Abstract

Problem Statement

Design a self-driving car system focusing on its perception component (semantic image segmentation in particular)

Problem Statement

Given a text and knowledge base, find all the entity mentions in the text(Recognize) and then link them to the corresponding correct entry in the knowledge base(Disambiguate).

For my own projects, the project should be set up as the following:

Problem Statement

• How would you build an ML system to predict the probability of engagement for Ads?
  • Search engine
  • Social media platforms

Problem Statement

Display media recommendations for a specific user. Type of user feedback includes explicit and implicit feedbacks. The implicit feedback allows collecting a large amount of training data.

Problem Statement

Design a Twitter with 500 million daily active users feed system that will show the most relevant tweets for a user based on their social graph.

Problem Statement

Ask for questions: Scale, Scope, Personalization.

• Scope: general search or specialized search?
• Scale: number of websites? QPS (queries per second)?
• Personalization: logged-in user or not

The Grad-CAM (Gradient-weighted Class Activation Mapping) is a generalization of CAM and is applicable to a significantly broader range of CNN model families.
The intuition is to expect the last convolutional layers to have the best compromise between high-level semantics and detailed spatial information which is lost in fully-connected layers. The neurons in these layers look for semantic class-specific information in the image.

$$L_{Grad-CAM}^c = ReLU(\sum_k\alpha_k^cA^k)$$

where $$\alpha_k^c = \frac{1}{Z}\sum_i\sum_j\frac{\partial{y_c}}{\partial{A_{ij}^k}}$$