Overview
Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn the foundational concepts of neural networks in this 13-minute educational video that explores artificial neurons, multi-layer perceptrons, and network processing optimization. Discover how perceptrons and artificial neurons function, understand various activation functions, and grasp the architecture of multi-layer perceptrons as universal function approximators. Master the techniques for modeling inputs and outputs in neural networks, with specific focus on classification and regression tasks. Explore the importance of vectorization, tensor operations, and GPU optimization for efficient neural network implementation, including batch processing methods. Access complementary learning materials including a downloadable mindmap and comprehensive references from renowned authors in the field of deep learning and machine learning.
Syllabus
- Introduction
- Perceptrons and Artificial Neurons
- Activation Functions
- Multi-Layer Perceptrons
- Universal Function Approximators
- Modeling Inputs for a Neural Networks
- Outputs for Classification and Regression Tasks
- Vectorization
- Tensors and GPUs
- Processing data in Batches
- Summary Map
- Conclusion
Taught by
Donato Capitella