Round 1: Technical Screening (Duration: 45 minutes)

Objective: Assess the candidate’s technical knowledge and experience in deep learning methodologies.

1. Evaluate experience in Python, TensorFlow, and PyTorch
2. Examples of questions:
   • Describe the key differences between CNN and RNN.
   • Explain how backpropagation works in a neural network.
   • How do you implement a deep learning model using TensorFlow or PyTorch?

Expectations: Candidates should display a strong understanding of deep learning concepts and techniques, as well as familiarity with popular frameworks.

Round 2: Hands-on Coding Challenge (Duration: 60 minutes)

Objective: Assess the candidate’s ability to build and optimize deep learning models by providing a real-life data-driven problem.

1. Focus on evaluating code implementation, model selection, and performance optimization
2. Examples of tasks:
   • Implement a CNN model for image classification using a provided dataset.
   • Optimize a pre-trained model for sentiment analysis in text data.
   • Build a recommender system using a deep autoencoder.

Expectations: Candidates should demonstrate their ability to apply deep learning techniques in a live coding environment and produce efficient code and high-performance models.

Round 3: Technical Deep Dive (Duration: 60 minutes)

Objective: Evaluate the candidate’s expertise in applying deep learning algorithms to real-world problems in computer vision, NLP, or speech recognition.

1. Evaluate the depth of understanding of specific deep learning methods and applications
2. Examples of questions:
   • Discuss the architecture and applications of Generative Adversarial Networks (GANs).
   • Explain the role of attention mechanisms in NLP.
   • Describe different techniques for improving the performance of deep learning models, such as data augmentation or transfer learning.

Expectations: Candidates should exhibit in-depth knowledge and experience in applying deep learning techniques to specific problem domains and applications.