0
Content
Introduction
Artificial Neuron
Weighted Sum for Pre-Activation Value
ReLU Activation Function for Post-Activation
Bias Term
S-Shaped Functions – TANH and SIGMOID
Network Impact
Summary
References
Introduction
Artificial Intelligence (AI) is a broad term for solutions that aim to mimic the functions of the human brain. Machine Learning (ML), in turn, is a subset of AI, suitable for tasks like simple pattern recognition and prediction. Deep Learning (DL), the focus of this section, is a subset of ML that leverages algorithms to extract meaningful patterns from data. Unlike ML, DL does not necessarily require human intervention, such as providing structured, labeled datasets (e.g., 1,000 bird images labeled as “bird” and 1,000 cat images labeled as “cat”).
DL utilizes layered, hierarchical Deep Neural Networks (DNNs), where hidden and output layers consist of computational units, artificial neurons, which individually process input data. The nodes in the input layer pass the input data to the first hidden layer without performing any computations, which is why they are not considered neurons or computational units. Each neuron calculates a pre-activation value (z) based on the input received from the previous layer and then applies an activation function to this value, producing a post-activation output (ŷ) value. There are various DNN models, such as Feed-Forward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), each designed for different use cases. For example, FNNs are suitable for simple, structured tasks like handwritten digit recognition using the MNIST dataset [1], CNNs are effective for larger image recognition tasks such as with the CIFAR-10 dataset [2], and RNNs are commonly used for time-series forecasting, like predicting future sales based on historical sales data.
To provide accurate predictions based on input data, neural networks are trained using labeled datasets. The MNIST (Modified National Institute of Standards and Technology) dataset [1] contains 60,000 training and 10,000 test images of handwritten digits (grayscale, 28x28 pixels). The CIFAR-10 [2] dataset consists of 60,000 color images (32x32 pixels), with 50,000 training images and 10,000 test images, divided into 10 classes. The CIFAR-100 dataset [3], as the name implies, has 100 image classes, with each class containing 600 images (500 training and 100 test images per class). Once the test results reach the desired level, the neural network can be deployed to production.
Figure 1-1: Deep Learning Introduction.
Continue reading
