Certificate of Completion
Introduction to Neural NetworksPresented To
Siddhivinayak Waghmode
Certificate ID
sn89-gxbt-q9wd-zqe7
Issued Date
January 7, 2026
Description
Gain a foundational understanding of neural network concepts and implementation. This course covers the essential components of neural networks, including neuron structure, activation functions, network architecture, and the training process involving forward and backward propagation. Learn how to prepare data, calculate loss, apply gradient descent, and build simple networks using common techniques.
Certificate Details
Course Level:
Professional
Issued Date:
January 7, 2026
Certificate ID:
sn89-gxbt-q9wd-zqe7
Learning Outcomes
- Describe the components of a neural network, including neurons, layers, weights, and biases.
- Explain the purpose and characteristics of common activation functions like Sigmoid, Tanh, and ReLU.
- Understand techniques for preparing data for neural network training, including scaling and encoding.
- Implement the forward pass calculation to generate predictions from a neural network.
- Explain the process of backpropagation for calculating gradients.
- Apply gradient descent and its variants to update network weights and minimize loss.
- Implement a basic neural network training loop.
- Understand common techniques like L1/L2 regularization and dropout to prevent overfitting.