One reason that people manipulate hyperparameters is to improve the bias and variance that a given model contains. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Models with high bias pay little attention to the training data and oversimplify the model, which will lead to high errors on training and test sets. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. High Variance models do not generalize based on previous data much, if at all, and thus while it may perform well on training data it will not on test sets. Briefly, the Bias vs Variance scale is a scale between over- and under-fitting of a model. By manipulating various hyperparameters, you can change between over- and under-fitting to get the best predictions

Bias and Variance in Deep Learning

Tuning or optimizing hyperparameters involves finding the values of each hyperparameter which will help the model provide the most accurate predictions.

Hyperparameter Tuning in Deep Learning

Hyperparameters related to neural network structure:
- Number of hidden layers (Depth)
- Number of hidden units (Width)
- Dropout method
- Activation function for each layer
- Weights Initialization

Hyperparameters related to training algorithm:
- Learning rate $\alpha$
- Momentum parameter $\beta \sim 0.9$
- $\beta_1 \sim 0.9, \beta_2 \sim 0.999, \epsilon \sim 10^{-8}$
- Number of Gradient descent iterations
- Mini-batch size 
- Optimizer algorithm
- Learning rate decay
- Regularization rate $\lambda$ 

List of Common Hyperparameters in Deep Learning

A hyperparameter is a setting that affects the structure or operation of the neural network. In real deep learning projects, tuning hyperparameters is the primary way to build a network that provides accurate predictions for a certain problem. 

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Deep feedforward networks, also called feedforward neural networks, or multilayer perceptrons (MLPs), are the quintessential deep learning models.

It was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.

Deep Feedforward Networks (MLP = Multi-Layer Perceptrons)

A helpful website that introduces neural networks:
https://missinglink.ai/guides/neural-network-concepts/

Neural Network Reference

Feedforward neural networks are called networks because they are typically represented by composing together many different functions. The model is associated with a directed acyclic graph describing how the functions are composed together.

For example, we might have three functions $f^{(1)} , f ^{(2)}$, and $f^{(3)}$ connected in a chain, to form $f (x) = f ^{(3)}(f^{(2)}(f^{(1)}(x)))$. These chain structures are the most commonly used structures of neural networks. 


Overall Structure of Deep Feedforward Networks

A Feed forward neural network model training occurs in six stages:

- Iterate until convergence
   - Initialization
   - Forward propagation
   - Error function (Objective function)
   - Backpropagation
   - Weight update

At the end of this process, the model is ready to make predictions for unknown input data. New data can be fed to the model, a forward pass is performed, and the model generates its prediction.

Stages of Feed Forward Neural Network Learning

Hyperparameters of Feedforward Neural Network

There are a few different types of cost functions, but for the most part the same type of cost function is used for neural networks. We usually use the principle of maximum likelihood for most cases, such as when our parametric model uses a distribution p(y | x;θ), where the relationship between the input (training data) and output (model's predictions) of the model as the cost function. A sometimes simpler approach used, where we are predicting some statistic of y conditioned on x, instead of predicting a complete probability distribution over y. 

Deep Feedforward Network Cost Functions

-The chain rule that underlies the back-propagation algorithm (one of the common algorithms used to train neural networks) was invented in the seventeenth century.

-While algebra and advanced mathematics have always been implemented to solve computer optimization problems, gradient descent was not introduced as a technique for iteratively approximating the solution to optimization problems until the nineteenth century.

-Non-linear functions could only be properly studied once a multilayer perceptron and a means of computing the gradient were developed. One of the biggest advancements for this development occurred once eﬃcient applications of the chain rule based on dynamic programming were implemented between the 1960s and 70s. 


Deep Feedfoward Network as time passed.

Approaches to back-propagation taking a computational graph and a set ofnumerical values for the inputs to the graph and returning a set of numerical valuesdescribing the gradient at those input values is called symbol-to-number diﬀerentiation.

Symbol-to-number Diﬀerentiation

Theano and Tensorflow Approach is to take a computational graph and add additional nodes to the graph that provide a symbolic description of the desired derivatives.

Theano and Tensorflow Approach

These algebraic and graph-based representations operating on symbols, orvariables that do not have speciﬁc values are called symbolic representations.

Symbolic Representations

Language modeling is one of the application of feedforward networks: predicting upcoming words from prior word context. Neural language modeling
is an important NLP task in itself, and it plays a role in many important algorithms for tasks like machine translation, summarization, speech recognition, grammar correction, and dialogue.



Feedforward Neural Language Modeling

Most Neural NLP classifications take word embeddings as their inputs.
$$ x = [e_{w1};e_{w2};e_{w3};...;e_{wn}]$$
These kind of embeddings are gained from the pertaining process relying on another algorithm.
Embedding can also be learned as part of the NLP task in the neural language modeling task.



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