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AI and Machine Learning, Getting Started with Google Cloud

Intro to TensorFlow and Deep Learning Frameworks

By Sabina Bhasin | March 24, 2021

This was written by Jennifer Marsh, connect with her on the platform,  @jennifer  .


Artificial intelligence (AI) continues to become a sophisticated and integral part of daily life, so talented individuals and those interested in algorithm development will turn to deep learning frameworks like PyTorch by Facebook, OMNex by Microsoft, and other open-source tools like Keras, Gluon, and MXNet.

All deep learning (DL) and machine learning (ML) models are built with a similar goal: to improve the way machines recognize intricate patterns and make decisions. These AI concepts and understanding the difference between DL vs. ML are imperative to choosing the right data model for your programming knowledge and needs.

Perhaps the most popular DL framework is TensorFlow. In ML and DL frameworks, TensorFlow is an open-source library that helps data scientists and developers create and train models. DL frameworks are the foundation for building and designing neural networks, which are components in AI study. The goal of neural networks is to simulate the way humans make decisions based on information and patterns contained within data. Like humans, the data within models "train" the neural network so that the computers that process data can make intelligent decisions.

If you're new to algorithmic programming and ML/DL tools, read our comprehensive intro to TensorFlow and join the greater discussion on DL.    


What Is a Deep Learning Framework?


DL frameworks are open-source ML/DL tools used for numerical computation. Many of these digital libraries contain building block tools and resources to construct, design, and validate neural networks.

Neural networks are a subset of ML and AI. DL is the process of using several large neural networks to analyze data models and make predictions in large environments where models contain potentially terabytes of data. 

For the average data scientist, access to neural networks is limited. Unless you have access to large data sets and enormous computational power, you need help. DL frameworks give you access to extensive data models and the computational power required to make predictions. TensorFlow is one of these frameworks.


What Is TensorFlow?


One of the most popular and widely recognized ML/DL programming tools is TensorFlow. TensorFlow is an open-source data library for numerical computation. It provides data scientists, researchers, and programmers arrays of data flow graphs and resources for deploying code to ML/DL models.

TensorFlow uses JavaScript and requires a knowledge of Python. Python and R are the two main languages in data science, but TensorFlow requires programming knowledge of Python or JavaScript. TensorFlow supports other languages such as Swift, C, Go, Java, and C#. If you want to learn more about data science, it's best to target Python as your language of choice.

You still need basic knowledge of ML to work with TensorFlow. Still, TensorFlow makes data collection much easier by providing models out of the box and providing an API to build applications and graphs. Applications can run on any device, including your local machine, in the cloud, or on a mobile Android and Apple device.


Other Deep Learning Frameworks


Programmers and data scientists have more DL frameworks to choose from than just TensorFlow. For programmers working with large data sets and running multiple programs at once, Keras offers unrivaled speed in the world of numeric computation.

The TensorFlow framework is popular, but it's not the only option for building ML models. Developers generally report that TensorFlow has a significant learning curve. MLpack is a popular option for programmers new to ML. It provides recommendations to help programmers get started with models and training algorithms.

Training data models requires computational power, so programmers often use GPU and CPU power to process instructions. PyTorch is a tool that will leverage GPU processing power for scientific computations. It contains several DL algorithms that scientists can use for their predictions.

For programmers who prefer .NET, Microsoft introduced its application named Infer.NET. This tool works with heterogeneous data and provides scaling options for large applications. Developers who like Linux-based Apache Spark MLlib are another alternative for decision trees, regression, and clustering.


Intro to TensorFlow Framework


Installation, Packages, and TensorFlow Basics

The TensorFlow framework uses dataflow graphs to describe computation: Nodes represent mathematical operations like multiplication, division, and so on, while the graph's edges represent data sets of the operation. This framework optimizes the calculation of mathematical expressions, allowing for ML scalability.


TensorFlow Installation


Before installing TensorFlow features, make sure to first have Python 3.5-3.8 for TensorFlow 2.0 and 3.8 for TensorFlow 2.2. You can work with TensorFlow on Ubuntu 16.04 or later, macOS 10.12.6 (Sierra) or later, or Windows 7 or later versions.


General Steps to Install TensorFlow:


  1. Install Python.

  2. Create a virtual environment in Python.

  3. Install the pip3 library in Python.

  4. Install TensorFlow.


Mathematical Expressions in TensorFlow


To get started and practice TensorFlow basics, you can enter basic mathematical expressions. An easy way to do this is to use a basic math function such as:


y = 3*x + 10


To code this in Python, use the following code:


x = 2.0
y = 3*x + 10
print y


TensorFlow uses the above code and prints the value "16" for y. Now, you can use TensorFlow to define a graph. The following Python code will graph the previous expression:


import tensorflow as tf

x = tf.placeholder(tf.float32, name="x")
y = tf.placeholder(tf.float32, name="y")

z = tf.multiply(x, y, name="z")

with tf.Session() as session:
    print session.run(z, feed_dict={x: 2.0, y: 16.0})


You can now view the graph in TensorBoard and visualize the data. Visualizing data helps identify if the output is as expected or if you need to refine your code.


Other TensorFlow Features


In addition to scalability, one of the main reasons for TensorFlow's popularity is the architectural flexibility it provides. Some basic TensorFlow features include:

  • Multi-dimensional arrays

  • GPU/CPU computing

  • The ability to code for mobile devices

  • Scalability for production-level machine intelligence


Do you have expertise in a product or technical specialization? Would you like to also contribute to the C2C Learn Knowledge Base? Get in touch with Sabina Bhasin, Content Manager (@ContentSabina) or sabina.bhasin@c2cglobal.com 


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