Introduce yourself: What’s your background? Did you have a technical role prior to enrolling in the course?
My background is split between a traditional business education and a computer science education. I studied Business Administration and Economics in college and worked as a Strategy & Enterprise Intelligence consultant at a Big 4 consultancy. After a few years, I decided to study computer science part-time because I knew that technology and digitalization would add the highest value to businesses in the future. Knowing some math and programming was definitely helpful before taking the course, but the specialization really built on top of those basic skills to help me sharpen my intuition about deep learning.
Why did you take the DL specialization?
I loved Andrew‘s first course on machine learning, so I was really excited to take the Deep Learning Specialization. My background in computer science was quite limited, but I was working on machine learning for my masters thesis while looking for a job as a Data Scientist. I knew that this specialization would give me both the knowledge and the credibility to work in this field. I’m glad that my plan paid off, as the specialization helped me land my dream Data Science job at the Volkswagen subsidiary Carmeq in Berlin.
What was your level of familiarity with AI before taking this course?
I had taken two online courses on machine learning before taking the Deep Learning Specialization. However, I would have still considered myself a novice in the field of AI. The specialization helped me become more comfortable thinking about and understanding the possibilities of AI.
What did you find most valuable about the deep learning specialization?
I have a huge man-crush on Andrew – his ability to explain complex problems easily to others is simply outstanding. Within a few minutes and a couple slides, he manages to give his viewers the feeling that they can learn any concept just as easily. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward. And the field of deep learning truly has so many challenging yet powerful concepts and ideas.
Have you continued to learn about deep learning after completing the specialization?
I continue to try to learn as much as possible about AI. I’ve taken a few more online courses, and I’ve written blogs about AI to hone my own understanding and ability to explain new concepts. I’m also very glad to work in the automotive industry – an industry that is heavily shaken up by the advancement of deep learning. It is incredibly satisfying to apply concepts I learned in the specialization to relevant, real-world use-cases.
What are you currently working on? How did the DL specialization help you get there?
I’m currently working at Carmeq GmbH, the Berlin-based innovation vehicle of Volkswagen AG. The company does a tremendous job investing in machine learning and its applications. I’m working on a computer vision task, which could potentially be used by all Volkswagen brands. And even more excitingly, I’m using the deep learning knowledge I gained in the specialization to solve the current issue. I’m also working on improving the communication and exchange between all machine learning practitioners for the company. Finally, I’m involved in a project with the Silicon Valley-based research arm from Volkswagen. The work is a challenging yet rewarding experience.
Anything else you’d like to share?
I can’t wait to see more courses from deeplearning.ai! The field of deep learning is so vast and evolving so rapidly that the community is thirsting for new content. Personally, I’d love to see one on GANs/VAEs, Reinforcement Learning or Genetic Algorithms.
To all Data Science newbies – take this specialization! The course content is great and the programming exercises are fun. The mentors are great to turn to for help when you’re stuck. In general, if you’re spending more than ten minutes on a given problem, search the forums. Don’t miss out on this chance to really understand neural networks for a reasonable price – it’ll be worth it.