Deep learning on Hadoop/Spark -NextML
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Deep Learning on hadoop at galvanize for next ml
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Deep learning on Hadoop/Spark -NextML
- 1. DEEP LEARNING ON HADOOP ADAM GIBSON // DL4J presentation@ Scale out Deep Learning on YARN Next.ML
- 2. deeplearning4j.org presentation@ ADAM / DL4J ● Adam: ○ DL specialist, in residence at Galvanize ○ Head programmer in DL4J project ● SkyMind ○ Commercial support arm of Deeplearning4j ● Deep Learning for Java (DL4J) ○ a distributed, open-source deep-learning framework for the JVM ○ has most major deep neural nets
- 3. deeplearning4j.org presentation@ OVERVIEW ● What is Deep Learning? ● DL major types of Neural Networks: a. Feed-forward NN b. Recurrent NN c. Convolutional NN d. Recursive NN ● Scaling DL4J with HADOOP
- 4. deeplearning4j.org What is DL? Machine Perception, Pattern Recognition
- 5. deeplearning4j.org presentation@ WHY IS DL HARD? We see this Machines see this… (Where’s the cat?)
- 6. deeplearning4j.org presentation@ WHAT’S IT GOOD FOR? ● Recommendation engines: anticipate what you will buy or click ● Anomaly detection: ○ fraud in e-commerce ○ tumors in X-rays ○ loans’ likely to default ● Signal processing: ○ customer lifetime value estimation ○ necessary inventory ○ approaching market crash ● Image
- 7. deeplearning4j.org presentation@ Google, Nov. 17 2014 “TWO PIZZAS SITTING ON A STOVETOP”
- 8. deeplearning4j.org presentation@ FACIAL RECOGNITION & FEATURE HIERARCHY DEEP means “high level abstractions achieved by using model architectures composed of multiple non-linear transformations”, typically traditional Machine Learning algorithms, mostly Neural-Network based (source: Wikipedia)
- 10. deeplearning4j.org presentation@ LEARNING PROPERTIES OF DL ● They can learn unsupervised data ● They save data scientists months of work ● They eat vectors and learn patterns based on occurrence statistics ● They can handle millions of parameters at a time ● After training, they can be deployed for fast classification
- 11. deeplearning4j.org MAJOR TYPES OF DL NETWORKS
- 12. deeplearning4j.org presentation@ FEED FORWARD NN ● Perceptron (1957) ● Back-prop learning ● No loops ● Can have different topologies, typically multi layered
- 13. deeplearning4j.org presentation@ RECURRENT NN ● Loops ● Temporal behavior ● Used for temporal series
- 14. deeplearning4j.org presentation@ CONVOLUTIONAL NN Specific type of Feed-Forward NN, impressive recent results ● Neocognitron (Fukushima’80) ● Improvement (LeCun’98) ● Google Brain (Ng’11)
- 15. deeplearning4j.org presentation@ RECURSIVE NN ● Recursive Autoencoder Deals with variable-length input, has a topological structure, good for NLP
- 16. deeplearning4j.org presentation@ RECURSIVE NN ● Recursive Neural Tensor Network
- 18. deeplearning4j.org presentation@ SGD: Serial vs. Parallel Model Training Data Worker 1 Master Partial Model Global Model Worker 2 Partial Model Worker N Partial Model Split 1 Split 2 Split 3 …
- 19. deeplearning4j.org presentation@ WHAT’S ? ● Everything is in the cloud now, right?
- 20. deeplearning4j.org presentation@ MapReduce vs. Parallel iterative Input Output Processor Processor Processor Superstep 1 Processor Processor Superstep 2 . . . Processor