Deep learning is a type of machine learning that uses artificial neural networks with multiple layers to extract higher-level features from data. It can learn complex patterns within data and handle large numbers of inputs and outputs. Deep learning is implemented using deep neural networks with multiple hidden layers that learn representations of data through backpropagation. The goal of deep learning is to develop systems that can perform tasks requiring human intelligence like visual perception and speech recognition.

1. Deep Learning

2. Artificial Intelligence
• AI is implemented in machines to perform tasks that require human intelligence
• some examples of AI in real life:
• Robots:
• Some of the other examples include self-driving cars or Amazon
Alexa or even Siri. One other important application can be speech
recognition
• Image Recognition
• Its also Used in Deep Oceans .
• If a driving a car if u r in over speed chalan is send to your mailing
address .
• We use camera and its automatically sends you a message .

3. Cont.
• Artificial Intelligence accomplish how human brain will thinks and
how human brain learns decide and work to solve a problem .
• Output of this is study as the basis of development of intelligent
software and systems.
• Major Goal of AI is to have systems or software that can imitate the
human behavior , the way they think , decide and work to solve
problem.
• That’s what machine need to do that.

4. Applications
• Speech Recognition
• Natural Language
• Image Recognition
• Etc….
• Self Driving cars
• Youtube search list of your own

5. To achieve AI
• Machine Learning :
• We started using Neural Network concepts in ML in 2000s
• Deep learning over comes the limitations of ML and is implemented
from 2010

6. Unit-I
• UNIT I : Deep Learning Fundamentals
• Introduction to deep learning and biological motivation, Applications
of deep learning ,suitable problems for deep learning, Neurons,
Neural Network, Activation Function

8. Machine Learning
• Machine learning enables a machine to automatically learn from
data, improve performance from experiences, and predict things
without being explicitly programmed.
• ML is a subset of AI and provides a ability to learn without beibg
explicitly Programmed.

9. Procedure :
• We need to train machine on our data
• Based on the data machine can understand and think for the
output
• When new input is given to a machine it will be able to predict
the output.
• Types of ML:

10. Types:
• 1) Supervised Learning
• Supervised learning is a type of machine learning method in
which we provide sample labeled data to the machine learning
system in order to train it, and on that basis, it predicts the
output.
• Supervised learning can be grouped further in two categories of
algorithms:
• Classification
• Regression

11. • Unsupervised Learning
• Unsupervised learning is a learning method in which a machine
learns without any supervision.
• The training is provided to the machine with the set of data that
has not been labeled, classified, or categorized, and the
algorithm needs to act on that data without any supervision. The
goal of unsupervised learning is to restructure the input data
into new features or a group of objects with similar patterns.
• Clustering
• Association

12. Reinforcement Learning
Reinforcement learning is a feedback-based learning method
or by learning experience , in which a learning agent gets a
reward for each right action and gets a penalty for each wrong
action.
The agent learns automatically with these feedbacks and
improves its performance.
In reinforcement learning, the agent interacts with the
environment and explores it. The goal of an agent is to get the
most reward points, and hence, it improves its performance.

13. Limitations
• Cannot Deal with large numbers of inputs.
• Unable to solve critical problems
• Features Extractions
• Image predictions
• Hand writing predictions

14. Difference

15. Deep Learning :
• This models are capable to focus on right
Features by themselves requiring little
guidance from the programmers .
• By this they can generate the features on
which the outcome will depend on.
• It allows large number of inputs and outputs.
• Deep Learning is implemented with help of
Neural networks.
• Deep learning is implemented with the help of
Deep Networks – with multiple hidden layers.
• Neural networks are nothing but neurons –
Brain cells

16. Hidden nodes:
We have some inputs from input layer after that some process happens
and it will go to next node, or hidden layer 1 and to hidden layer 2
where some functions will happen in hidden layers and comes to
output node .
Output is verified with actual output if not valid come back to input
layer and with changing waits process goes on again this process is
called as back propagation and process continues until valid output is
generated.
They can be multiple hidden layers .

18. Deep Learning
• A type of machine learning based on artificial neural networks in
which multiple layers of processing are used to extract progressively
higher-level features from data.
• It is capable of learning complex patterns and relationships
within data.
• Deep learning is the branch of machine learning which is based
on artificial neural network architecture. An artificial neural
network or ANN uses layers of interconnected nodes called
neurons that work together to process and learn from the input
data.