For the past few years after my Master’s, I did many jobs, long term, short term, internship, or full-time. I also had too many interviews, some of them I failed. Together with my friends, we had collected many materials, including basic algorithms, popular questions, basic machine learning knowledge, and deep learning knowledge. Then I organized them as one huge PDF (150+ pages).
A very brief outline:
- Data structure + popular questions
- Machine Learning
- SoftDev interview questions
The material covers some screenshots from other people’s lectures and books. [Some slide pages are not in English! I am too lazy to translate them..]
I went through this PDF each time before there is an interview, in the case to answer questions like “what is knn”. I hope you may find the material useful. Download link:
Recently, I am working on a new version by adding more deep learning basics.
New items need to be updated: Merge sort; Sorting code in Python; Boyer-Moore Vote Algorithm.
My post about Auto-encoder.
For Variational Auto-Encoders (VAE) (from paper Auto-Encoding Variational Bayes), we actually add latent variables to the existing Autoencoders. The main idea is, we want to restrict the parameters from a known distribution. Why we want this? We wish the generative model to provide more “creative” things. If the model only sees the trained samples, it will eventually lose the ability to “create” more! So we add some “noises” to the parameters by forcing the parameters to adapt to a known distribution.
Continue reading “Understanding Variational Graph Auto-Encoders”
Graph Convolution Networks (GCNs)  deal with graphs where the data form with a graph structure. A typical graph is represented as
G(V, E), where
V is the collection of all the nodes and
Eis the collection of all the edges.
Continue reading “Understanding Graph Convolutional Networks”
Please check my notes for Transfer Learning introduction!
A brief introduction on unsupervised transfer learning methods.
The presentation focused on unsupervised transfer learning methods, introducing feature-based and model-based strategies and few recent papers from ICML, ACL.
Unsupervised Transfer Learning
Comments are welcomed!
TensorFlow provides save and restore functions for us to save and re-use the model parameters. If you have a trained VGG model, for example, it will be helpful for you to restore the first few layers then apply them in your own networks. This may raise a problem, how do we restore a subset of the parameters? You can always check the TF official document here. In this post, I will take some code from the document and add some practical points.
Continue reading “TensorFlow 08: save and restore a subset of variables”