What Is TensorFlow? Beginner’s Guide to Developing AI Models
Being a proprietary machine learning software powered by Google, TensorFlow has become an industry standard over the past few years. It’s important for developers and other professionals to at least have a working knowledge of TensorFlow to keep up with the new developments in the industry.
Table of Contents
- TensorFlow basics
- Understanding deep learning
- How TensorFlow works
- TensorFlow components
- Example TensorFlow projects
TensorFlow powers all of Google’s AI-based services like Google Ads. The Google Brain team developed it for research needs. In 2015, Google optimized the software for large-scale production-centered use and made it available for free as an open-source library.
If you’re looking to develop and launch AI-powered machine learning applications that can be used across platforms, TensorFlow is quite suitable. Its models are easily deployable and come with high-level APIs for macOS, iOS, and Android app development. Mobile device applications are generally developed using TensorFlow Lite.
TensorFlow applications can process large volumes of data, including text, audio-visual data, and images. For instance, Airbnb uses TensorFlow to categorize the hundreds of thousands of photos posted with listings and check if the captions written with them are completely accurate.
Understanding deep learning
TensorFlow is widely known for its proficiency in a particular area of machine learning, deep learning.
So, what exactly is deep learning? It’s a subset of machine learning that deals with complex neural network models capable of learning from large data sets. (And we mean LARGE. A good rule for estimating the amount of data that your model requires is considering 10 times the degree of freedom. For instance, if you’re working with three independent parameters, you must have at least 30 data points.) These models process data, find relevant logical patterns and algorithms, and then produce predictions or results based on them.
Let’s understand this with the help of an example. Consider an AI model trained to identify and separate oranges from a vast array of objects. The model is first fed thousands of images of oranges and a couple of thousand objects that are NOT oranges.
From these, it concludes that oranges are:
- Generally round
- On the yellow-orange color spectrum
- Generally the size of a tennis ball
The deep learning workflow will now use this information to identify oranges in any picture or video that’s fed to it.
But what if you show it the picture of an orange tennis ball? While most machine learning algorithms are bound to fail here, deep learning can produce incredibly accurate results.
Deep learning employs an artificial neural network that contains three or more layers. This makes it much more capable of identifying complex parameters like the texture of the object and color gradation. With this information handy, a deep learning algorithm can easily differentiate between a tennis ball and an orange based on surface texture alone.
Deep learning is used to power AI systems that perform complex analytical tasks without direct human intervention. Both simple applications like chatbots and cutting-edge machines like self-driving cars can be developed using deep learning.
Still confused? There’s always help! Choose from hundreds of certified deep learning experts listed on Upwork to help kick-start your next deep learning model.
How TensorFlow works
TensorFlow follows a simple, flow-based operational model that consists of three primary stages:
- Preprocessing data—filtering and standardizing it to make it fit for use
- Building or coding the model you wish you train
- Training and estimating—tweaking the model for increased accuracy
The platform’s name provides excellent insight into its operational structure. It’s a combination of two keywords—tensor and flow. A “tensor” is a simple, multidimensional array that forms TensorFlow’s most basic data unit. It can contain various text, audio, or data elements.
“Flow” refers to dataflow graphs, flowcharts that establish a logical sequence of operations, or a model the application is expected to perform several times.
These dataflow graphs form the basis of what the model is supposed to learn and eventually implement to produce the desired results. For our intelligent orange detector, a simple dataflow graph would be:
- Looking for round objects
- Checking if they’re on the yellow-orange scale
- Approximating their sizes
- Checking for finer parameters like surface texture and color gradation
- Storing the tags (identifiers) of all images that meet all set criteria
Let’s delve deeper into the three subparts of the process.
Data fed to machine learning models is collected from multiple, often varying sources and can differ significantly in type and structure. The first step of the process is to filter all input data before it’s fed to the model.
This includes replacing missing values with the mean or median of the rest of the data, eliminating outliers, and filtering the data according to custom metrics. This data is then standardized, i.e., converted to a single, coherent structure consistent across the entire data set.
Once rigorously filtered and normalized or standardized, the data is sent further in the process. Preprocessing also includes ensuring that the data meets set user standards and—in the case of industrial applications—doesn’t contain any personally identifiable information (PII) that could put the company in legal jeopardy if misused.
Building a model
The first step of TensorFlow modeling involves creating a Python file using a Google cloud service like Google Colab, which provides ready integration with TensorFlow. Colab allows you to create a new notebook where you can start coding your model.
The second step is to import all the dependencies or libraries (simple, pre-loaded functions) like NumPy and matplotlib that your Tensorflow model requires.
The preprocessed data set is then saved and imported. Once saved, the data set is made publicly available to all TensorFlow users, given its open-source license.
This makes it possible to import additional, pre-filtered data sets for your model like the MNIST database of handwritten digits—largely considered to be the data set used to train the “Hello, world,” or beginning program, of computer vision models.
The user then divides this data into training and testing data. Training data sets are much larger and are used to train models, while testing data sets are used as dummy test cases to validate the model.
All that’s left to do is build or code the model or the logical algorithm you expect all incoming data to pass through.
Training and estimating a model
Here, the data set to be analyzed is imported and loaded onto the runtime environment. The first few (usually five) elements from this data set are extracted onto an object called a DataFrame for further examination. This is where you assign each result parameter a numerical value, which is how you’ll reference it throughout the training process.
For instance, if 0 and 1 are the listed labels for our orange identifier, the corresponding input will be [“not orange,” “orange’]. You’re then required to import the preprocessed data set, which will be used to train the model.
In the process, loss and gradient functions are estimators used to manage the accuracy of the model’s predictions.
The goal is to optimize or minimize both of these and make the model as accurate as possible. Finally, the model is validated using the test data set.
If you feel confident in your abilities in this area, consider finding machine learning jobs on Upwork. Plenty of companies are in need of experts like you.
TensorFlow consists of several data-based and visual or graphical components rendered by built-in processing units—namely TPUs, CPUs, and GPUs—to provide a lucid visualization of the data flow. These include:
- Variables. Like any other programming language or platform, TensorFlow uses modifiable variables to store values between sessions. These values are usually the weights or biases the model needs to factor in to produce more accurate results.
- Nodes. Nodes refers to the individual graphical objects present in the visual representation of your model that the GPUs render. They are simple shapes or groups of shapes that represent the various processes that your data goes through.
- Tensors. As mentioned, tensors are the fundamental data units that give TensorFlow its name. They are multidimensional arrays that can store several objects of various kinds simultaneously.
- Placeholders. Placeholders are unique variables with no stored data at the time of session execution. They’re assigned values sometime during the execution, which are then fed to placeholder graphs.
- Sessions. A session can be seen as a graphical runtime that allows user-created dataflow models to use available GPUs and be executed on user command.
Example TensorFlow projects
TensorFlow can be used to create many cutting-edge applications that leverage complex neural networks to perform cognitive tasks without the need for human intervention.
Let’s go over some of its use cases.
One of the most ambitious applications of TensorFlow is its use in self-driving car models. TensorFlow 3D models help these cars with long- and short-distance 3D perception.
Self-driving cars use TensorFlow to determine how close an object on the street is, what direction an approaching car is coming from, and other depth-of-vision-related functions.
Google developed TensorFlow and TensorFlow 2 for two primary uses: generating intelligent user insights from the petabytes of data available on the web and helping the platform rank web content.
The company still uses neural network-based learning-to-rank models to dynamically rank webpages and other content according to several custom parameters. This helps the search engine pull the best, most relevant search results from its vast, ever-changing data store.
NASA uses programmable telescopes like the Hubble Telescope, and more recently, the James Webb telescope to automatically classify and organize all the galaxies and other celestial objects that they capture. This is done with the help of advanced deep learning models that check for custom parameters and classify objects accordingly.
Sentiment analysis is an important technique used in political polling. It employs deep learning algorithms to mine large volumes of unstructured data (text, videos, webpages, blogs, etc.) to discover and classify the emotions and opinions that they depict.
Revolutionary deep learning models are now helping make drug discovery preclinical trial processes quicker and cheaper. Monitored simulations are used to predict the interactions between drugs and their targets and generate novel molecular structures suitable for a target of interest.
Using TensorFlow as a deep learning expert
If you’re a deep learning expert, libraries like TensorFlow and Keras can prove to be a boon. These libraries use the TensorBoard to help convert elaborate deep learning problems into simple, computational graph solutions and mathematical operations that can be custom modeled to fit your needs.
With Upwork, you can leverage your TensorFlow skills to find deep learning jobs of your choice!
Most freelance data science jobs on the platform require you to code an efficient model, which is trained with the help of a provided data set and finally used to solve the problem at hand. Browse the best deep learning jobs on Upwork today.
And remember to list all relevant deep learning certifications and GitHub projects on your profile to attract more project invitations and job offers!