Deep learning in itself is a vast topic. But just to mention it is also a subfield of Machine learning. The algorithm of Deep learning is inspired by neural networks. Deep learning for beginners must cover the concept of neural networks. For those who are completely unaware of the advantages of Deep Learning, Machine Learning or even neural networks; this is the best place to be right now. Let’s move straight to a journey where Deep learning for beginners will be simplified.
As you all know science, medicine, creativity, emotions act as pillars to support the complex and vast world we live in. The absence of any one of these elements will result in an extremely different environment the sight of which is very horrendous. So, an important query which arises here is, how are we able to learn and camouflage into this socially complex world?
The answer to this question can be discovered when we understand the cognitive ability of the human species. Perception is an important derivative of the cognitive ability which basically covers everything from taking in and processing inputs from all the senses. The first part that is input from the surroundings is taken care of by our six senses, whereas the processing that is involved following the input from our senses happens in our brain where the input goes through a humongous network of neurons.
Deep Learning And Neural Networks
As we already mentioned that algorithm of Deep learning is inspired by neural networks so let’s dig a little deep inside that.
An interesting aspect about a neuron is that each neuron is connected to numerous other neurons generating an extremely complex network that generate and transmit electrical impulses which execute everyday tasks performed by humans. Next up, what if we are able to create our own miniature network of the artificial network! That will count as one of the major advantages of Deep learning.
There is an entire stream dedicated to this motive. Right from its birth in 1986, deep learning and neural networks continue to impress and surprise deep learning enthusiasts all across the world. To deeply understand neural networks, we must first understand how a biological neuron works. The biological inspiration of the neural networks consists of three elements-the cell body, the dendrites, and the axon. So briefly stating, a neuron takes input as electrical impulses from various synapses, processes those inputs and then gives another electrical impulse via synapses and dendrites to other neurons, thus forming a huge colony of neurons that are interconnected with each other. When we consider neural networks, layers are of utmost importance.
A layer is a bunch of artificial neurons that take the input from the previous layer and pass it on to the next layer. Does artificial neuron seem intriguing? An artificial neuron is a programmed algorithm that is tuned to take in the input, process it and then generate the output passing it on to the subsequent layers. Let’s jump right into the specifics of a learning model using some amazing analogies.
Algorithm Of Deep Learning
To start off with understanding the algorithm of Deep learning for beginners, imagine a toddler who is learning to walk which, is a breath-taking sight. But that’s not it, the view of one learning to walk is a picture which is worth a thousand words. When we learn to walk we fall countless times, we cry, we give up and then get back up again. Most importantly we are supervised by someone to keep us motivated and to teach what and how to walk. Surprisingly neural networks also learn in a similar fashion. Learning by repetitive iterations under what is right and what is wrong is what supervised learning is all about.
There is also an alternate yet more interesting way of learning termed as an unsupervised method of learning. This is analogous to babies and toddlers learning to do things only based on observation without having any idea as to what is right. Let’s consider an important type of model called the artificial neural network. This is a rather simple type of network consisting of multiple layers with individual threshold values for each layer. These threshold act as a benchmark for the output of a particular neuron. The most important thing to begin within deep learning is an existing dataset like the iris dataset with the inputs and the corresponding outputs which will, in turn, help a programmer to train the neural network model. The inputs to these models are in the form of parameters or variables that depend on different conditions.
These variables may change or alter subject to changes in the learning environment. An artificial neural network has its own set of weights that it develops in accordance with these input parameters. When the training data is subjected to the training model, it determines the various parameters that are crucial for determining the output and also keeps track of the output based on the dataset. It then classifies those parameters with the help of weights which help it sort the input parameters on the basis of importance.
These weights are determined over repetitive iterations and updates with regard to the output. The output of each of these models may vary depending on the activation of the model. Some might give a binary output whereas some give us a continuous input and some, on the other hand, give us a class of output. These outputs may then be accordingly classified as classification or regression. Classification is when a model is trying to predict whether a particular set of input values give an output that fit in a particular class. The latter, however, is an approximate prediction of the output of a set of input values.
Let’ take an example for the advantage of Deep learning. Predicting the stock price over a period of time subject to various parameters is regression whereas a yes/no question or detecting whether a patient is diabetic or no etc. fall under classification. This example in itself describes the algorithm of deep learning.
This path of ANN and other neural network models lead us to some mind-blowing technologies like convolution networks, reinforcement learning, image recognition, classification and much more. Deep learning when applied properly, can be used to a variety of different things that need not be necessarily be executed by the conventional algorithm of Deep learning.
Google’s deep dream is one such example. It basically uses a network model called convolution neural networks which develops hallucinogenic appearance to the pictures that are fed into the network. Automatic machine translation is also a key area that has been developed advent of deep learning and neural networks. The key concept here is how a deep learning model is used to automatically translate text and images into some other language.
As astonishing as it sounds, google translate uses this very phenomenon to help process pictures of words in one language and then convert it to another.
Deep learning thus has a plethora of opportunities for enthusiasts to try out and experiment on. These count as the advantage of Deep learning as well. For the most part, many times a good deep learning model may be better or smarter than the programmer who built it!