# Deep Learning Study Plan

- 3 minsI plan to spend the next 6 months, roughly 25 weeks, studying deep learning; commenting on each weeks’ achievements as I go. I’ll spend the first 10 weeks getting up to speed with the material, using various online resources (see below). I don’t have a clear plan for weeks 11-25 yet; they will mostly depend on what I find most fascinating in the first ten weeks. I expect that as I progress my focus will gradually shift from studying material to completing interesting projects.

For the first 10 weeks, I plan complete the following online courses:

- Fast.ai course 1 & 2
- Andrew Ng’s 5-part DL Course
- Ian Goodfellow’s Deep Learning Book
- Berkeley’s Deep Reinforcement Learning

I listened to some of the fast.ai course already, and they drop you right into the subject. I care both about fundamentals and practical applications, and usually I proceed to study in that order. Fast.ai’s top-down methodology reverses that approach, and gives you the applications first, theory later. I’ll be curious to see how their method works. They cover a lot of material, obtain stellar reviews, and have an active forum community.

Andrew Ng’s course and Ian Goodfellow’s book goes the other way - building up from the fundamentals. I’ll use them to fill in holes in my understanding. That will produce some redundancy as I’ll go over the same material multiple times. Not ideal efficiency-wise, but it will hopefully cement my understanding.

The RL course in the list as the other resources don’t treat the topic.

## Timeline

My timeline is intentionally optimistic and challenging. It serves as a motivating standard to compare myself against, even if I don’t finish on time.

### Week 1 - Review

Since I haven’t done any coding and math lately, I have to get up to speed.

For the math, I’ll go over part 1 of Godfellow’s book. The parts that come easy I’ll skim, the parts that I find difficult I’ll study in detail.

I’ll also finish Andrew Ng’s course on machine learning. It doesn’t contain much new material for me, but it serves well for a review. I’ve completed about half the course already, the rest shouldn’t take long. For assignments, I’ll use the unofficial Python notebooks (there is no point in learning Octave).

I mostly used R in the past, so I also have to get up to speed with my Python syntax. I’ll go over Udemy’s Python for Data Science and Machine Learning course, skipping most of the theory, focusing on the syntax of the algorithms I already know.

### Weeks 2-3 - Fast.ai Course 1

Previously I have done some lessons of this course - but not on a serious level. I plan to start from the beginning, reproducing the notebooks discussed in the videos.

### Weeks 4-5 - Andrew Ng’s DL Course, Goodfellow Part 2

I don’t know much about the DL course, but based on the suggested weekly hours, I should be able to complete it in 2 weeks.

### Weeks 6-10 - Fast.ai Course 2, RL Course, Goodfellow Part 3

A long time (5 weeks) and a lot of material. I’ll probably post a more detailed breakdown as I approach week 6.