History of Transformers

Transformers were introduced in 2017 as a new neural network architecture that revolutionized the field of natural language processing (NLP). They were first introduced by Vaswani et al. in their paper "Attention Is All You Need" and quickly gained popularity due to their ability to process entire sequences of data at once, rather than relying on recurrent connections like RNNs.

One of the key features of Transformers is the self-attention mechanism, which allows the network to focus on different parts of the input sequence depending on their relevance to the current task. This mechanism has proven to be highly effective in NLP tasks such as machine translation, sentiment analysis, and question answering.

Since their introduction, Transformers have become a key component of many state-of-the-art NLP models, including BERT, GPT-2, and T5. They have also been adapted for use in other domains such as computer vision and speech recognition, further highlighting their versatility and impact on the field of deep learning.

Convolutional Neural Networks (CNN)

CNNs are a type of deep neural network commonly used in image and video recognition tasks. They use a process called convolution to extract features from input data, which allows them to identify patterns and structures in images. The convolution process involves sliding a small window called a kernel over the input data and performing element-wise multiplication and summation to generate an output feature map. CNNs typically include multiple convolutional layers followed by pooling layers, which reduce the size of the feature maps while retaining important information. The final layers of the network are typically fully connected layers that combine the extracted features to make a prediction. CNNs have achieved impressive results in image recognition tasks such as object detection and image classification.

This is an example of a Convolutional Neural Network (CNN) architecture. The image shows the flow of information through the layers of the network, with convolutional layers followed by pooling layers and fully connected layers. The final layer produces the output prediction.

The sliding window, or kernel, moves across the input data in a series of strides, extracting features from each part of the input. The size of the kernel and the stride length are both hyperparameters that can be adjusted to optimize the performance of the network. By using a series of these convolutional filters, CNNs are able to identify features of varying complexity in the input data. The output from the convolutional layer is then passed through a non-linear activation function, such as ReLU, to introduce non-linearities into the network and increase its modeling power.

CNN are Great for image and video data

• CNNs are commonly used in recognition tasks, such as object detection and image classification.
• CNN not beneficial with text data

CNNs are not as effective with text data because they are designed to work with fixed-size inputs, such as images. Text data, on the other hand, is often variable-length and requires a different approach to processing. While CNNs can be used with text data by converting it to a fixed-size representation such as a bag-of-words or embedding, these methods can lose important information about the sequence and context of the data.

• RNNs are better suited for processing sequential data like text, as they are able to maintain an internal state that captures information about previous inputs in the sequence.

Recurrent Neural Networks (RNNs)

are a type of neural network architecture commonly used in natural language processing and speech recognition tasks. Unlike traditional feedforward networks, which process input data in a single pass, RNNs maintain an internal state that allows them to process sequences of data. This internal state is updated at each time step, and can be thought of as a memory that retains information about previous inputs. This makes RNNs well-suited for tasks such as language modeling, where the meaning of a word is often dependent on previous words in the sentence.

Here is an example of a recurrent neural network architecture:

• RNN are good with Sequential data
• RNNs can be computationally expensive to train, particularly with long input sequences.
• They are also prone to the problem of vanishing gradients, where the gradients used to update the weights during training become very small, making it difficult for the network to learn long-term dependencies.
• RNNs are limited in how much parallelization they can achieve during training, since each time step depends on the previous time step.

RNN and Markov chain

RNNs do not use Markov chains, but they can be thought of as a type of Markov chain where the hidden state at each time step is a function of the previous hidden state and the current input. This allows RNNs to capture long-term dependencies in sequential data, which is difficult for other types of neural networks.

Transformers

Transformers solve the problems of RNNs by using self-attention mechanisms that allow them to process entire sequences at once, rather than relying on recurrent connections. This makes training faster and more parallelizable, since each position in the sequence can be processed independently. The self-attention mechanism also allows the network to focus on different parts of the sequence depending on their relevance to the current task, which helps it learn long-term dependencies more effectively. Additionally, Transformers are not prone to the problem of vanishing gradients, since they do not use recurrent connections.

Attention

In the context of neural networks, attention refers to a mechanism that allows the network to focus on different parts of the input sequence depending on their relevance to the current task. This is achieved by computing a weight for each element in the input sequence, which reflects its importance to the current output. The weights are then used to compute a weighted sum of the input elements, which is used as the output of the attention mechanism. This allows the network to selectively attend to different parts of the sequence, which can be particularly useful for tasks that involve long-term dependencies or variable-length inputs. The attention mechanism is a key feature of Transformer networks and has been shown to be highly effective in a variety of natural language processing tasks.