Deep learning is a subfield of machine learning focused on algorithms inspired by the brain's structure, particularly artificial neural networks. It encompasses various architectures like feedforward and convolutional neural networks, each with specific applications in fields such as computer vision and natural language processing. Deep learning libraries like TensorFlow and PyTorch facilitate the development of deep learning applications by providing reusable code.

1. Deep Learning
Presented by-
Saurav Jaiswal
A8805217007

2. What is Deep Learning ?
Deep Learning is a subfield of machine
learning concerned with algorithms
inspired by the structure and function of
the brain called artificial neural networks.

3. Deep Learning Algorithms/Models
The most important Deep Learning algorithms and
architectures concieved over the years for use in a variety of
applications such as Computer Vision and Natural Language
Processing.
Some of them are used more frequently than others and each
one has its own streghth and weeknesses.

4. Neural Networks
Just like the human brain, Neural Network consist of Neurons. Each
Neuron receives signals as an input, multiplies them by weights, sums
them up and applies a non-linear function. These neurons are stacked next
to each other and organized in layers.

5. Feedforward Neural Networks
Feedforward Neural Networks are usually fully connected, which means
that every neuron in a layer is connected with all the other neurons in
the next layers. The described structure is called Multilayer Perceptron.
Single-layer perceptron can only learn linearly separable patterns, but a
multilayer perceptron is able to learn non-linear relationships between
the data.

6. Convolutional Neural Network
Convolutional Neural Networks employ a function
called convolution The concept behind them is that instead of
connecting each neuron with all the next ones, we connect it with only
a handful of them (the receptive field).
In a way, they try to regularize feedforward networks to avoid
overfitting (when the model learns only pre-seen data and can’t
generalize), which makes them very good in identifying spatial
relationships between the data.

7. That’s why their primary use case is Computer Vision and applications
such as image classification, video recognition, medical image analysis
and self-driving cars where they achieve literally superhuman
performance.
They are also ideal to combine with other types of models such as
Recurrent Networks and Autoencoders. One such example is Sign
Language Recognition.

8. Autoencoders
Autoencoders are mostly used as an unsupervised algorithm and its main
use-case is dimensionality reduction and compression. Their trick is that
they try to make the output equal to the input. In other works, they are
trying to reconstruct the data.
They consist of an encoder and a decoder. The encoder receives the input
and it encodes it in a latent space of a lower dimension. The decoder takes
that vector and decodes it back to the original input.

9. That way we can extract from the middle of the network a
representation of the input with fewer dimensions. Genius, right?
Of course, we can use this idea to reproduce the same but a bit
different or even better data (training data augmentation, data
denoising, etc.

10. Deep Learning Libraries
Deep Learning libraries provide pre-written, professional-quality code
that you can use for our own projects. ... For a commercial-grade deep
learning application, the best libraries are-
• TensorFlow
• Keras
• Caffe
• PyTorch
• Theano
• Caffe2