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High Performance Analytics with Dask & Tensorflow | AnacondaCON 2017

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Presented at AnacondaCON 2017 by Stan Seibert & Matt Rocklin, Continuum Analytics.

Publié dans : Données & analyses
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High Performance Analytics with Dask & Tensorflow | AnacondaCON 2017

  1. 1. DEEP LEARNING WITH ANACONDA PYTHON, TENSORFLOW & GPUS, OH MY!
  2. 2. #OpenDataScienceMeans #AnacondaCON WHAT IS DEEP LEARNING? •The return of neural networks for machine learning •Many high profile successes with: •Image recognition •Language translation •Speech recognition •Automated image captioning
  3. 3. #OpenDataScienceMeans #AnacondaCON WHAT HAPPENED? (Much) Bigger Training Sets Faster & Specialized Hardware Open Source Tools Improved Algorithms
  4. 4. #OpenDataScienceMeans #AnacondaCON WHAT IS A NEURAL NETWORK? •A very simple computation unit connected in a large mesh •Very flexible •Very trainable (given sufficient training data)
  5. 5. #OpenDataScienceMeans #AnacondaCON NEURAL NETWORKS: NODE
  6. 6. #OpenDataScienceMeans #AnacondaCON NEURAL NETWORKS: LAYER
  7. 7. #OpenDataScienceMeans #AnacondaCON NEURAL NETWORKS: NETWORK ReLU ReLU ReLU ReLU
  8. 8. #OpenDataScienceMeans #AnacondaCON THE DEEP LEARNING SOFTWARE STACK MULTI-CORE CPU GPU MANY-CORE CPU (XEON PHI) HARDWARE MKL 2017 CUDNNPRIMITIVES TENSORFLOWTHEANOPYTORCHTENSOR MATH NEURAL NETWORKS KERAS TFLEARNCAFFE ...and many others MIOPEN
  9. 9. #OpenDataScienceMeans #AnacondaCON DEEP LEARNING WITH ANACONDA Available in Anaconda today: • Theano • TensorFlow (CPU) Coming very soon: • TensorFlow (GPU w/ cuDNN) • Keras • ...and more
  10. 10. #OpenDataScienceMeans #AnacondaCON DEEP LEARNING IN THE JUPYTER NOTEBOOK Defines a simple model in Keras to recognize handwritten digits
  11. 11. #OpenDataScienceMeans #AnacondaCON DEEP LEARNING IN THE JUPYTER NOTEBOOK Trained to 98% accuracy in 4 minutes using a single NVIDIA GTX 1080 GPU
  12. 12. #OpenDataScienceMeans #AnacondaCON DEEP LEARNING IN THE JUPYTER NOTEBOOK Trained to 98% accuracy in 4 minutes using a single NVIDIA GTX 1080 GPU
  13. 13. #OpenDataScienceMeans #AnacondaCON CONCLUSION Now is a great time to experiment with Deep Learning: • Prepare your data with your favorite Python libraries • Create models and run training experiments in the Jupyter Notebook • Visualize and understand your training results right in the notebook environment • Look for more Deep Learning packages coming to Anaconda! (GPU acceleration, Keras, and more)
  14. 14. QUESTIONS?

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