creating a machine learning model on the cloud
TRANSCRIPT
Building Your First Machine Learning Model
With IBM Data Science Experience
By Aoun Lutfi and Kunal MalhotraIBM Cloud Developer [email protected], [email protected]
Agenda
1. Introduction to Data Science
2. Introduction to IBM Data Science Experience
3. Introduction to Tensorflow
4. Hands-On
IBM Confidential
3IBM Confidential
We are surrounded by, and are constantly creating
digital data. Whether it’s in emails we write, photos
we take, or where we drive; almost everything
creates data today. Data Science is the discipline of
acquiring, finding insights, and sharing discoveries in
all this data.
CRISP-DMCross Industry Standard Process for Data Mining
Machine Learning
Concept
Methodology
Machine Learning
Neural Networks
Perceptron
Activation Function
Training – Backward Propagation
1. Initialize the weights and bias randomly.
2. Fix the input and output.
3. Forward pass the inputs. calculate the cost.
4. compute the gradients and errors.
5. Backprop and adjust the weights and bias accordingly
Convolutional Neural Networks
IBM Data Science Experience
Data Science Experience
Data Science Experience offers the opportunity to work with big data on the cloud. Use Python or R on
Spark to process big data, build models, and deploy models. Data Science Experience allows you to
easily collaborate on descriptive, prescriptive, predictive analytics, and Machine Learning on the cloud.
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Data Science Experience
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Data Science Experience
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Introductionto TensorFlow
PLACE IMAGEHERE
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TensorFlow
Originally developed by the Google Brain Team within Google'sMachine Intelligence research organisation
TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives.
An open source software library fornumerical computation using data flowgraphs
Tensor?
Simply put: Tensors can be viewed as a
multidimensional array of numbers. This means
that:
• A scalar is a tensor,
• A vector is a tensor,
• A matrix is a tensor
• ...
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Data Flow Graph?
Computations are represented asgraphs:
• Nodes are the operations(ops)
• Edges are theTensors (multidimensional arrays)
Typicalprogram consists of 2 phases:
• construction phase: assembling a graph (model)
• execution phase: pushing data through thegraph
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Neural Networks? DeepLearning?
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● Neural Networks are represented by the lower figure, not the
top one....
● Link:
Tinker with a Neural Network inYour Browser
Presentation title (Go to View > Master to edit) 8Source: https://www.udacity.com/course/deep-learning--ud730
Presentation title (Go to View > Master to edit) 9Source: https://www.udacity.com/course/deep-learning--ud730
Presentation title (Go to View > Master to edit) 15Source: https://www.udacity.com/course/deep-learning--ud730
Presentation title (Go to View > Master to edit) 16Source: https://www.udacity.com/course/deep-learning--ud730
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Why would you use NN / Deep Learning?
• Neural Networks (NNs) are universal function
approximators that work very well with huge
datasets
• NNs / deep networks do unsupervised feature
learning
• Track record, being SotA in:
• image classification,
• language processing,
• speech recognition,
• ...
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Why TensorFlow?
There are a lot of alternatives:
● Torch
● Caffe
● Theano (Keras, Lasagne)
● CuDNN
● Mxnet
● DSSTNE
● DL4J
● DIANNE
● Etc.
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TensorFlow has the largestcommunity
Sources: http://deliprao.com/archives/168
http://www.slideshare.net/JenAman/large-scale-deep-learning-wit
h-tensorflow
Runs on CPUs, GPUs, TPUs over one or more
machines, but also on phones(android+iOS) and
raspberrypi’s...
TensorFlow is very portable/scalable
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TensorFlow is more than an R&D project
• Specific functionalities for deployment (TF Serving /
CloudML)
• Easier/more documentation (for more general public)
• Included visualization tool(Tensorboard)
• Simplified interfaces likeSKFlow
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Hands On Lab
Building your first Machine Learning model on IBM Data Science Experience.
Sign in to IBM Cloud on: ibm.biz/Intro2MLonDSX
Access Data Science Experience on: datascience.ibm.com
GitHub Link: github.com/aounlutfi/building-first-ML-model
MNIST CNN using
Tensorflow
Build an CNN to classify handwritten digits using the MNIST dataset.