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Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence...
Transcript of Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence...
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Introduction to Artificial IntelligenceDeep Learning - Tensor Flow
Janyl JumadinovaDecember 2, 2016
Credit: Google Workshop
![Page 2: Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence Deep Learning - Tensor Flow Janyl Jumadinova December 2, 2016 Credit: Google Workshop.](https://reader030.fdocuments.net/reader030/viewer/2022041014/5ec471bf7de7b60a1b6d79c1/html5/thumbnails/2.jpg)
Neural Networks
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Neural Networks
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Neural NetworksA fully connected NN layer
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Implementation as Matrix Multiplication
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Non-Linear Data Distributions
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Deep Learning
I Each neuron implements a relatively simple mathematicalfunction.
I y = g(w · x + b)
I The composition of 106 − 109 such functions is powerful.
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Deep Learning
I Each neuron implements a relatively simple mathematicalfunction.
I y = g(w · x + b)
I The composition of 106 − 109 such functions is powerful.
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![Page 10: Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence Deep Learning - Tensor Flow Janyl Jumadinova December 2, 2016 Credit: Google Workshop.](https://reader030.fdocuments.net/reader030/viewer/2022041014/5ec471bf7de7b60a1b6d79c1/html5/thumbnails/10.jpg)
Deep Learning
Book: http://www.deeplearningbook.org/
Chapter 5
“A core idea in deep learning is that we assume that the data wasgenerated by the composition of factors or features, potentially atmultiple levels in a hierarchy.”
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Results get better with:
I more data
I bigger models
I more computation
Better algorithms, new insights and improved methods help, too!
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Results get better with:
I more data
I bigger models
I more computation
Better algorithms, new insights and improved methods help, too!
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Adoption of Deep Learning Tools on GitHub
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Tensor FlowI Operates over tensors: n-dimensional arrays
I Using a flow graph: data flow computation framework
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Tensor FlowI Operates over tensors: n-dimensional arrays
I Using a flow graph: data flow computation framework
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Tensor FlowI Operates over tensors: n-dimensional arrays
I Using a flow graph: data flow computation framework
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Tensor Flow
I 5.7 ← Scalar
I Number, Float, etc.
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Tensor Flow
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Tensor Flow
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Tensor Flow
I Tensors have a Shape that is described with a vector
I [1000, 256, 256, 3]
I 10000 Images
I Each Image has 256 Rows
I Each Row has 256 Pixels
I Each Pixel has 3 values (RGB)
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Tensor Flow
I Tensors have a Shape that is described with a vector
I [1000, 256, 256, 3]
I 10000 Images
I Each Image has 256 Rows
I Each Row has 256 Pixels
I Each Pixel has 3 values (RGB)
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Tensor Flow
Computation is a dataflow graph
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Tensor Flow
Computation is a dataflow graph with tensors
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Tensor Flow
Computation is a dataflow graph with state
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Core TensorFlow data structures and concepts
I Graph: A TensorFlow computation, represented as a dataflowgraph:- collection of ops that may be executed together as a group.
I Operation: a graph node that performs computation on tensors
I Tensor: a handle to one of the outputs of an Operation:- provides a means of computing the value in a TensorFlowSession.
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Core TensorFlow data structures and concepts
I Graph: A TensorFlow computation, represented as a dataflowgraph:- collection of ops that may be executed together as a group.
I Operation: a graph node that performs computation on tensors
I Tensor: a handle to one of the outputs of an Operation:- provides a means of computing the value in a TensorFlowSession.
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Core TensorFlow data structures and concepts
I Graph: A TensorFlow computation, represented as a dataflowgraph:- collection of ops that may be executed together as a group.
I Operation: a graph node that performs computation on tensors
I Tensor: a handle to one of the outputs of an Operation:- provides a means of computing the value in a TensorFlowSession.
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![Page 29: Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence Deep Learning - Tensor Flow Janyl Jumadinova December 2, 2016 Credit: Google Workshop.](https://reader030.fdocuments.net/reader030/viewer/2022041014/5ec471bf7de7b60a1b6d79c1/html5/thumbnails/29.jpg)
Tensor Flow
I Constants
I Placeholders: must be fed with data on execution.
I Variables: a modifiable tensor that lives in TensorFlow’s graphof interacting operations.
I Session: encapsulates the environment in which Operationobjects are executed, and Tensor objects are evaluated.
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Tensor Flow
I Constants
I Placeholders: must be fed with data on execution.
I Variables: a modifiable tensor that lives in TensorFlow’s graphof interacting operations.
I Session: encapsulates the environment in which Operationobjects are executed, and Tensor objects are evaluated.
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Tensor Flow
I Constants
I Placeholders: must be fed with data on execution.
I Variables: a modifiable tensor that lives in TensorFlow’s graphof interacting operations.
I Session: encapsulates the environment in which Operationobjects are executed, and Tensor objects are evaluated.
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![Page 32: Introduction to Artificial Intelligence Deep Learning ... · Introduction to Arti cial Intelligence Deep Learning - Tensor Flow Janyl Jumadinova December 2, 2016 Credit: Google Workshop.](https://reader030.fdocuments.net/reader030/viewer/2022041014/5ec471bf7de7b60a1b6d79c1/html5/thumbnails/32.jpg)
Tensor Flow
I Constants
I Placeholders: must be fed with data on execution.
I Variables: a modifiable tensor that lives in TensorFlow’s graphof interacting operations.
I Session: encapsulates the environment in which Operationobjects are executed, and Tensor objects are evaluated.
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Tensor Flow
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