Deep Learning With Python Tutorial | Edureka

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

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

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

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

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

9. What is Artificial Intelligence ? 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. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

10. What is Machine Learning? 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.” DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

11. What is Machine Learning? 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. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

12. Drawback of Machine Learning 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 DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

13. Curse of Dimensionality 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 DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

14. Application of Deep Learning Application of Deep Learning Speech Recognition Automatic Machine Translation DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

15. Application of Deep Learning Application of Deep Learning Automated Self Driven Cars Instant Visual Translation DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

16. Application of Deep Learning Application of Deep Learning Predicting the Future Chat-bots DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

17. Application of Deep Learning Application of Deep Learning Google AI Eye Doctor Dream Reading Machine DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

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

19. Artificial Neuron Perceptron Artificial Neuron Perceptron DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

20. Classification Problem 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. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

21. AND GATE: Artificial Neuron AND GATE: Artificial Neuron DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

22. Role of Weights and Bias 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 ?_0 = 1 DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

23. Training a Perceptron 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. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

24. Training Network Weights 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. DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

25. Deep Neural Network Deep Neural Network DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

26. Deep Neural Network Deep Neural Network DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

27. MNIST : Dataset MNIST : Dataset DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

28. MNIST : Dataset MNIST : Dataset DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

29. Deep Neural Network Deep Neural Network DEEP LEARNING CERTIFICATION TRAINING www.edureka.co/ai-deep-learning-with-tensorflow

30. DEMO Copyright © 2019, edureka and/or its affiliates. All rights reserved.