From the moment we open our eyes in the morning our brain starts collecting data from different sources. **To keep up with the pervasive growth of data** from different sources mankind was introduced with modern **Data Driven Technologies** like **Artificial Intelligence**, **Machine Learning, Deep Learning** etc. These technologies have engineered our society in many aspects already and will continue to do so.

This tutorial series guides you through the basics of Deep Learning, setting up environment in your system to building the very first **Deep Neural Network model**.

**Watch this logistic regression Machine Learning Video by Intellipaat: **

- What is Deep Learning?
- Why do we need Deep Learning?
- Applications of Deep Learning
- Why should you opt for Deep Learning now?
- Biological Neural Network vs Artificial Neural Network:
- Perceptron
- Deep Neural Networks
- Understanding workings of Deep Learning with an example
- Deep Learning Platforms

**Deep Learning** is a subset of **Machine Learning** which is used to achieve **Artificial Intelligence**. Confusing? Let us look at the diagram given below to have a better understanding of these words.

In other words, **Deep Learning** is an approach to **learning** where we can **make a machine imitate the network of neurons in a human brain**. It consists of algorithms which allow machines to train to perform tasks like speech, image recognition and Natural Language Processing. It is a statistical approach based on Deep Networks, where we break down a task and distribute into machine learning algorithms. These algorithms are constructed with **connected layers**. In between first layer or **input layer** and last layer or **output layer **we have set of **hidden layers** in between that eventually gave rise to the word **Deep** which means networks that join **neurons** in more than two layers. These neurons are connected to one another, which propagates the input signal after it goes through the process. In Deep Learning a network can consume a large amount of input data, then process them through multiple layers because of which we can learn complex features of the data.

Now that we have gathered an idea of what Deep Learning is, let’s see why we need Deep Learning.

Our human brain can easily differentiate between a cat and a dog. But how can we make a machine differentiate between a cat and a dog? We would train the machine with a lot of images of cats and dogs. Then once the training is done we will provide the machine with an image of either cat or a dog. Now, we will manually extract some features from the image and make a machine learning model out of it, which would help the machine recognize the input image. And then the machine learning model will predict whether it was a dog or cat. It was easy, wasn’t it? But what will happen when we have a large number of inputs? **Manual extraction** of features for a large input is **backbreaking work.**

What if we could skip the manual extraction part? Wouldn’t it make things a lot easier? When the amount of input data is increased, traditional machine learning techniques are insufficient in terms of performance. That is when **Deep Learning came into the picture**.

Deep Learning and its innovations are advancing the future of precision medicine and health management. Breast Cancer, Skin Cancer diagnostics are just a few examples of Deep Learning in Health Care. In coming years computer aided diagnosis will play a major role in healthcare.

Describing photos, restoring pixels, restoring colors in B&W photos and videos.

Self-driving cars, beating people in computer games, making robots act like human are all possible due to AI and Deep Learning.

Apple’s Siri, Google Now, Microsoft Cortana are a few examples of deep learning is voice search & voice-activated intelligent assistants.

Deep Learning makes allows and publishers and ad networks to leverage their content to create data-driven predictive advertising, precisely targeted advertising and much more.

Predicting natural hazards and seating up a deep-learning-based emergency alert system is to play an important role in coming years.

Analyze trading strategy, review commercial loans and form contracts, cyber-attacks are examples of Deep Learning in the Finance Industry.

**Watch this Machine Learning Video by Intellipaat:**

- Pervasive growth of
**Data**and collection of**Data**became easier. - Advancement of modern hardware and software technologies helping us benefit from the massive data.

We have both collection and access to the data, we have software’s like TensorFlow which makes building and deploying models easy. That is how **Deep Learning is reshaping automation industry** in a big way, becoming one of the hottest evolving technologies of 21^{st} century. Which also means that this is the perfect time to acquire this skill.

So now that we have learnt the importance and applications of Deep Learning let’s go ahead and see workings of Deep Learning. Also, we will discuss one use case on Deep Learning by the end of this tutorial.

Before moving ahead with how Deep Learning works, let us try to understand take how **biological neural network** works.

Our **human brain is a neural network**, which is full of **neurons** and each neuron is connected to multiple neurons. Again, neurons have several **Dendrites**. Dendrites collect input signals which are summed up in the **Cell body** and later are transmitted to next neuron through **Axon**.

Similarly, in an **artificial neural network** a **perceptron** receives multiple inputs which are then processed through functions to get an output. But in case of artificial neural network weights are assigned to various neurons. Then in final layer everything is put together to come up with an answer.

Let us compare Biological Neural Network to Artificial Neural Network:

Biological | Artificial |

Dendrites | Inputs |

Call Nucleus | Nodes |

Synapse | Weights |

Axon | Outputs |

A perceptron is an artificial neuron unit in a neural network. It is an algorithm that enables neurons to learn and processes elements in the training set one at a time for supervised learning of binary classifiers that does certain computations to detect features or business intelligence in the input data.

There are two types of Perceptrons:

- Single Layer Perceptron and
- Multilayer Layer Perceptron.

Single layer Perceptrons is the simplest type of artificial neural network can learn only linearly separable patterns. This type of perceptron is based on a threshold **transfer function**.

Neural networks with two or more layers are called multi-layer perceptron. This type of neural network has greater processing power. In this, the algorithm consists of two phases: the forward phase where the activations are propagated from the input to the output layer, and the backward phase, where the error between the observed actual and the requested nominal value in the output layer is propagated backwards to modify the weights and bias values.

Deep neural network refers to neural networks with multiple hidden layers and multiple non-linear transformations.

As we can see above, simple neural network has only one hidden layer, whereas deep learning neural network has multiple hidden layers.

**Understanding workings of Deep Learning with an example:**

Here we are going to take an example of one of the open datasets for Deep Learning every Data Scientists should work on, **MNIST- a dataset of handwritten digits.** This is one of the most popular deep learning datasets available on the internet.

- It has 70,000 images in 10 classes (0 to 9)
- Out of those 70,000 images, 60,000- training set and 10,000-test set.

- Consider
**neurons**as something that hold a number between 0 to 1, called**activation**In our case it represents the**grey scale value**of the**corresponding pixel**.

- Each one of these images consists of 28 x 28 pixels=784 pixels.

- These
**784 neurons form our first layer**e. the**input layer**of the network. **Output layer**has 10 neurons with activation number from 0 to 1 representing digits from 0 to 9.- Feed
**hidden layers**an image with an activation function, which causes a specific pattern in the next layer, which causes another pattern in the one next to that. - Finally, we get some pattern at the output layer as well.
- Neuron with the highest activation i.e. the brightest one is the output of the network.

Now, let me ask you a question, what role do the hidden layers play in this process? To understand that let us relate to the biological neural network system and how our brain would recognize a digit from an image.

When we see an image of the digit 9, our brain breaks it down as one circle on top. And one line on bottom. Which separately represents 0 and 1. Similarly with 8, one circle on top another on bottom.

Similarly, in deep learning, hidden layers break down the components of the given image forming a pattern. Feed in the image of 9, some specific neurons whose activation would become close to 1.

Combination of these components will trigger a neuron(s*ee the last neuron of the output layer *) with high activation in the last layer. Thus, giving us an **output digit**.

Some of the well-known platforms for Deep Learning:

- TensorFlow
- Keras
- Torch
- DL4J

**In this tutorial series, we will be focusing on modelling our very first Deep Neural Network using TensorFlow**. TensorFlow is one of the best libraries available to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expression.

Next part of this tutorial guides you through the basics of TensorFlow and its **installation **on your system and how tensor flow helps us implement Deep Learning. Jump right into the **TensorFlow Use Case Tutorial**, if TensorFlow is already installed in your system.

To get a more elaborate idea with the algorithms of deep learning refer to our Deep Learning Course.

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