<p>Deep learning is a subfield of ai and ml which is inspired by the structure of a human brain.</p>
<p>Deep Learning algorithms draw a similar conclusion as a human would by constantly analyzing data with a given local structure called a neural network.</p>
<h2 id="heading-why-is-dl-getting-so-famous">Why is dl getting so famous?</h2>
<ol>
<li><p>Applicability</p>
</li>
<li><p>Performance</p>
</li>
</ol>
<h2 id="heading-difference-between-dl-and-ml">Difference between DL and ML ?</h2>
<h3 id="heading-data-dependency">Data Dependency:</h3>
<p>Deep learning needs more data for learning</p>
<h3 id="heading-hardware-dependency">Hardware Dependency:</h3>
<p>Need GPU for training because complex matrix multiplication is happening.</p>
<h3 id="heading-training-time">Training Time:</h3>
<p>It is high in deep learning.</p>
<h3 id="heading-feature-selection">Feature Selection:</h3>
<p>DL automatically selects relevant features from data.</p>
<h3 id="heading-interpretability">Interpretability:</h3>
<p>DL models are not interpretable because features are selected automatically.</p>
<h2 id="heading-why-has-dl-become-famous-now">Why has DL become famous now?</h2>
<p>DL become so famous now because of:</p>
<ul>
<li><p>Datasets</p>
</li>
<li><p>Frameworks</p>
</li>
<li><p>Community</p>
</li>
<li><p>Architecture</p>
</li>
<li><p>Hardware</p>
</li>
</ul>
<h2 id="heading-ready-to-use-models">Ready to use Models ?</h2>
<ul>
<li><p>Image classification - ResNET</p>
</li>
<li><p>Text classification - BERT</p>
</li>
<li><p>Image Segmenting - UNet</p>
</li>
<li><p>Image translation - Pix2Pix</p>
</li>
<li><p>Object Detection - YOLO</p>
</li>
<li><p>Speech Generation - WaveNET</p>
</li>
</ul>


Deep learning is a subfield of ai and ml which is inspired by the structure of a human brain.

Deep Learning algorithms draw a similar conclusion as a human would by constantly analyzing data with a given local structure called a neural network.

## Why is dl getting so famous?

1. Applicability
    
2. Performance
    

## Difference between DL and ML ?

### Data Dependency:

Deep learning needs more data for learning

### Hardware Dependency:

Need GPU for training because complex matrix multiplication is happening.

### Training Time:

It is high in deep learning.

### Feature Selection:

DL automatically selects relevant features from data.

### Interpretability:

DL models are not interpretable because features are selected automatically.

## Why has DL become famous now?

DL become so famous now because of:

* Datasets
    
* Frameworks
    
* Community
    
* Architecture
    
* Hardware
    

## Ready to use Models ?

* Image classification - ResNET
    
* Text classification - BERT
    
* Image Segmenting - UNet
    
* Image translation - Pix2Pix
    
* Object Detection - YOLO
    
* Speech Generation - WaveNET

What is Deep Learning

Why is dl getting so famous?

Difference between DL and ML ?

Data Dependency:

Hardware Dependency:

Training Time:

Feature Selection:

Interpretability:

Why has DL become famous now?

Ready to use Models ?