One of the foundational papers in the field of neural networks is called "Perceptrons" by Frank Rosenblatt, which was published in the Journal of Mathematical Psychology in 1958. In this paper, Rosenblatt introduced the concept of the perceptron, which is a simple type of neural network that is capable of learning to classify linearly separable patterns.

Another important paper in the field is "A Simple Theory of the Biological Learning Mechanism" by Warren McCulloch and Walter Pitts, which was published in the Bulletin of Mathematical Biophysics in 1943. This paper introduced the concept of using artificial neurons as a model for understanding how the brain processes information.

There have been many other influential papers published on neural networks since then, and the field has grown and evolved significantly. Some other classic papers that you might find interesting include "Learning Representations by Back-Propagating Errors" by David Rumelhart, Geoffrey Hinton, and Ronald Williams, and "Neural Network Learning: Theoretical Foundations" by Michael Kearns and Umesh Vazirani.

Perceptrons" by Frank Rosenblatt:Search On Web

A Simple Theory of the Biological Learning Mechanism - by Warren McCulloch and Walter Pitts:Search On Web

Learning Representations by Back-Propagating Errors- David Rumelhart, Geoffrey Hinton, and Ronald Williams:Search On Web

Neural Network Learning: Theoretical Foundations - Michael Kearns and Umesh Vazirani:Search On Web

1. What features effect the output?

2. Feature engineering

3. Auto discovery of features that matter

4. Transforming inputs into a different representation that yield better results

5. How to learn representations themselves?

6. How does a deep learning network discover these representations at each layer?

1. Those that convert data into different representaion

2.Autoencoders are a type of neural network that is used for unsupervised learning. They are called autoencoders because they are trained to reconstruct their input data automatically, without any external supervision.

3. The basic structure of an autoencoder consists of two parts: an encoder and a decoder. The encoder takes in the input data and converts it into a lower-dimensional representation, known as the latent representation. The decoder then takes the latent representation and converts it back into the original input data.

4. During training, the autoencoder is given a dataset and it tries to learn to reconstruct the data by minimizing the difference between the input and the reconstructed output. The idea is that the autoencoder will learn a compressed representation of the data in the latent space, and this representation will capture the most important features or patterns in the data.

5. Autoencoders have a variety of applications, including dimensionality reduction, denoising, and generating new data. They are often used as a building block for more complex neural network architectures, such as variational autoencoders and generative adversarial networks

Generative adversarial networks (GANs) are a type of neural network architecture that is used for generating new data. They consist of two networks: a generator network and a discriminator network. The generator network is trained to produce new data that is similar to some training data, and the discriminator network is trained to distinguish between the generated data and the training data.

During training, the generator and discriminator networks are trained simultaneously in an adversarial process. The generator produces new data, and the discriminator tries to determine whether the data is real or fake. The generator is then adjusted based on the output of the discriminator, and the process is repeated.

The goal of the training process is to find a balance between the generator and the discriminator, so that the generator produces high-quality, realistic data and the discriminator is unable to distinguish between the generated data and the real data.

GANs have been used for a wide range of applications, including generating images, music, and text. They have also been used for tasks such as style transfer, in which the generator is trained to transfer the style of one image or piece of text to another.

Recurrent neural networks (RNNs) are a type of neural network that allows for information to be passed from one step of the network to the next. This makes them particularly useful for tasks such as language translation, text generation, and language modeling, where the context of the previous words is important in predicting the next word.

RNNs have a "memory" which allows them to remember information from the past, and this makes them well-suited to working with sequences of data. They are called "recurrent" because they perform the same task for every element of a sequence, with the output being depended on the previous computations.

One of the main differences between a regular neural network and a recurrent neural network is that a regular neural network will process an input and forget about it, while a recurrent neural network will process an input and use the output as part of the input for the next step. This means that the recurrent neural network can make use of information from previous steps in the input sequence when processing the current input.