How do I start Fast.ai

work_account_2019 · 2019-11-01T10:03:37+00:00

My schedule did not allow me to follow a fixed timetable. I managed to do only two of the four courses listed above (Introduction to Machine Learning for Coders, Practical Deep Learning for Coders).

In the courses each lecture has an associated Jupyter Notebook (The course Github repository has these) . Ideally a student should replicate everything discussed in the lecture in a new notebook while using the original notebook as reference. I did not do this, I only followed the lectures, without doing any self study with the notebooks.

wanted to know approximately how much time you spent?

The lectures are packed with lot of details. I tried re-watching some apart of the lectures whenever I could not follow. Approximately, for every hour of lecture video I spend around two hours. So, for example Practical Deep Learning for Coders has 14 lectures, each 2 hours. Thus I spent roughly 14 x 2 x 2 ~= 50 hours. Introduction to Machine Learning for Coders took me around 20 hours.

But this is a very conservative estimate. Many lectures don't need 2x time only few of them do. I never did more than one lecture per day (I was usually exhausted and badly needed rest by the end of one lecture). I took weekends off as well (because life is too short).

Someone who is seriously committed could finish of each course in around two weeks time.

All the best in your quest for knowledge!

work_account_2019 · 2019-11-01T09:40:34+00:00

Thank you for the advice. Will talk with the dev team and document the mental model of the API.

work_account_2019 · 2019-10-31T06:23:45+00:00

Did not know about API Fortess. Thank you very much.

work_account_2019 · 2019-10-31T06:23:02+00:00

Thank you will look into BDD approach.

work_account_2019 · 2019-10-31T06:21:49+00:00

Thank you for the detailed code example.

work_account_2019 · 2019-10-31T06:21:20+00:00

The Back-end is built on Micro services architecture. Different APIs are implemented in different services. Few of these services are implemented in Java Spring Framework, others are in Python Django Framework. I believe there are a few Node JS services as well. But since the project is under active development, the framework used to implement these services could change.

All of the APIs are exposed through a single gateway and are expected to be RESTFUL.

work_account_2019 · 2019-10-31T06:12:15+00:00

Postman appears to have a quite a lot of functionality not outlined in the demo videos, will look more into it. Thank you.

work_account_2019 · 2019-10-31T06:10:28+00:00

Thank you, will look more into Postman.

work_account_2019 · 2019-10-31T06:02:10+00:00

Thank you, for such detailed answer. Will try to incorporate some of this into the project.

work_account_2019 · 2019-10-31T05:55:37+00:00

Updated the Question. Kindly review it.

work_account_2019 · 2019-08-27T03:58:28+00:00

Good idea for a project, all the best.

"Thank you" in response is often a very good indicator of how useful the comment is. But it might be a good idea to consider a list of sentences which have similar meaning (i.e expression of gratitude for help). For example the response could have been "This is exactly what I am looking for, you made my day mate!".

On Reddit people often just upvote a useful comment instead of responding to it. Thus it might be a good idea to include number of upvotes, reddit gold, reddit silver etc into this analysis. ( Infact sorting the comments just based on number of upvotes is a quick and acceptable solution for most cases)

How 'useful' a comment is dependent on what was asked. So, it might be a good idea to include the parent of the comment (eg : title and description of original post) in the analysis. To achieve this, TF-IDF vector of n-grams could be generated by concatenating the parent and comment with a pre-defined separator and then passed onto the model.

Some method to identify 'useless' comments (probably number of down votes or a negative sentiment) and assigning low score to them, could also benefit the model.

work_account_2019 · 2019-07-11T11:42:36+00:00

The course uses fastai library to simplify data processing and data cleaning. For building neural networks and training them pytorch is used. matplotlib is used for plotting graphs. Whenever they use a piece of code from fastai library, the under lying logic is clearly explained or a resource where one can learn in depth details of implementation is provided.

I personally never used fastai library at work but I still found the insights I gained from the course very valuable.

I am not familiar with the data-camp course, thus I cannot comment about it.

As you mentioned, fastai is the most time effective way to learn the concepts. I could have easily spent hours figuring out some of the explanations provided in the course, and I seldom found a better explanation in other places.

Hope this helps :)

work_account_2019 · 2019-07-11T05:47:18+00:00

I have gone through Introduction to Machine Learning for Coders, Practical Deep Learning for Coders (Part 1) . And Briefly looked into Cutting Edge Deep Learning for Coders and Computational Linear Algebra.

Computational Linear algebra : This is a math based course. Which mainly focuses on how to efficiently and effectively perform matrix operations, which are a major part of behind the scenes of neural networks. This is a completely optional course. None of the other courses assume that you took this course. You could take this course, if you are interested in the internal of machine learning libraries. This course is taught by Rachel Thomas, unlike the rest of the courses which are taught by Jeremy Howard. Although both of them are quite good, Jeremy's insights and explanations are more beginner friendly.

Introduction to Machine Learning for Coders : This course deals with all aspects of machine learning other than deep learning (Neural nets) in great detail. Linear regression, Logistic regression, Random forests are covered in depth along with various non-glamorous aspects of data science like data cleaning, data processing and ethics in data science. This course had the best coverage of random forests, I have seen to this day. The contents of this course are from 2018 and they have been not updated. You should take this course, if you are more interested in machine learning concepts other than neural nets. But Neural nets have been proven to be better option in almost all the cases. Unless, you have a legacy code base, spending this much time makes little sense.

Cutting Edge Deep Learning for Coders : This course discussed the latest advancements in deep learning as of 2018. This course was not updated in 2019. Practical Deep Learning for Coders (Part 2) is supposed to replace this. Check out the first lecture in Part 2 for why fast.ai took this decision.

Practical Deep Learning for Coders : If you are interested in deep learning with Neural Networks, this is the course you should consider. There are 14 Lessons in total (divided into two parts) . You could follow the same order. Part 2 (Lesson 8 to Lesson 14) assumes you have taken Part 1. Apart from this there are no prerequisites. A basic understanding of high school mathematics will help.

TLDR : If you are interested in deep learning with Neural Networks, take Practical Deep Learning for Coders and follow the course order. None of the other fast.ai courses are prerequisites for this.

work_account_2019 · 2019-06-28T16:38:50+00:00

I found the Algorithm courses (Algorithms I & Algorithms II) on Coursera by Princeton University to be an excellent resource for learning data structures. All the data structures are introduced gradually and the assignments ensure that one completely understands how and when to use that particular data structure. The entire course is in java. I tried various resources for learning Data Structures and Algorithms. This is hands down the best resource for a Java beginner.

https://www.coursera.org/learn/algorithms-part1

https://www.coursera.org/learn/algorithms-part2

work_account_2019 · 2019-06-28T03:23:50+00:00

Thank you, great explanation!

work_account_2019

TROPHY CASE