Machine learning and deep learning have captivated me, as they have so many others in recent years.
Unlike so many others, I’ve quit my prior work to try to transform a pastime into a career, and I’ve set myself a three-month deadline to learn everything I can about DL and test the waters for a future career.
If you Google “deep learning study route,” you’ll come up with a huge list of suggested methodologies, classes, math books, projects, and so on for becoming a “machine learning expert.” However, what I truly require is a schedule.
You must start somewhere, and I’m starting with a Software Engineering degree, a jack-of-all-trades in a variety of languages, hands-on experience building a Raspberry Pi Hadoop Cluster, completion of Udacity’s Data Analyst Nanodegree, and recent work as a Technical Product Owner at a professional services start-up.
I don’t think you need my experience to follow my schedule; in fact, I’m going to go out on a bough and say you only need a year of coding experience — if Fast.ai MOOC can do it, I can too.
There is no master’s degree. There isn’t a Ph.D. insight. You’ll be on your own way to a new job in only three months and 13 steps.
Now get down to business!
• Part 1 of a two-part series on deep practical learning for programmers
• Create your own deep learning system.
• Enter a competition on Kaggle.
• Begin a blog.
• Attend meetings in your area.
• Part 2 of deep practical learning for coders
• Begin your own unique project.
• Continue to blog.
• Complete your individual project.
• Make changes to your résumé.
• Make an online portfolio.
• Look for amazing businesses.
• Begin your new job.