The study brings together the aspects of mathematics, physics, and chemistry to elucidate the origin, evolution, and functions of the Universe and the celestial bodies contained within it. Naturally, different practitioners have their own preferred ways of doing things.Astronomy is a branch of science that covers the study and analysis of all extraterrestrial objects and their phenomena. We strive to continually improve, so future experiences on Google Cloud Platform might differ from my slides.Īdvice and process is based on my experience as a data scientist. These screenshots were captured in winter 2017/8, so there's no guarantee things still look the same whenever you find this, dear reader. Reference book PDFs (can give beginners indigestion): Bishop, Elements of SL.Bayesian ML intro with Python code: Bayesian Methods for Hackers.Interactive tinkering with neural networks at /.Intro ML book (older classic with lots of wisdom, uses WEKA examples): Data Mining by Ian Witten.Intro ML guide (sense of humor, Python code): /.Intro ML textbook (clear math, R code): ISL.I post info about talks and fun things I make for you on Twitter: /quaesita Classic learning resources for beginners Enjoy! If this was useful, where can you find more? When you're done with Demo00.pdf, move to Demo01.pdf, and so on. Think of it as watching over my shoulder while I open my console, click around, and write code. How do you get started if you want to see the demo without doing anything hands-on? If you get stuck, hopefully the slides will help you get unstuck. Whenever you see instructions in the README, follow along in the slides. Open Demo00.pdf from this repo in one browser tab and the README.md from the Step-by-Step Deep Learning Tutorial in another tab. Hope you have fun learning some deep learning! How do you get started? How do you get started if you want to do the hands-on Deep Learning Tutorial? Then I captured screenshots to help you along in case you get stuck following our instructions. I had a lot of fun collaborating with the amazing Brian Foo to make that code for you here. My original day-long ML workshop ('Making Friends with ML') did not have a hands-on component, so the next step was to write code following the same step-by-step structure as the workshop. Bonus slides from my old lectures with ML hints, tips, summaries, and pitfall alerts.

Screenshots taken while I walk through the Step-by-Step Deep Learning Tutorial.These walkthrough slides help guide you through the code demo I wrote with Brian Foo Where did these slides come from? Step-by-Step Deep Learning Tutorial WalkthroughĪuthor: Cassie Kozyrkov Twitter: Help yourself to the code

Deep learning tutorial pdf