Lecture 11. Neural Network Fundamentals#

1. Deep Learning Topics:

  • Fundamentals of Neural Networks

  • Training using gradient backpropagation

  • Regularization techniques

  • Network architectures: Autoencoders, Convolutional networks (CNN), Recurrent networks (RNNs), Attention and Transformers.

2. Multi-layer Perceptron:

  • Introduces non-linearity through function composition.

3. Perceptron Model:

  • Basic building block for ANNs.

  • Consists of inputs, synaptic weights, bias weight, and an activation function.

4. Limitations of Linear Models:

  • Many real-world problems aren’t linearly separable.

  • XOR problem introduced as an example.

5. Activation Functions in ANNs:

  • Step function, Sign function, Logistic function, tanh function, Rectified linear unit (ReLU), etc.

6. Feed-forward Artificial Neural Network:

  • Comprises input layer, hidden layer(s), and output layer.

7. ANN in Supervised Learning:

  • Can handle univariate regression, multivariate regression, binary classification, and multiclass classification.

8. Power of ANNs:

  • Capable of approximating various non-linear functions.

  • Universal Approximation Theorem: Single-layer ANNs can approximate any continuous function.

9. Deep Learning and Representation Learning:

  • Depth refers to the number of hidden layers.

  • Consecutive layers in ANNs form representations of increasing complexity.

  • Depth often provides more accurate models than width alone.

10. Backpropagation:

  • A method to calculate the gradient of loss in a neural network.

  • Uses the chain rule for derivatives.

  • Consists of forward and backward propagation of errors.

  • Helps in updating weights based on the error.

11. Next Topics:

  • Deep net training and autoencoders.

Note: The above summary captures the core concepts and topics discussed in the lecture. It is advisable to revisit the detailed content for a deeper understanding, especially if specific examples, equations, or nuances are essential for your application.