Introduction to Deep Learning

December 4, 2023  ·  machine learning

Introduction to Deep Learning

Motivation for Deep Learning

• Eliminates the need for manual feature extraction.

Why Deep Learning is Prevalent Now

1. Big Data: Availability of large datasets.
2. Hardware Progress: Enhancements in hardware, especially those that are massively parallelizable.
3. Software Advancements: Development of efficient software, e.g., TensorFlow.

Understanding the Perceptron

• A perceptron is a single neuron used for forward propagation.
• Components:
  1. Inputs
  2. Weights
  3. Summation
  4. Non-Linearity (Activation Functions):
     • Common functions include:
       1. tf.math.sigmoid
       2. tf.math.tanh
       3. tf.nn.relu
     • Purpose: To model real-world non-linearities.
  5. Output

From Perceptron to Neural Network

• Dense Layer: Basic building block (tf.keras.layers.Dense(units)).
• Sequential Model: model = tf.keras.Sequential([layers]).
• Concept: Stacking multiple layers forms a deep neural network.

Training a Deep Learning Model

1. Loss Function:
   • Examples: cross_entropy_with_logits (for binary classification), Mean Squared Error (MSE).
2. Loss Optimization (Backpropagation):
   • Learning Rate: Balancing exploration and exploitation.
   • Gradient Descent Algorithms:
     1. Stochastic Gradient Descent (SGD)
     2. Adam
     3. Adadelta
     4. Adagrad
     5. RMSProp
   • Mini-batches: For efficient training.
3. Avoiding Overfitting:
   • Regularization Techniques:
     1. Dropout
     2. Early Stopping