Are you gearing up for a job interview in the deep learning field? Look no further!

We just published a video course on the YouTube channel that is specifically designed to prepare candidates for deep learning job interviews, covering 50 common interview questions with detailed explanations.

Tatev Aslanyan created this coruse. She is a seasoned data science professional with expertise in Machine Learning, Deep Learning, NLP, AI, and Statistical Modelling. She holds a Bachelor’s and Master’s degree in Econometrics and Operations Research from top-tier universities in the Netherlands.

Course Overview

This comprehensive course is structured to enhance your understanding of deep learning through a series of focused questions and detailed explanations. Here's a glimpse of the main topics you'll explore:

Fundamental Concepts

  • Understanding Deep Learning: Grasp the essence of deep learning and its significance in the AI landscape.
  • Neural Networks: Dive into the core structure of neural networks, their function, and importance.

Key Differences and Comparisons

  • Deep Learning vs Traditional Machine Learning: Discover what sets deep learning apart from traditional approaches in machine learning.

Technical Deep Dives

  • Neurons and Neural Network Architecture: Learn about the building blocks of neural networks and their intricate architecture.
  • Activation Functions in Neural Networks: Explore the various types of activation functions and their roles in neural networks.

Optimization and Problem-Solving

  • Gradient Descent and Backpropagation: Understand these crucial concepts and their roles in training neural networks.
  • Tackling the Vanishing and Exploding Gradient Problems: Get insights into solving some of the most common challenges in neural network training.

Advanced Topics

  • Regularization Techniques: Learn about L1 and L2 regularization and their impact on preventing overfitting in neural networks.
  • Optimization Methods and Adaptive Learning Rates: Delve into advanced methods like Adam, AdamW, RMSProp, and their significance in neural network training.

... and many more, including discussions on batch sizes, dropout techniques, normalization methods, and the handling of specific challenges like overfitting and the curse of dimensionality.

Whether you are a beginner eager to break into the field of deep learning or a professional seeking to brush up on your knowledge before an interview, this course is made for you. It's not just about answering questions; it's about understanding the 'why' and 'how' behind each answer, giving you a robust foundation in deep learning concepts.

Watch the full course on the YouTube channel (4-hour watch).