TensorFlow Mobile is mainly used for any of the mobile platforms like Android and iOS. It is used for those developers who have a successful TensorFlow model and want to integrate their model into a mobile environment. It is also used for those who are not able to use TensorFlow Lite. Primary challenges anyone can find in integrating their desktop environment model into the mobile environment are:
• To see how to use the TensorFlow mobile.
• They are building their model for a mobile platform.
• They are adding the TensorFlow libraries into their mobile applications.
• Preparing the model file.
• Optimizing binary size, file size, RAM usage etc.

Cases for Using Mobile Machine Learning

The developers associated with TensorFlow use it on high powered GPU’s. But it is a very time consuming and costly way to send all device data across a network connection. Running it on any mobile is an easy way to do it.

1) Image Recognition in TensorFlow: It is a useful way to detect or get a sense of the image captured with a mobile. If the users are taking photos to know what’s in, there can be a way to apply appropriate filters or label them to find them whenever necessary.
2) TensorFlow Speech Recognition: Various applications can build with a speech-driven interface. Many times a user cannot be giving instructions, so streaming it continuously to a server would create a lot of problems.
3) Gesture Recognition in TensorFlow: It is used to control applications with the help of hands or other gestures, through analyzing sensor data. We do this with the help of TensorFlow.
Example of Optical character recognition (OCR), Translation, Text classification, Voice recognition, etc.

TensorFlow Lite:

TensorFlow Lite is the lightweight version that is specially designed for mobile platforms and embedded devices. It provides a machine learning solution to mobile with low latency and small binary size.
TensorFlow supports a set of core operators who have been tuned for mobile platforms. It also supports in custom operations in models.
TensorFlow Lite tutorial explains a new file format based on Flat Buffers, which is an open-source platform serialization library. It consists of any new mobile interpreter, which is used to keep apps smaller and faster. It uses a custom memory allocator for minimum load and execution latency.

Architecture of Tensorflow lite

The trained TensorFlow model on the disk can convert into the TensorFlow Lite file format using the TensorFlow Lite converter. Then we use that converted file in the mobile application.
For deploying the lite model file:
• Java API: It is a wrapper around C++ API on Android.
• C++ API: It can load the lite model and calling the interpreter.
• Interpreter: It executes the model. It uses selective kernel loading, which is a unique feature of Lite in TensorFlow.
We may also implement custom kernels using the C++ API.
Following are the points regarding TensorFlow Lite
It supports a set of operators that have been tuned for mobile platforms. TensorFlow also supports custom operations in models.
• It is a new file format based on Flat Buffers.
• It is an on-device interpreter that uses a selective technique of loading.
• When all the supported operators are linked, TensorFlow Lite is lesser than 300kb.
• It supports Java and C++ API.

TensorFlow Lite Vs. TensorFlow Mobile

As we saw what TensorFlow Lite and TensorFlow Mobile are, and how they support TensorFlow in a mobile environment and embedded systems, we know how they differ from each other. The differences between TensorFlow Mobile and TensorFlow Lite are given below:
• It is the next version of the TensorFlow mobile. Mainly, applications developed on TensorFlow lite will have better performance and less binary file than TensorFlow mobile.
• It is still in the early stages, so not all the cases cover, which is not the case for TensorFlow mobile.
• TensorFlow Lite supports particular sets of operators. Therefore here, not all the models will work on TensorFlow Lite by default.
So, this brings us to the end of blog. This Tecklearn ‘TensorFlow Mobile’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

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What you will Learn in this Course?

Introduction to Deep Learning and AI
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• Real-Life use cases of Deep Learning
• Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
• Pre-requisites for AI & DL
• Python Programming Language
• Installation & IDE
Environment Set Up and Essentials
• Installation
• Python – NumPy
• Python for Data Science and AI
• Python Language Essentials
• Python Libraries – Numpy and Pandas
• Numpy for Mathematical Computing
More Prerequisites for Deep Learning and AI
• Pandas for Data Analysis
• Machine Learning Basic Concepts
• Normalization
• Data Set
• Machine Learning Concepts
• Regression
• Logistic Regression
• SVM – Support Vector Machines
• Decision Trees
• Python Libraries for Data Science and AI
Introduction to Neural Networks
• Creating Module
• Neural Network Equation
• Sigmoid Function
• Multi-layered perception
• Weights, Biases
• Activation Functions
• Gradient Decent or Error function
• Epoch, Forward & backword propagation
• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
Multi-layered Neural Networks
• Error Back propagation issues
• Drop outs
Regularization techniques in Deep Learning
Deep Learning Libraries
• Tensorflow
• Keras
• OpenCV
• SkImage
• PIL
Building of Simple Neural Network from Scratch from Simple Equation
• Training the model
Dual Equation Neural Network
• TensorFlow
• Predicting Algorithm
Introduction to Keras API
• Define Keras
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
GPU in Deep Learning
• Introduction to GPUs and how they differ from CPUs
• Importance of GPUs in training Deep Learning Networks
• The GPU constituent with simpler core and concurrent hardware
• Keras Model Saving and Reusing
• Deploying Keras with TensorBoard
Keras Cat Vs Dog Modelling
• Activation Functions in Neural Network
Optimization Techniques
• Some Examples for Neural Network
Convolutional Neural Networks (CNN)
• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Understanding and Visualizing a CNN
RNN: Recurrent Neural Networks
• Introduction to RNN Model
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
Application of Deep Learning in image recognition, NLP and more
Real world projects in recommender systems and others

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