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Introduction to Deep Learning

Talk given by Jitender Chauhan, Sr. Software Engineer at Salesforce, at Tech Time Meetup in June 2016


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Introduction to Deep Learning

  1. 1. Introduction to Deep Learning Jitender Chauhan Senior Engineer jsinghchauhan@salesforce.com
  2. 2. Introduction to Learning What is Machine learning ? Field of study that gives computers the ability to learn without being programmed explicitly By: Arthur Samuel 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 By: Tom Mitchell
  3. 3. How Machine learning is different from Artificial Intelligence ? ● Artificial Intelligence is the study of how to create intelligent agents. ● It is how to program a computer to behave as an intelligent agent (say, a person). ● This does not have to involve learning or induction at all. ● Artificial Intelligence uses models built by Machine Learning
  4. 4. Machine Learning VS Artificial Intelligence
  5. 5. Evolution of Machine Learning(learning from data )
  6. 6. Supervised Learning ● Learning from Labelled Data Ex: ● Predicting price of a house using available data set containing house area and house price. ● Deciding whether to approve/reject credit card application using existing customer records. ● Predicting whether a message is spam or non spam using existing labelled data set
  7. 7. Unsupervised Learning ● Learning from unlabelled data ● Grouping data in categories based on similarities between them Ex: ● Summarize the news for the past month ànd cluster them ● Fraud Detection ● Anomaly Detection
  8. 8. ● Learning with labelled and unlabelled Data ● This for example can be used in Deep belief networks, where some layers are learning the structure of the data (unsupervised) and one layer is used to make the classification Semi supervised Learning Reinforcement Learning ● Reinforcement learning is the problem of getting an agent to act in the world so as to maximize its rewards. Ex ● Consider teaching a dog a new trick: you cannot tell it what to do, but you can reward/punish it if it does the right/wrong thing. It has to figure out what it did that made it get the reward/punishment, which is known as the credit assignment problem.
  9. 9. ● filters information by using the recommendations of other people. Ex: ● A person who wants to see a movie, might ask for recommendations from friends. The recommendations of some friends who have similar interests are trusted more than recommendations from others. This information is used in the decision on which movie to see. Apps: ● Recommendation System Collaborative Learning (Filtering)
  10. 10. ● On-line learning algorithms take an initial guess model and then picks up one-one observation from the training population and recalibrates the weights on each input parameter. Ex: ● Learning User’s interest and behave accordingly Online Learning
  11. 11. Deep Learning
  12. 12. Limitations with classical machine learning algorithms
  13. 13. Limitations with classical machine learning algorithms Cont...
  14. 14. Neural Networks & Limitations
  15. 15. Working of Neurons in Human Brain
  16. 16. ● The Branch of study that tries to mimic Human Brain ● The main concept in deep learning algorithms is automating the extraction of representations from the data ● Deep learning algorithms use a huge amount of unsupervised data to automatically extract complex representation. Deep Learning
  17. 17. ● Image Classification ● Speech Recognition ● Natural Language Processing ● Optical Character Recognition Deep Learning Applications
  18. 18. Demo
  19. 19. thank y u

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