This document provides an overview of deep learning including definitions of machine learning, types of machine learning, and techniques used in deep learning such as neural networks. It discusses applications of deep learning in companies like Netflix and Uber. Finally, it provides resources for learning more about deep learning.

1. DEEP LEARNING
SHASHI JEEVAN M P

2. SPEAKER
• M. Tech. from IIT Kharagpur
• Inventor of US Patent # 6,609,084, Issue Date: August 19, 2003
• Two decades of experience in Software Industry
• My Blog (https://shashijeevan.com)

3. DEFINITION
• Artificial Intelligence
• Machine Learning
• Deep Learning

7. MACHINE LEARNING DEFINITION
• Machine Learning: Field of study that gives computers the
ability to learn without being explicitly programmed.
~Arthur Samuel, 1959
• Well posed Learning Problem: 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, 1998

8. MACHINE LEARNING TYPES
• Supervised Learning – Naïve Bayes, SVM, Artificial Neural Nets,
Random Forest
• Unsupervised Learning – K Means Clustering
• Reinforcement Learning – Model Free Learning, MDP, Q
Learning
• Semi Supervised Learning – GAN (New)

9. LINEAR REGRESSION
• Best fit line

10. LINEAR REGRESSION
Best fit line
Y = m * X + B
Minimize Errors using Least Squares method

11. APPLICATIONS
• Netflix
• Uber
https://eng.uber.com/michelangelo/
• Amazon shopping

18. TECHNIQUES
• Loss Function
• Gradient Descent
• Back propagation

22. NETWORK ARCHITECTURES
• https://becominghuman.ai/cheat-sheets-for-ai-neural-
networks-machine-learning-deep-learning-big-data-
678c51b4b463

26. BUILDING MODEL
• Layers are organized – Convolutional, Recurrent
• Activation – Sigmoid, ReLU, Softmax
• Loss function
• Optimizer

27. TRAINING
• Batch
• Epoch
• Training Set/Testing Set
• Loss
• Accuracy

28. INFERENCE
• Saved model is used
• Input is processed and a prediction is made

29. MODEL EXCHANGE STANDARDS

30. DEMO
• Training
• Keras MNIST
• Inference
• http://myselph.de/neuralNet.html

31. RESOURCES
• https://www.analyticsvidhya.com
• https://www.coursera.org/
• https://machinelearningmastery.com/handwritten-digit-
recognition-using-convolutional-neural-networks-python-
keras/