With the start of the second semester at Stanford University (January 2018), a new class was released — CS 20: Tensorflow for Deep Learning Research. Since during the day I fully focus on my at Speechify in SF, I am not able to attend any lectures or meet the professors. For that reason, I decided to do the course (+every assignment) remotely. Thankfully, Stanford is so welcome that they have publicly opened all the resources.
I decided to document my journey in a series of blog posts. I am thrilled to share my impression of the course with you and, also, to further enhance my ML skills.
My plan is of writing follows the course structure which is ordered in a way that after 3 sets of 6 lectures, students need to submit an assignment. So I will do this:
- Write one article for every 6 lectures (3 weeks).
- Write article/articles with explanations + solutions of the assignments.
I will keep this introductory article short in order to focus on the course material from the next one. I will start with Overview of Tensorflow, Operations, Linear and Logistic Regression, Eager execution and Variable sharing and managing experiments.
Hope you enjoy the series. You can find Part 1 here.
Thank you for the reading. If you enjoyed the article, give it some claps ? . Hope you have a great day!
My impression of Stanford’s Tensorflow course: Introduction was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Source: Deep Learning on Medium