It’s day 5 since we’ve attended the lecture. I was wondering if learners could share the steps they have taken to practice lesson1. I’m particularly interested in the steps taken by folks who are:
New to DL/ML arena and have average coding experience.
Folks who are doing this course full time. How’s your typical day?
Here are few of the questions that I’ve in my mind:
Are you coding everything starting import statements?
How are you approaching the assignment?
e.g. going through the notebook and then coding? or
going through the notebook, making notes, trying to code or
something else?
How many attempts have you made before you could write code without peeking into the notebook?
Are you looking at the function definitions used in the lesson? at least the functions defined in the fastai library?
Have you tried different dataset? If yes, pls. share the location.
Are you trying Kaggle? DogsVsCats
Apart from lesson1, what’s on TODO list for this week? e.g. blogs, papers etc…
@jeremy Is it possible for you to organise a kaggle in class competition for students for practising.
I know that there are so many competitions already open that we can participate but wanted to know your opinion about a competition for us.
@jeremy I understand this course is for coders but what if I’m someone who doesn’t code regularly. I’ve sense of coding but I come from typical services/support background.
I’m able to understand your lectures but I’m spending a lot of time on helper functions used in class than exploring papers, reading blogs etc. Once I decipher each line of function, everything makes sense.
I remember you mentioning that you were coding since 7 yet I thought of asking you for the approach you would take to gain max out of course. Please see that I can invest 20+hours a week.
Practicing coding is the most important thing for you to do - it’s the only way to improve. You don’t need to understand how the helper functions work - just what they do. Focus on trying to run the same steps on different data sets, and focus on looking at the inputs and outputs.
Everyone is at a different level, so just do what you can given your particular situation.
I suggest reading “Python for Data Analysis” 2nd ed, since you’ve got the time to invest. Everything in that book is useful for this course.
Magic of HTML5, It’s more suitable for things like Stack Traces and additional details in my opinion. You can also add a summary tag <summary></summary> tag within detail to have your custom summary text. Like the one below. You can easily use Markdown Links for links such as yours.
Every minute you learn something new.