Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Deep learning introduction

93 views

Published on

Presentation on the Deep Learning Introduction

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Deep learning introduction

  1. 1. DEEP LEARNING SHASHI JEEVAN M P
  2. 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. 3. DEFINITION • Artificial Intelligence • Machine Learning • Deep Learning
  4. 4. 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
  5. 5. 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)
  6. 6. LINEAR REGRESSION • Best fit line
  7. 7. LINEAR REGRESSION Best fit line Y = m * X + B Minimize Errors using Least Squares method
  8. 8. APPLICATIONS • Netflix • Uber https://eng.uber.com/michelangelo/ • Amazon shopping
  9. 9. TECHNIQUES • Loss Function • Gradient Descent • Back propagation
  10. 10. NETWORK ARCHITECTURES • https://becominghuman.ai/cheat-sheets-for-ai-neural- networks-machine-learning-deep-learning-big-data- 678c51b4b463
  11. 11. BUILDING MODEL • Layers are organized – Convolutional, Recurrent • Activation – Sigmoid, ReLU, Softmax • Loss function • Optimizer
  12. 12. TRAINING • Batch • Epoch • Training Set/Testing Set • Loss • Accuracy
  13. 13. INFERENCE • Saved model is used • Input is processed and a prediction is made
  14. 14. MODEL EXCHANGE STANDARDS
  15. 15. DEMO • Training • Keras MNIST • Inference • http://myselph.de/neuralNet.html
  16. 16. RESOURCES • https://www.analyticsvidhya.com • https://www.coursera.org/ • https://machinelearningmastery.com/handwritten-digit- recognition-using-convolutional-neural-networks-python- keras/

×