ARTIFICIALINTELEGENCE
TENSORFLOW
Start
whAt we talking about?
History of it
TensorFlow?
Its Progression
What it supports
Elaborarion
Pros and Cons
Live example
What is it made of
Programme aspect
Training the model
Preparing Data
Data collection
Uses of Tensorflow
The result
Tensor flow?
what is tensorflow?
DEFFINITION
WHAT IS IT?
TensorFlow is an open-source end-to-end platform for creating Machine Learning applications. It is a symbolic software library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks.
History of TensorFlow
A couple of years ago, deep learning started to outperform all other machine learning algorithms when giving a massive amount of data. Google saw it could use these deep neural networks to improve its services. They built a Library called TensorFlow to let researchers and developers work together on an AI model. Once developed and scaled, it allows lots of people to use it.
EVOLOUTION OF TENSORFLOW
2018
2017
2019
TensorFlow 1.0 was released for machine learning in JavaScript.
Kubeflow is released for the operation and deployment of TensorFlow on Kubernetes.
TensorFlow 2.0 is released which adds a number of components to TensorFlow.
PROS OF TENSORFLOW
Scalable: TensorFlow is not limited to one specific device.
Open Source Platform: It is available free of cost to anyone who wants to work with this.
Graphs: TensorFlow has a better data visualization power than any other available library.
Debugging: TensorFlow has Tensorboard which allows easy debugging of nodes.
Parallelism: TensorFlow employs GPU and CPU systems for its functioning.
cons of tensorflow
No windows support: Windows has a very limited set of features for Windows users.
Slow: It is comparatively slower and less usable compared to its competing frameworks.
Architectural limitation: TensorFlow’s architecture allows only execution of models and doesn’t allow its training.
Dependency: Even though TensorFlow reduces the size of the program and makes it user-friendly, it adds a layer of complexity to it.
Frequent Updates: TensorFlow undergoes frequent updates making it overhead for a user to time to time uninstall and reinstall it
CAN YOU ELABORATE MORE?
How does it work. Why is it called TensorFlow. Where can i run it.
More info
Where can TensorFlow run?
Why is it called TensorFlow?
How TensorFlow Works?
TensorFlow is available on 64-bit Linux, MacOS, Windows, and mobile computing platforms including Android and iOS. Its flexible architecture allows for the easy deployment of computation
it is called TensorFlow because the tensor goes in it, flows through a list of operations, and then it comes out the other side.
In a nutshell, data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.
What programming interfaces support TensorFlow?
There currently exist two programming interfaces, in C++ and Python, that permit interaction with the TensorFlow back end.
TENSORFLOW COMPONENTS
gRAPHS
tENSOR
A tensor is a vector or matrix of n-dimensions that represents all types of data. All values in a tensor hold identical data type with a known shape.
TensorFlow makes use of a graph framework. The graph gathers and describes all the series computations done during the training.
TECHNICAL ELABORATION
THE TENSORFLOW PROGRAMMING MODEL
THE TENSORFLOW PROGRAMMING MODEL
machine learning algorithms are represented as a computational graph. A computational or dataflow graph is a form of directed graph where vertices or nodes describe operations, while edges represent data flowing between these operations.
live example
Coding Example using TENSORFLOW
The next codes perform 4 important steps:- 1- Collecting Data 2- Prepareing Data 3- Training the Model 4- Evaluating the Model
collecting Data
The data used in this Example is a list of car objects like this:
preparing data
When preparing for machine learning, it is always important to Remove the data you don't need. This can be done by iterating (looping over) your data with a Map function .
converting into tensors
To use TensorFlow, input data needs to be converted to tensor data:
training the model
After preparing the data, training comes along, the previous two steps with this one will iterate for a significant amount of times:
plot the result
Testing the final result and mesure the accuracy of the model
Most common uses of tensorflow
02 Text-Based applications
01 Voice/sound recognition
Further popular uses of TensorFlow are text based applications such as sentimental analysis (CRM, Social Media), Threat Detection (Social Media, Goverment) and Fraud Detection (Insurance, Finance)
One of the most well-known uses of TensorFlow are Sound based applications. With the proper data feed, neural networks are capable of understanding audio signals.
Most common uses of tensorflow
03 image recognition
05 Video detection
04 Time series
Mostly used by Social Media, Telecom and Handset Manufacturers, Face recognition, Image search, Motion Detection, Machine Vision and Photo Clustering can be used also in Automotive, Aviation and Healthcare industries. Image Recognition aims to recognize and identify people and objects in images as well as understanding the content and context.
TensorFlow neural networks also work on video data. This is mainly used in Motion Detection, Real-Time Thread Detection in Gaming, Security, Airports and UX/UI fields.
TensorFlow Time Series algorithms are used for analyzing time series data in order to extract meaningful statistics. They allow forecasting non-specific time periods in addition to generate alternative versions of the time series.
THANKS!
RETROFUTURE
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Created on October 29, 2022
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Transcript
ARTIFICIALINTELEGENCE
TENSORFLOW
Start
whAt we talking about?
History of it
TensorFlow?
Its Progression
What it supports
Elaborarion
Pros and Cons
Live example
What is it made of
Programme aspect
Training the model
Preparing Data
Data collection
Uses of Tensorflow
The result
Tensor flow?
what is tensorflow?
DEFFINITION
WHAT IS IT?
TensorFlow is an open-source end-to-end platform for creating Machine Learning applications. It is a symbolic software library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks.
History of TensorFlow
A couple of years ago, deep learning started to outperform all other machine learning algorithms when giving a massive amount of data. Google saw it could use these deep neural networks to improve its services. They built a Library called TensorFlow to let researchers and developers work together on an AI model. Once developed and scaled, it allows lots of people to use it.
EVOLOUTION OF TENSORFLOW
2018
2017
2019
TensorFlow 1.0 was released for machine learning in JavaScript.
Kubeflow is released for the operation and deployment of TensorFlow on Kubernetes.
TensorFlow 2.0 is released which adds a number of components to TensorFlow.
PROS OF TENSORFLOW
Scalable: TensorFlow is not limited to one specific device.
Open Source Platform: It is available free of cost to anyone who wants to work with this.
Graphs: TensorFlow has a better data visualization power than any other available library.
Debugging: TensorFlow has Tensorboard which allows easy debugging of nodes.
Parallelism: TensorFlow employs GPU and CPU systems for its functioning.
cons of tensorflow
No windows support: Windows has a very limited set of features for Windows users.
Slow: It is comparatively slower and less usable compared to its competing frameworks.
Architectural limitation: TensorFlow’s architecture allows only execution of models and doesn’t allow its training.
Dependency: Even though TensorFlow reduces the size of the program and makes it user-friendly, it adds a layer of complexity to it.
Frequent Updates: TensorFlow undergoes frequent updates making it overhead for a user to time to time uninstall and reinstall it
CAN YOU ELABORATE MORE?
How does it work. Why is it called TensorFlow. Where can i run it.
More info
Where can TensorFlow run?
Why is it called TensorFlow?
How TensorFlow Works?
TensorFlow is available on 64-bit Linux, MacOS, Windows, and mobile computing platforms including Android and iOS. Its flexible architecture allows for the easy deployment of computation
it is called TensorFlow because the tensor goes in it, flows through a list of operations, and then it comes out the other side.
In a nutshell, data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.
What programming interfaces support TensorFlow?
There currently exist two programming interfaces, in C++ and Python, that permit interaction with the TensorFlow back end.
TENSORFLOW COMPONENTS
gRAPHS
tENSOR
A tensor is a vector or matrix of n-dimensions that represents all types of data. All values in a tensor hold identical data type with a known shape.
TensorFlow makes use of a graph framework. The graph gathers and describes all the series computations done during the training.
TECHNICAL ELABORATION
THE TENSORFLOW PROGRAMMING MODEL
THE TENSORFLOW PROGRAMMING MODEL
machine learning algorithms are represented as a computational graph. A computational or dataflow graph is a form of directed graph where vertices or nodes describe operations, while edges represent data flowing between these operations.
live example
Coding Example using TENSORFLOW
The next codes perform 4 important steps:- 1- Collecting Data 2- Prepareing Data 3- Training the Model 4- Evaluating the Model
collecting Data
The data used in this Example is a list of car objects like this:
preparing data
When preparing for machine learning, it is always important to Remove the data you don't need. This can be done by iterating (looping over) your data with a Map function .
converting into tensors
To use TensorFlow, input data needs to be converted to tensor data:
training the model
After preparing the data, training comes along, the previous two steps with this one will iterate for a significant amount of times:
plot the result
Testing the final result and mesure the accuracy of the model
Most common uses of tensorflow
02 Text-Based applications
01 Voice/sound recognition
Further popular uses of TensorFlow are text based applications such as sentimental analysis (CRM, Social Media), Threat Detection (Social Media, Goverment) and Fraud Detection (Insurance, Finance)
One of the most well-known uses of TensorFlow are Sound based applications. With the proper data feed, neural networks are capable of understanding audio signals.
Most common uses of tensorflow
03 image recognition
05 Video detection
04 Time series
Mostly used by Social Media, Telecom and Handset Manufacturers, Face recognition, Image search, Motion Detection, Machine Vision and Photo Clustering can be used also in Automotive, Aviation and Healthcare industries. Image Recognition aims to recognize and identify people and objects in images as well as understanding the content and context.
TensorFlow neural networks also work on video data. This is mainly used in Motion Detection, Real-Time Thread Detection in Gaming, Security, Airports and UX/UI fields.
TensorFlow Time Series algorithms are used for analyzing time series data in order to extract meaningful statistics. They allow forecasting non-specific time periods in addition to generate alternative versions of the time series.
THANKS!