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Modern Deep Learning Computation Tutorial
Section titled “Modern Deep Learning Computation Tutorial”This tutorial introduces the core concepts behind modern deep learning frameworks like PyTorch and TensorFlow. We explore how these libraries enable efficient definition, optimization, and evaluation of the complex mathematical expressions used in machine learning, leveraging automatic differentiation and hardware accelerators like GPUs. We will use PyTorch for practical examples.
Audience
Section titled “Audience”This tutorial is designed for learners aiming to understand the foundations of modern deep learning frameworks and develop projects using tools like PyTorch or TensorFlow.
Prerequisites
Section titled “Prerequisites”Before you proceed with this tutorial, familiarity with the following is recommended:
- Basic Python programming.
- Fundamental concepts of linear algebra (vectors, matrices) and calculus (derivatives/gradients).
- Familiarity with NumPy for numerical operations is helpful.
- Basic understanding of Machine Learning and Neural Network concepts (e.g., training, parameters, backpropagation).