The document discusses deep learning, a subset of machine learning utilizing artificial neural networks that learn from large data sets. It highlights the advantages of deep learning, such as generating new features and working with unsupervised learning, while also noting its disadvantages, including high computational costs and lack of transparency. Additionally, it mentions famous deep neural networks like convolutional neural networks and recurrent neural networks.

1. Agenda
By khalid Shaikh
Presented to :- Sir Mudasar Soomro

2. Agenda
 AI,ML,DL
 Deep Neural network
 Working structure of deep neural network
 Pros and cons of DL
 Application of DKNW
 Famous deep learning neural network

5. Deep learning
Deep learning is a subset of machine learning where artificial neural networks,
algorithms inspired by the human brain, learn from large amounts of data.
Deep learning is the one category of machine learning that emphasizes training
the computer about the basic instincts of human beings.
It is a prime technology behind the concept of virtual assistants, facial
recognition, driverless cars, etc.
The working of deep learning involves training the data and learning from the
experiences.

7. Deep Learning Method

8. How deep learning works

9. Deep Learning Examples
Open AI

11. Advantage of Deep learning
Ability to generate new features from the limited available training data sets.
Its ability to work on unsupervised learning techniques helps in generating
actionable and reliable task outcomes.
It reduces the time required for feature engineering, one of the tasks that
requires major time in practicing machine learning. (Speaking of machine
learning.
With continuous training, its architecture has become adaptive to change and is
able to work on diverse problems.

12. Disadvantage of Deep learning
The cost of computational training significantly increases with an increase in
the number of datasets.
Lack of transparency in fault revision. No intermediate steps to provide the
arguments for a certain fault. In order to resolve the issue, a complete algorithm
gets revised.
Need for expensive resources, high-speed processing units and powerful GPU’s
for training to the data sets.

13. Famous Deep Neural Network
• Convolutional Neural Networks
• Deep Belief Networks
• Support Vector Machine
• K-means clustering
• Linear regression
• Recurrent Neural Network(RNN)

14. ELAN MUSK

16. Open Ai Replace The Deep Learning Concept