This is the continuation of what I have been doing for last two months. You can refer to my earlier reading lists for June & July.

In June I over estimated myself as to what I can read in a month. I couldn’t complete the syllabus I set for myself. I refined my reading list to accommodate the time in July. I was able to complete most of it but couldn’t work on a project.

In August I’ll focus only on the one Course on Natural Language Processing by Stanford. I have so far completed 10 Lectures from the series. In this month I’ll aim to complete 10 more lectures from the course.

Each lecture is very deep and requires additional readings and practice. I typically go through each video lecture at-least twice.

**Course**

Stanford CS224N: NLP with Deep Learning | Winter 2019 – Stanford’s course on NLP taught by Christopher Manning.

### Here’s the list of lectures, I completed in July,

- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction & Word Vectors [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors & Word Senses [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models & RNNs [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients & Fancy RNNs[Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation seq2seq, Attention [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects [Done]
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering [Done]

### List of lectures, I plan to complete in August,

- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning

If you’ve got any questions, or just want to say Hi, feel free to talk to me