 57:56 - Astroinformatics overview: ML applications in astronomy
- 59:29 - Physics background advantage: math, problem solving and modeling
- 1:02:33 - Leaving academia: lab constraints, maternity leaves and cloud credits
- 1:04:34 - Interview preparation: LeetCode, mock interviews and system design prep