*Written for some coworkers who wanted to learn deep learning*

## How to get started

I spent some time learning classical ML first since it was most relevant for my job. You can learn deep learning first without any other ML experience/knowledge.

### ML resources

I started off with a homemade ML in 10 weeks course. TL;DR, here’s the course, using content primarily from Hands-On Machine Learning with Scikit-Learn and TensorFlow and Andrew Ng’s Coursera course on ML:

```
- Chapter 2 End-to-End Machine Learning Project
- Chapter 3 Classification (precision/recall, multiclass)
- Text feature extraction (from sklearn docs)
- Chapter 4 Training Models (linear/logistic regression, regularization)
- Advice for Applying Machine Learning
- Chapter 5 SVMs (plus kernels)
- Chapter 6 Decision Trees (basics)
- Chapter 7 Ensemble Learning and Random Forests (xgboost, RandomForest)
- Chapter 8 Dimensionality Reduction (PCA, t-SNE, LDA)
- Machine Learning System Design
(Google) Best Practices for ML Engineering A group of friends and I worked through this content at a cadence of one meeting every other Wednesday starting late June 2018 wrapping up at the end of 2018.
```

### Deep learning resources

- Neural Networks and Deep Learning by Michael Nielsen http://neuralnetworksanddeeplearning.com/index.html
- fast.ai
- Practical Deep Learning for Coders https://course.fast.ai/videos/?lesson=1
- Part 2: Deep Learning from the Foundations https://course.fast.ai/videos/?lesson=8

- distill is a good resource for topics. ex:

### NLP resources

- Kyunghyun Cho’s lecture notes on “Natural Language Processing with Representation Learning”: https://github.com/nyu-dl/NLP_DL_Lecture_Note/blob/master/lecture_note.pdf
- Jacob Eisenstein’s textbook on “Natural Language Processing” (https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)

### Brushing up on math

It’s easy to get intimated by the math in papers. I found that taking the time to relearn linear algebra and some calculus has had compounding returns!

- Matrix Calculus by Terence Parr and Jeremy Howard
- backprop chapter in Neural Networks and Deep Learning
- Matrix Algebra - Linear Algebra for Deep Learning
- 3blue1brown for practical and visual linear algebra
- for theoretical linear algebra: Finite Dimensional Vector Spaces

## Paper reading

Once you’ve understood common concepts, the best way to keep up to date with research and continue learning beyond courses is by reading and reimplementing papers.

#### How to manage papers

I recommend you track papers either through Zotero or Mendeley. I started off using Zotero but switched Mendeley to share folders/papers in groups I was in. I don’t have a strong opinion on which one is better.

#### How to figure out what to read? Check out these sources:

- twitter - follow 20+ practitioners/researchers you admire on twitter to find interesting papers
- ML subreddit
- AI/DL fb groups
- arXiv - there’s 10-20 new papers on arXiv every day for AI/computational linguistics so you could just browse arXiv every day for the latest papers in the topics you’re most interested in
- AI blogs

#### How to read a paper:

- your objective is to figure out quickly which papers NOT to read
- spend time in the conclusions
- try to answer the question
`what is novel`

? - create a reading group! Even just one other person can already save you 50% of the time.