Contents

## 1. Deep Learning Tutorial Python

Today, we will see **Deep Learning** with Python Tutorial. Deep Learning, a **Machine Learning** method that has taken the world by awe with its capabilities. In this Deep Learning Tutorial Python, we will discuss the meaning of Deep Learning With Python. Also, we will learn why we call it Deep Learning. Moreover, this Python Deep learning Tutorial will go through artificial neural networks and Deep Neural Networks, along with deep learning applications.

So, let’s start Deep Learning with **Python**.

## 2. What is Deep Learning?

### a. Deep Learning Definition

To define it in one sentence, we would say it is an approach to Machine Learning. To elaborate, Deep Learning is a method of Machine Learning that is based on learning data representations (or feature learning) instead of task-specific **algorithms**. We also call it deep structured learning or hierarchical learning, but mostly, Deep Learning. For feature learning, we observe three kinds of learning- supervised, semi-supervised, or unsupervised.

The patterns we observe in biological nervous systems inspires vaguely the deep learning models that exist.

### b. Characteristics of Deep Learning With Python

Some characteristics of Python Deep Learning are-

- They use a cascade of layers of nonlinear processing units to extract features and perform transformation; the output at one layer is the input to the next.
- These learn in supervised and/or unsupervised ways (examples include classification and pattern analysis respectively).
- These learn multiple levels of representations for different levels of abstraction.

**Do you read Machine Learning With Python**

## 3. Deep Learning With Python – Why Deep Learning?

Deep Learning uses networks where data transforms through a number of layers before producing the output. This is something we measure by a parameter often dubbed CAP. The Credit Assignment Path depth tells us a value one more than the number of hidden layers- for a feedforward neural network. But we can safely say that with Deep Learning, CAP>2. Each layer takes input and transforms it to make it only slightly more abstract and composite.

## 4. Artificial Neural Networks

Now, let’s talk about neural networks. An **Artificial Neural Network** is a connectionist system. It is a computing system that, inspired by the biological neural networks from animal brains, learns from examples. When an ANN sees enough images of cats (and those of objects that aren’t cats), it learns to identify another image of a cat.

### a. Structure

An Artificial Neural Network is nothing but a collection of artificial neurons that resemble biological ones. Synapses (connections between these neurons) transmit signals to each other. A postsynaptic neuron processes the signal it receives and signals the neurons connected to it further.

**Have a look at Machine Learning vs Deep Learning**

A neuron can have state (a value between 0 and 1) and a weight that can increase or decrease the signal strength as the network learns. We see three kinds of layers- input, hidden, and output. There may be any number of hidden layers. Typically, such networks can hold around millions of units and connections. Note that this is still nothing compared to the number of neurons and connections in a human brain.

## 5. Deep Neural Networks

A Deep Neural Network is but an Artificial **Neural Network** with multiple layers between the input and the output. At each layer, the network calculates how probable each output is. A DNN will model complex non-linear relationships when it needs to. With extra layers, we can carry out the composition of features from lower layers.

Typically, a DNN is a feedforward network that observes the flow of data from input to output. It never loops back. Deep Neural Network creates a map of virtual neurons and assigns weights to the connections that hold them together. It multiplies the weights to the inputs to produce a value between 0 and 1. When it doesn’t accurately recognize a value, it adjusts the weights. This is to make parameters more influential with an ulterior motive to determine the correct mathematical manipulation so we can fully process the data.

Two kinds of ANNs we generally observe are-

**Recurrent Neural Networks-**Where data can flow in any direction. We use concepts like LSTM (Long Short-Term Memory) from these in areas like language modeling.**Convolutional Deep Neural Networks-**A deep, feedforward ANN. We use these in areas like analyzing visual imagery, computer vision, and acoustic modeling for ASR (Automatic Speech Recognition)

## 6. Deep Learning Applications

We observe the use of Deep Learning with Python in the following fields-

- Automatic speech recognition.
- Image recognition.
- Visual art processing.
- Natural Language Processing (NLP).
- Drug discovery and toxicology.
- Customer Relationship Management (CRM).
- Recommendation systems.
- Bioinformatics.
- Mobile advertising.
- Image Restoration.
- Financial fraud detection.

**For more applications, refer to 20 Interesting Applications of Deep Learning with Python.**

Before we bid you goodbye, we’d like to introduce you to *Samantha*, an AI from the movie *Her*.

In the film, Theodore, a sensitive and shy man writes personal letters for others to make a living. Samantha is an OS on his phone that Theodore develops a fantasy for. What starts with a friendship takes the form of love.

Will deep learning get us from Siri to Samantha in real life? Well, at least Siri disapproves.

So, this was all in Deep Learning with Python tutorial. Hope you like our explanation.

## 7. Conclusion

Hence, in this Deep Learning Tutorial Python, we discussed what exactly deep learning with Python means. Also, we saw artificial neural networks and deep neural networks in Deep Learning With Python Tutorial. Moreover, we discussed deep learning application and got the reason why Deep Learning. See you again with another tutorial on Deep Learning. Furthermore, if you have any query regarding Deep Learning With Python, ask in the comment tab.

**See also –**

**Learning rules in Neural Network**