Large Language Models and Deep Learning

Artificial intelligence (AI) refers to computers programmed to perform tasks that normally require human-level information processing.

Machine learning systems have the ability to learn without being explicitly programmed. They learn from data without being given a complex set of rules.

Neural networks are a machine learning algorithm that simulates the way the human brain works. They are composed of layers of artificial neurons that are connected to each other by weights. Originally, neural networks only had a few layers because of the difficulty in training deeper networks .

Deep learners are neural networks with many layers of neurons, which enables them to solve more complex problems with larger data sets than traditional one or two-layer networks.

Natural language processing (NLP), as depicted in the diagram to the right, cuts across all of these system types. NLP includes classification, text comprehension, summarization, and text generation. Deep learners used for NLP are often referred to as large language models.

Feedforward Networks

Feedforward neural networks were one of the first classes of neural network developed. Such a network takes a set of inputs and uses them to produce an output.

They are composed of neurons, which are arranged in layers. The input layer is the first layer of neurons and is responsible for taking in the input data. The neurons in the input layer pass the data to the neurons in the next layer, which then pass it on to the next layer. At each step the numerical values are multiplied by a set of learned weights.

This process continues until the output layer is reached, where the output is produced. The number of layers and the number of neurons in each layer are determined by the complexity of the task the neural network is trying to perform.

Neural networks are trained using a process called backpropagation. In backpropagation, the weights and biases of the neurons are adjusted in order to minimize the error between the desired output and the actual output. This process is repeated until the desired output is achieved.

Recurrent Neural Networks

Recurrent neural networks (RNNs) are a more sophisticated type of artificial neural network, used for processing sequential data. RNNs have a memory that allows them to use prior information. They can be used as large language models.

This memory allows them to recognize patterns in data and make decisions based on those patterns. RNNs have been found to be particularly useful in tasks that require understanding context, such as language translation and speech recognition.
The neurons in each layer are connected to the neurons in the previous layer, and the neurons in the same layer are connected to each other in a cyclic fashion.

This creates a recurrent structure that allows the RNN to maintain its state over time, allowing it to remember the data from the previous input.

Unlike other neural networks, RNNs can process data in blocks of variable size, making them more efficient and adaptive to changing data. One difficulty this class of networks has is in making use of information that emerges from long-range dependencies in the input.

Deep Learning

Deep learning networks are neural networks with more than a few layers. Their significance is that only recently has it been possible to reliably train such networks.

They have also been shown to be affective on more complex tasks which usually require huge amounts of data.

They may have recurrent connections, but it is not a requirement

Deep learning models are the latest generation in a large class of models known as “neural networks.” They are an important competent of large language models.

These classes of model are made up of many layers of densely connected units processing information and passing it to other layers for further processing. Deep learners have made significant advances in object detection, facial recognition, and linguistic analysis.

Many deep learners do not have recurrence, or memory, but use self-attention—to powerful effect.


Transformers use self-attention to process input data. Self-attention lets each element in the sequence attend to other words in the sequence and evaluate how relevant those are to the current word. An ambiguous word such as “bank” might be disambiguated by being paired with either “money” or “river.”

A self-attention score for each pair in the input is calculated, resulting in a weight matrix that is proportional to the contextual importance of each word in the input with respect to the other words.

Unlike traditional neural networks, transformers can achieve better performance in tasks such as natural language processing and machine translation due to their ability to capture long-term dependencies, even better than recurrent neural networks.

They can also be used in a variety of other tasks such as image and video processing, recommendation systems, and speech recognition.

Transformers are made up of an encoder, a decoder, and an attention layer. The encoder takes the input data and encodes it into a series of vectors. The decoder then decodes the vectors back into the original data. The attention layer allows the model to focus on the most important parts of the input data and ignore irrelevant information.

Transformers: An Intuitive Overview