 # Basic Deep Learning Algorithms You Need to Know

A comprehensive guide to the basic deep learning algorithms you need to know, including what they are, how they work, and why you should care.

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## Introduction to Deep Learning Algorithms

In general, a learning algorithm is any algorithm that can be trained to recognize patterns. Deep learning algorithms are a subset of machine learning algorithms that are used to learn complex patterns in data.

Deep learning algorithms are composed of multiple layers of processing units, called neurons, that extract and transform features in the data. The output of one layer becomes the input to the next layer, in a process that ultimately produces a prediction.

There are many different types of deep learning algorithms, but some of the most common include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs).

CNNs are commonly used for image classification and recognition tasks, while RNNs are designed for sequential data such as text or time series data. LSTMs are a type of RNN that can learn long-term dependencies, and are often used for tasks such as language translation and handwriting recognition.

Deep learning algorithms have been shown to be very successful at a variety of tasks, including image classification, object detection, and speech recognition.

## Linear Regression

Linear Regression is a supervised learning algorithm where we have a dataset consisting of training examples (x1, y1), …, (xn, yn), and each training example is a pair of input features and corresponding labels. We want to build a model that can predict the label y for a new data point x. This model is called a linear regression model and it has the following form:

y = w0 + w1 * x1 + … + wp * xp

where w0, …, wp are the model parameters and x1, …, xp are the input features. This equation is still called linear because it is linear in the model parameters w0, …, wp. We can fit this model using the training data we have by finding the values of w0, …, wp that minimize the sum of squared residuals:

minimize (y1 – (w0 + w1 * x11 + … + wp * xp1))^2 + … + (yn – (w0 + w1 * xn1 + … + wp * xnp))^2

## Logistic Regression

Logistic regression is one of the most basic and essential deep learning algorithms. It is used for binary classification, i.e. predicting whether an instance belongs to one class or the other. The algorithm is parametric, meaning that it makes assumptions about the data that might not be true in all cases. Nevertheless, it is a very powerful tool and can be used in a variety of tasks such as predicting whether a patient will develop a certain disease, or whether an email is spam or not.

## Support Vector Machines

Support Vector Machines (SVMs) are a type of supervised learning algorithm that can be used for both classification and regression tasks. The algorithm is mostly used in settings where there is a clear margin of separation between the classes.

SVMs are based on the concept of decision planes that separate different classes. A decision plane is a hyperplane that divides the feature space into two parts, one for each class. SVMs try to find the decision plane with the largest margin between the two classes. This way, even if there is some noise in the data, the chances of misclassifying points are minimized.

The main advantage of SVMs is that they can be used in situations where there is not a clear separation between the classes. This is done by using what is known as a kernel trick. Basically, this transforms the data into a higher dimensional space where a clear separation may exist. SVMs have been shown to be very effective in many real-world applications such as facial recognition and hand-written digit classification.

## Neural Networks

Neural networks are a type of machine learning algorithm that are used to model complex patterns in data. Neural networks are similar to other machine learning algorithms, but they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data.

Neural networks are often used for classification tasks, such as identifying images or fraudulent credit card transactions. They can also be used for regression tasks, such as predicting the future price of a stock. Neural networks are powerful machine learning algorithms, but they can be difficult to understand and work with.

## Convolutional Neural Networks

Convolutional Neural Networks, or CNNs, are a type of neural network that excels at image recognition tasks. CNNs are similar to other kinds of neural networks but they have an architecture that is specially designed to take advantage of the strengths of images (i.e., the fact that pixels that are nearby in an image are often related).

CNNs typically have a number of layers, including a convolutional layer (which does the image recognition) and a pooling layer (which downsamples the image to reduce noise and make it easier for the network to learn).

There are many different types of CNNs but they all share these basic characteristics.

## Recurrent Neural Networks

RNNs are a type of neural network where the output from previous timesteps is fed as input to the current timestep. This creates temporal dependencies which make RNNs ideal for tasks where the current output is dependent on the previous output, such as in text generation. RNNs can be unrolled to form a fully connected neural network, as shown below. RNNs can also be stacked to form deeper networks. ## Long Short Term Memory Networks

Deep learning is a subset of machine learning that is concerned with artificial neural networks. These neural networks are designed to function in a similar way to the biological brain, and they are capable of learning and making decisions on their own.

One of the most popular types of deep learning algorithms is the Long Short Term Memory network, or LSTM. This type of algorithm is often used for tasks such as text recognition and machine translation.

LSTM networks are a type of recurrent neural network, which means that they take into account information from previous inputs when making decisions. This makes them well-suited for tasks that require understanding context, such as language translation.

LSTM networks are made up of cells, each of which contains an input gate, an output gate, and a forget gate. The input gate controls what information is allowed into the cell, the output gate controls what information is allowed out of the cell, and the forget gate determines how much information from the previous input will be forgotten.

Inputs to an LSTM network can be any kind of data, including text, images, and time series data. Outputs can be anything from a single value to a complex vector.

## Auto-encoders

An auto-encoder is a neural network used for dimensionality reduction; that is, for feature selection and extraction. An auto-encoder consists of two parts: an encoder that transforms the input data into a hidden representation, and a decoder that reconstructs the input data from the hidden representation.

Auto-encoders are similar to Principal Component Analysis (PCA), but unlike PCA, they are non-linear and can learn complex patterns in data. Auto-encoders are also unsupervised, meaning they do not require labels or target values during training.

There are many different types of auto-encoders, but the most common is the Restricted Boltzmann Machine (RBM). RBMs are energy-based models that define a distribution over a set of binary unseen variables (visible units) and another set of binary latent variables (hidden units). The visible units are connected to the hidden units by weights, and each unit has a bias.

The energy function of an RBM defines the probability of a particular configuration of visible and hidden units:

E(v,h) = -∑i∑jvihwij -∑i∑jvibiasi -∑i∑jhibiasj
where v is the vector of visible units, h is the vector of hidden units, wij is the weight connecting visible unit i to hidden unit j, biasi is the bias for visible unit i, and biasj is the bias for hidden unit j.

The RBM training process starts with an initialization phase where the weights and biases are randomly assigned. The model then proceeds through a series of Gibbs sampling steps, where each step samples from the distribution defined by the current weights and biases. After each step, the weights and biases are updated to better match the distribution of the training data.

Once training is complete, the RBM can be used to reconstruct new data points by starting with a random vector of visible units and running through several Gibbs sampling steps. The final step will output a new vector of visible units that will be close to the original input vector.

## Restricted Boltzmann Machines

As a member of the generative models family, Restricted Boltzmann Machines stochastically create artificial data that matches the distribution of the training set. RBM can be viewed as a two-layer neural network: the visible input layer and a hidden layer. The key difference between RBMs and other neural networks is that there are no interconnections between units within each layer; units are only connected to units in the other layer. This connection structure makes it possible to train RBMs in an unsupervised manner using a technique called Contrastive Divergence.

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