Deep learning is a subfield of machine learning that involves training artificial neural networks to learn from data. It has revolutionized the field of artificial intelligence and has led to breakthroughs in areas such as computer vision, natural language processing, and speech recognition. In this lesson, we will explore the fundamental concepts of deep learning.
Artificial Neurons
Artificial neurons are the building blocks of neural networks. They are mathematical functions that take in inputs, apply weights to those inputs, and produce an output. The output is then passed on to the next layer of neurons. The weights are learned during the training process and determine the strength of the connections between neurons. The activation function determines whether the neuron should fire or not based on the weighted sum of its inputs.
Layers
Neurons are organized into layers in a neural network. The input layer receives the raw data, such as an image or text. The output layer produces the final output, such as a classification or prediction. The hidden layers are in between the input and output layers and are responsible for processing the data. Deep neural networks have many hidden layers, which allows them to learn complex representations of the data.
Activation Functions
Activation functions determine whether a neuron should fire or not based on the weighted sum of its inputs. Common activation functions include the sigmoid function, which produces a value between 0 and 1, and the rectified linear unit (ReLU) function, which produces a value between 0 and infinity. The choice of activation function depends on the task at hand and the structure of the neural network.
Training
Neural networks are trained using a process called backpropagation. During training, the network is presented with a set of labeled examples and adjusts its weights to minimize the difference between its predicted output and the true output. This process is repeated many times until the network achieves a satisfactory level of accuracy. Deep neural networks require a large amount of data and computational resources to train.
Applications
Deep learning has many applications in various fields. In computer vision, it is used for tasks such as object detection, image classification, and facial recognition. In natural language processing, it is used for tasks such as sentiment analysis, language translation, and chatbots. In speech recognition, it is used for tasks such as voice recognition and speech synthesis. Deep learning is also used in fields such as finance, healthcare, and transportation.
Now let's see if you've learned something...
⇦ 2 Applications of Deep Learning 4 Deep Learning Architectures ⇨