** Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** This PPT on "Deep Learning with Python" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into the emergence. The various subparts of Data Science, how they are related and How Deep Learning is revolutionalizing the world we live in. This Tutorial covers the following topics: Introduction To AI, ML, and DL What is Deep Learning Applications of Deep Learning What is a Neural Network? Structure of Perceptron Demo: Perceptron from scratch Demo: Creating Deep Neural Nets Deep Learning blog series: https://bit.ly/2xVIMe1 Deep Learning With TensorFlow Playlist: https://goo.gl/cck4hE Instagram:https://www.instagram.com/edureka_lea... Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

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Agenda
❖ Introduction To AI, ML and DL
❖ What is Deep Learning
❖ Applications of Deep Learning
❖ What is a Neural Network ?
❖ Structure of Perceptron
❖ Demo: Perceptron from scratch : Python
❖ Demo: Creating Deep Neural Nets: Python

3. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

4. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

5. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow
AI, ML and DL

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AI, ML and DL
Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible

7. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow
AI, ML and DL
Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible

8. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow
AI, ML and DL
Artificial Intelligence
Machine Learning
Deep Learning
ARTIFICIAL INTELLIGENCE
A technique which enables machines
to mimic human behaviour
MACHINE LEARNING
Subset of AI technique which use
statistical methods to enable machines
to improve with experience
DEEP LEARNING
Subset of ML which make the
computation of multi-layer neural
network feasible

9. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow
What is Artificial Intelligence ?
The theory and development of computer systems able to perform tasks normally requiring human intelligence,
such as visual perception, speech recognition, decision-making and translation between languages.

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What is Machine Learning?
Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal"
algorithms it is the data that "tells" what the "good answer" is
Getting computers to program themselves and also teaching them to make decisions using data
“Where writing software is the bottleneck, let the data do the work instead.”

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What is Machine Learning?
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its
performance on T, as measured by P, improves with experience E.” — Tom Mitchell, Carnegie Mellon University
Basically, Machine Learning is referred to as a type of artificial intelligence (AI) that provides computers with the ability to
learn without being explicitly programmed by exposing them to vast amount of data.

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Drawback of Machine Learning
1. Not useful while working with high dimensional
data.
2. Second major challenge is to tell the computer
what are the features it should look for

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Curse of Dimensionality
• Consider a line of 100 yards and you have dropped a coin somewhere on the line.
• Next, consider you have a square of side 100 yards.
• Lets take it a step ahead by considering a cube of side 100 yards each

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Application of Deep Learning
Speech Recognition Automatic Machine Translation

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Application of Deep Learning
Instant Visual Translation Automated Self Driven Cars

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Application of Deep Learning
Chat-botsPredicting the Future

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Application of Deep Learning
Dream Reading MachineGoogle AI Eye Doctor

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Neurons

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Artificial Neuron Perceptron

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Classification Problem
• Class 1: Inputs having output as 0 that lies below the decision line.
• Class 2: Inputs having output as 1 that lies above the decision line or separator.

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AND GATE: Artificial Neuron

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Role of Weights and Bias
• For a perceptron, there can be one more input called bias
• While the weights determine the slope of the classifier line, bias allows us to shift the line towards left or right
• Normally bias is treated as another weighted input with input value 𝑥_0 = 1

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Training a Perceptron
• By training we are trying to find a line | plane | hyperplane which can correctly separate two classes by
adjusting the weights and biases
• We train the perceptron to respond to each input vector with a corresponding target value of 0 or 1.
• Let’s understand the perceptron training process.

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Training Network Weights
• We can estimate the weight values for our training data using ‘stochastic gradient descent’ optimizer.
• Stochastic gradient descent requires two parameters:
• Learning Rate: Used to limit the amount each weight is corrected each time it is updated.
• Epochs: The number of times to run through the training data while updating the weight.
• These, along with the training data will be the arguments to the function.

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Deep Neural Network

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Deep Neural Network

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MNIST : Dataset

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MNIST : Dataset

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Deep Neural Network

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