Introduction to Deep Learning Algorithms
Deep Learning, a large and broad domain, may initially seem daunting. Choosing the appropriate methodology for your specific task is paramount, as using the wrong model could lead to inferior performance or even hinder problem-solving. Artificial Neural Networks (NNs) underpin all deep learning practices and were named due to their composition of interconnected neuron nodes representing the functioning of the human brain.
Neural Networks: Layers and Depth
Neural networks are structured in layers. They consist of an input layer that captures data, an output layer that forms a judgment or prediction based on the input, and numerous intermediary layers. Each layer comprises neurons, and neurons in specific layers are connected with their peers in the adjacent layers. When a model encompasses multiple layers, it is termed 'deep'. Thus, the term “Deep Learning” emphasizes deep neural networks as the backbone of this domain.
Once the number of layers and nodes for your model is set, the next step is to find the ideal parameters for each neuron. Given that a model could encompass thousands of neurons, this task is unreasonable to perform manually. Fortunately, the network itself determines the variable ranges by analyzing the data submitted - it learns from the data.
Key Deep Learning Models
Some of the most influential deep learning algorithms include Multilayer Perceptrons (simple) as well as advanced models like Convolutional Networks and Generative Adversarial Networks.
- CNNs - Convolutional Neural Networks: Use convolutional layers and their naming hinges on convolution, a crucial matrix operation in neural networks. Information flows in a linear direction within a CNN, from input to output. At the core of CNN architecture lie convolutional layers – hidden layers that employ convolution. This mathematical procedure is crucial for deep learning, especially in computer vision. CNNs are frequently used for computer vision and time-series forecasting.
- GANs - Generative Adversarial Networks: Deep learning models used for generative modeling – the process of learning a pattern in input data to generate new samples. Training GAN involves creating a model capable of generating new data that mimics the original dataset samples. GAN consists of two sub-models – one trained to create fresh samples, while the other attempts to categorize samples as genuine or synthetic. Both sub-models train competitively until the generator deceives the discriminator, signaling that the generative model is producing convincing samples. GANs are ideal for producing new, authentic samples, such as images resembling the ones in your collection or creating new video game levels.
- MLPs – Multilayer Perceptrons: The simplest neural network type, and therefore, often known as vanilla neural networks. MLPs comprise one or more hidden layers of neurons, with neurons connected to the ones in subsequent layers, ensuring a linear flow of information. When the input flows in one direction, the network is deemed a feed-forward neural network and MLPs are a basic form of this. MLPs are practical while handling tabular data and classifying inputs. They also aid in resolving regression problems, predicting an exact value for each input.
- RNNs – Recurrent Neural Networks: Designed to handle sequential data like texts or weather predictions. Unlike simple feed-forward neural nets that process input to output data linearly, RNNs permit bi-directional flow of information – from input to output and repetitively within a hidden layer and itself. RNNs are generally not suitable for tabular or image datasets but perform well with sequences.
- LSTMs - Long Short Term Memory networks: RNNs stand a risk of losing previous information if there is a substantial gap between relevant data and the present step. LSTMs mitigate this issue; they are RNNs capable of forming long-term connections. What's unique to LSTMs is the cell - a unique structure with various gates that direct the flow of information. The cell state serves as a passageway for data to traverse from the beginning of the network to its end. With specialized layers called gates, the LSTM can delete or add information to the cell state, enabling the LSTM to maintain useful data over extended periods.