Practice Labs: Deep Learning Interviews (Kashani)

Source PDF: 2201.00650v2.pdf Book: Deep Learning Interviews by Shlomo Kashani (2nd Edition)

This book covers mathematical foundations and interview-style problems for deep learning. Labs below are organized by the book’s progressive difficulty levels.

Labs

Lab	Topic	Book Chapter	Difficulty
Lab 01	Logistic Regression from Scratch	Part II: Logistic Regression	Kindergarten
Lab 02	Information Theory & Entropy	Part III: Information Theory	High School
Lab 03	Calculus, Gradients & Backpropagation	Part III: Calculus, Algorithmic Differentiation	High School
Lab 04	Probability & Bayesian Deep Learning	Part II: Probabilistic Programming & Bayesian DL	Kindergarten
Lab 05	Neural Network Ensembles	Part IV: NN Ensembles	Bachelors
Lab 06	CNN Feature Extraction & Deep Learning	Part IV: CNN Feature Extraction + Deep Learning	Bachelors

How to Use

1. Each lab is a Jupyter notebook with theory (markdown) and fully implemented code cells

2. Read the theory cells, study the implementations, and run each cell

3. Open in Jupyter: jupyter notebook lab_01_logistic_regression.ipynb

Prerequisites

• Python 3.8+

• NumPy

• Matplotlib

• SciPy (for Lab 04)

Suggested Order

1. Lab 01 - Logistic Regression (foundational)

2. Lab 04 - Probability & Bayesian DL (builds on probability basics)

3. Lab 02 - Information Theory (entropy, KL divergence)

4. Lab 03 - Calculus & Backpropagation (core for training NNs)

5. Lab 06 - CNN Feature Extraction (applies NN concepts)

6. Lab 05 - Ensemble Methods (advanced techniques)