This document provides an introduction to deep learning through a series of sections on topics like the ontology of AI, deep learning in action demonstrated through examples, the differences between BA, ML and AI, hands-on examples of supervised learning in RStudio, an overview of neural networks and backpropagation, a comparison of different machine learning algorithms, and applications of deep learning with examples in areas like computer vision. It also profiles some of the leading figures in deep learning and artificial intelligence research.

1. AN INTRODUCTION TO DEEP LEARNING
BY
SUMIT JHA :: 2014FPM003

2. ONTOLOGY OF AI
• “CAN MACHINE THINK?”
• “ARE THERE IMAGINABLE DIGITAL COMPUTERS WHICH
WOULD DO WELL IN IMITATION GAME ?”
• “CAN ONE ARGUE AGAINST ALL MAJOR OBJECTIONS TO
THE PROPOSITION THE ‘MACHIN CAN THINK’ ?”
12/3/2018 2https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence/

3. DEEP LEARNING IN ACTION
12/3/2018 3https://www.kaggle.com/c/ml1718-fashion-mnist
“Will play a short (2 minutes) video of DL execution in R Studio”

4. BA OR ML OR AI ???
12/3/2018 4https://365datascience.com/blog/

5. HANDS ON :: LET US ML (SUPERVISED LEARNING)
• IDE (R-STUDIO) SETUP,
REQUIRED LIBRARIES &
BASIC DATA STRUCTURE
• THREE WISE MEN : = , <- , %>%
& TWO TOUGH FIGHTERS : =
VS ==
• EVER CONFUSING – “Conquer
The Confusion Matrix”
12/3/2018 5
• Remove target variable from the
training data
• Reduce the amount of redundant
information
• Convert categorical information
to a numeric format
• Split dataset into testing and
training subsets
• Convert the cleaned data-frame
to a matrix
https://www.kaggle.com/rtatman/machine-learning-with-xgboost-in-r/notebook

6. WHAT ALL ML CAN & CAN’T DO ??
12/3/2018 6https://www.techleer.com/articles/203-machine-learning-algorithm-backbone-of-emerging-technologies/

7. HANDS ON :: The (maybe)First ML R Code
LM (package :: stats)
lm.r = lm(Income~Rent,
training_data)
predict(lm.r,test_data,
interval="confidence")
error matrices
ANN (package :: neuralnet)
ann.r = neuralnet(rating ~
calories + protein, training_data ,
hidden=3, threshold=0.001)
compute(ann.r, test_data)
error matrices
12/3/2018 7

8. ANN : THE BUILDING BLOCKS OF DEEP LEARNING (R Code)
1. f <- function(x)((x-min(x))/(max(x)-min(x)))
2. train_set_scaled <-apply(train_set, 2,f)
3. dev_set_scaled <- apply(dev_set, 2,f)
4. ann.r.withscale <- neuralnet(avg_training_score ~
no_of_trainings+age+previous_year_rating+length_of_service+KPI
s_met..80.+awards_won.+is_promoted, data = train_set_scaled)
5. pred_ann_dev <- compute(ann.r.withscale, dev_set_scaled)
12/3/2018 8

9. NEURAL NETWORKS OVERVIEW
12/3/2018 9https://www.cnblogs.com/ericxing/p/3860707.html /

10. BACKPROP*** (OPTIONAL)
12/3/2018 10https://twitter.com/KirkDBorne

11. TOP 10 MLALGO vs The NNs
Linear Algorithms:
• Algorithm 1: Linear Regression
• Algorithm 2: Logistic Regression
• Algorithm 3: Linear Discriminant Analysis
Nonlinear Algorithms:
• Algorithm 4: Naive Bayes
• Algorithm 5: Classification and Regression Trees
• Algorithm 6: K-Nearest Neighbors
• Algorithm 7: Learning Vector Quantization
• Algorithm 8: Support Vector Machines
Ensemble Algorithms:
• Algorithm 9: Bagged Decision Trees and Random Forest
• Algorithm 10: Boosting and AdaBoost
12/3/2018 11https://www.datasciencecentral.com/forum/topics/0-top-machine-learning-algorithms

12. ACCURACY OF “Many Classification Algorithms Using R” ON CAR EVALUATION DATASET
Naive Bayes : 0.8738
Logistic Regression : 0.9456
Linear Discriminant Analysis : 0.901
Mixture Discriminant Analysis : 0.9034
Regularized Discriminant Analysis : 0.8779
Flexible Discriminant Analysis : 0.8999
Neural Network : 0.9878
Support Vector Machine : 0.9688
k-Nearest Neighbors : 0.923
CART : 0.9439
C4.5 : 0.963
Boosted C5.0 : 0.9965
PART : 0.9826
Random Forest : 0.9988
Bagging CART : 0.9979
Gradient Boosted Machine : 0.70
12/3/2018 12https://github.com/surajvv12/17_Classification/commit/68b37fb1e2012d14391f14ed5fa53761f4b0a7cf

13. HANDS ON ( R – CODE FOR BASIC DEEP LEARING)
require(keras)
1. baseline_model <- keras_model_sequential() %>% layer_dense(units = 4, activation
= "relu", input_shape = 47) %>% layer_dense(units = 2, activation = "softmax")
2. baseline_model %>% compile( optimizer = "adam", loss =
"categorical_crossentropy",metrics = list("accuracy"))
3. baseline_history <- baseline_model %>% fit( train_data, train_labels, epochs = 5,
batch_size = 512, validation_data = list(val_data, val_labels),verbose = 2)
4. predictions <- baseline_model %>% predict(as.matrix(test))
12/3/2018 13
https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

14. HANDS ON (MAGIC OF DEEP NET :: REAL WORLD SOLUTION)
12/3/2018 14https://in.axpcampus.com/AnalyzeThis/campusactivity/leaderboard.php

15. R vs PYTHON
• EASE (R)
• IDE for Windows (R)
• VISUALIZATION (R)
• SPEED (PYTHON)
• COMMUNITY(PYTHON)
• SALARY (PYTHON)
12/3/2018 15https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis

16. “Applications and Value of Deep Learning”
12/3/2018 16https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning

17. AUTOMATED MACHINE LEARNING
• SapFix – AUTO DEBUGGING :: Facebook
• NNI – NET SEARCH AND HYPERPARAMETER TUNING :: Microsoft
• Driverless AI – STATE-OF-ART AUTOMATION ON GPU :: H2O
• AUTO ML - ON CLOUD :: Google
12/3/2018 17https://www.kdnuggets.com/2018/07/automated-machine-learning-vs-automated-data-science.html

18. “A Deeper Dive into Deep Learning”
• “Multi-layer Perceptrons”
• “Deep Boltzmann machines”
• “Self-Organizing Maps”
• “Feed-forward Neural
Networks”
• “Convolutional Neural
Networks”
• “Recurrent Neural Network” or
LSTM
12/3/2018 18
https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

19. ROCKstars of DEEP LEARNING AND AI
• Geoffrey Hinton :: Godfather of AI
• Ian Goodfellow :: GAN
• Andrej Karpathy :: Human Vs AI
• Satya Nadella :: “ Calls for AI Regulation of FR Technology ”
12/3/2018 19

20. SOME GOOD RESOURCES
• https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWT
QDNU6R1_67000Dx_ZCJB-3pi
• https://www.datacamp.com/community/tutorials
• https://stackoverflow.com
• https://www.kaggle.com
• https://www.deeplearningbook.org *
• https://www.analyticsvidhya.com/blog/2018/09/heroes-deep-
learning-top-takeaways-andrew-ng-interview-series/
12/3/2018 20

21. THANK YOU 
12/3/2018 21