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Machine Learning

Advanced Learning Algorithms: Training Neural Networks

Advanced Learning Algorithms: Training Neural Networks
There are many technique to train neural networks. It is important to leverage multiple functions to achieve the best result.There are many technique to train neural networks. It is important to leverage multiple functions to achieve the best result.

Choosing Activation Functions

In the realm of neural networks, selecting the appropriate activation function is crucial for optimal performance. This choice hinges on the specific task at hand. For binary classification problems, where the target label is either 0 or 1, the sigmoid activation function is the go-to choice. This function allows the network to predict the probability of the target being 1, similar to logistic regression.

Example of Activation Functions. Sigmoid, Linear, or ReLu. In the image to the right we see a TensorFlow example whereby the two hidden layers are using a relu activation function, the output layer returns a binary classification.

When it comes to hidden layers, the ReLU activation function has become the standard choice. It offers several advantages over older options like the sigmoid function, including faster computation and a reduced likelihood of gradient vanishing, a common issue in deep neural networks.

Multi-class Classification: Beyond Binary Choices

Multi-class classification is a machine learning technique that extends binary classification to scenarios with more than two possible outcomes. Unlike binary classification, where models predict one of two classes (e.g., 0 or 1), multi-class classification enables predictions across multiple categories.

Real-world Applications:

  • Handwritten Digit Recognition: Recognizing digits from 0 to 9.
  • Medical Diagnosis: Identifying various diseases from patient data.
  • Image Classification: Categorizing images into multiple classes, such as different types of defects in manufactured products.

How it Works:

In multiclass classification, models are trained to assign probabilities to each possible class for a given input. For instance, given an image of a handwritten digit, the model might assign probabilities to each digit from 0 to 9. The class with the highest probability is then selected as the predicted class.

Key Differences from Binary Classification:

  • Multiple Classes: Instead of two classes, models must consider multiple possible outcomes.
  • Complex Decision Boundaries: Decision boundaries become more intricate to separate multiple classes effectively.
  • Advanced Algorithms: Specialized algorithms are required to handle the complexity of multi-class classification, such as those that will be explored in the next video.

Softmax Regression Technique

Softmax regression, also known as multinomial logistic regression, is a generalization of logistic regression that allows us to classify data into multiple classes. It's a widely used technique in machine learning and neural networks, especially in tasks like image classification, text categorization, and more.  

How it Works:

  1. Input: The model takes an input vector x representing the features of a data point.
  2. Linear Transformation: The input is multiplied by a weight matrix W and added to a bias vector b:
z = W * x + b
  1. Softmax Function: The resulting vector z is passed through the softmax function to obtain a probability distribution over the classes:
softmax(z_i) = exp(z_i) / sum(exp(z_j)) for all j
  1. Prediction: The class with the highest probability is chosen as the predicted class.  
Example of a softmax layer in a neural network taking in multiple classes or outcomes.

Adam Algorithm Intuition

Adam, or Adaptive Moment Estimation, is a popular optimization algorithm in machine learning, especially for training neural networks.

Here's the intuition behind Adam:

  1. Momentum:
  • Imagine a ball rolling down a hill. It gains momentum as it rolls, and this momentum helps it overcome small obstacles and continue its descent.  
  • In Adam, momentum is used to accelerate the gradient descent process. It keeps track of an exponentially decaying average of past gradients. This helps the optimizer to move faster in the right direction, especially when gradients are noisy or have similar directions.  
  1. RMSprop:
  • RMSprop (Root Mean Square Propagation) adapts the learning rate for each parameter based on the root mean square of recent gradients.  
  • This helps to dampen oscillations in directions with large gradients and accelerate updates in directions with small gradients.

It is standard to recommend using the Adam optimization algorithm for training neural networks. Adam is generally faster than gradient descent and is widely used by practitioners. While gradient descent is a viable option, Adam is a safer and more efficient choice for most neural network training tasks.

Neural Networks Utilize Convolutional Layers for Efficiency and Accuracy

A new type of neural network layer, known as a convolutional layer, has the ability to improve computational efficiency and reduce overfitting.

This technique involves limiting the scope of each neuron's input to a specific region of the image, rather than processing the entire image. By focusing on smaller, localized areas, convolutional layers can significantly speed up training time and require less training data.

This approach has been shown to be particularly effective in tasks such as image recognition, where neural networks can struggle with overfitting, a phenomenon where the model becomes too specialized to the training data and performs poorly on new, unseen data. Convolutional layers help mitigate this issue by encouraging the network to learn more generalized features.  

[1]: Andrew Ng; DeepLearning.AI & Stanford University's Advanced Learning Algorithms

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