Why does it make sense to check out a TensorFlow Tutorial today? Well, Artificial intelligence (AI) is the future. Recently, we’ve seen the emergence of the first driverless cars and developments such as Project InnerEye have huge implications for healthcare. But it’s not all about the technology of tomorrow.
If you look closely you’ll find evidence of AI in your day-to-day life. AI is everywhere, from smartwatches and automated lighting to film and product recommendations. AI is changing businesses too. We’ve seen the introduction of the workflow orchestrator. This has allowed for the coordination and management of systems across multiple cloud vendors and domains.
The AI industry is expected to grow to $1,394.30 billion USD by 2029. If you want to capitalize on this growth, innovation is key. Underpinning many recent developments is machine learning, a branch of AI that allows it to adapt. This article will look at Tensorflow, and how it can help you to build your own machine-learning models.
With Tensorflow on your side, you can be at the forefront of AI innovation. So, without further ado, let’s learn more about the language.
What is Tensorflow?
Tensorflow was developed in 2012 by the Google Brain Team. It’s an open-source, end-to-platform designed to support innovations in machine learning. At the core of Tensorflow is usability. In fact, it’s often viewed as one of the easiest code development platforms to use.
One of the reasons for this is that it includes readily available APIs. This saves users the time-consuming task of editing certain sections of code. You’ll spend less time training a model and find fewer errors in the program. The use of the IBM Mainframe AS400 can further enhance the use of APIs.
Tensorflow has integration with multiple programming languages. This includes popular options such as C++ and Python, making development even quicker. It is also extremely scalable. Once code has been written, it can be run on either the CPU or GPU.
Take a look at Azure Databricks TensorFlow for even deeper insights into Tensorflow.
Tensorflow offers a number of improvements to how we will access Web 3.0 storage data.
Machine Learning Vs Deep Learning
As we’ve explained, machine learning has powered a great deal of recent AI development. Deep learning is a subset of machine learning, an AI technology that can complete tasks without human intervention. Deep learning is essentially a more complex version of the former.
In this system, the structure of AI is based on the human brain. Like the human mind, it can adapt to different circumstances and make independent decisions. Another difference is that deep learning relies on a large amount of unstructured data, perhaps stored in virtual servers. Unstructured data can take many forms, including:
- Images.
- Videos.
- Text.
- Audio.
This data is processed and can be used to power many different processes. This ranges from data analytics to mobile app testing automation.
What is MLOps?
MLOPs is a term that you will see often in relation to Tensorflow. It stands for Machine Learning Operations. MLOps is at the heart of all machine learning engineering. It usually involves multiple groups of data scientists and engineers.
MLOps will take models to production, as well as ensure that they are properly maintained and monitored. It’s important to learn MLOps before beginning any operations within Tensorflow.
Artificial Neural Networks
As previously explained, deep learning is intended to mirror the human brain. To accomplish this, a complex neural network is created. Similar to a biological network, nodes act as neurons, links as axons, and acceptor algorithms of a perceptron act as dendrites.
You are almost certainly going to have to define, save and restore a model if you want to do machine learning.
A model is an abstract form that comprises:
- A function whose purpose is to compute something on tensors.
- Variables that are updated in response to learning.
In most models, the machine learning convolutional neural network is divided into layers. The layers have defined mathematical structures that can be reused and have trained variables. Let’s look at each type of layer.
Input Layer
Within the input layer, neurons receive data from the surrounding environment. No computation is performed here, nor does the layer interact with data.
Hidden Layer
All processing takes place in the hidden layer (otherwise known as the deep neural network). Here, features are extracted and data is converted into useful information.
Output Layer
Once the processing of data is complete, it is transferred to the output layer. Here, it is computed and sends information to the outside environment,
The Components of Tensorflow
There are two major components to Tensorflow: the tensor and the graph framework. Let’s look at both in more detail.
Tensor
As the name suggests, the tensor is the main component in Tensorflow. In other words, it is the foundation of all software. The tensor is a matrix or vector (a mathematical object that has a direction and a magnitude) that makes up all forms of data.
The values within a tensor contain values that are alike with a partly or completely known shape (the dimensionality of the array or matrix).
Images are great examples of the application of a tensor. When a Red Green Blue image is processed, it is a 3-D tensor. It has layers for each color as well as dimensions for height and width.
Each operation that you carry out in Tensorflow is called an OP node. Nodes are connected, and the edge of each node is the tensor. This can be populated with data. Tensorflow provides a graph to display each operation. Graphs will allow you to see the connections between different nodes.
Arrays
This is an arrangement or a series of elements such as symbols, numbers, or expressions. Arrays can be made of multiple dimensions. A matrix, for example, is an array with 2 dimensions.
Vectors
Vectors are mathematical objects that have a direction and a magnitude. A vector is used to locate the position of one point in space relative to another point.
Graph Framework
As shown above TensorFlow uses a graph framework to display operations. Graphs are a central concept of Tensorflow. When training a new model, the graph describes the different computations that are taking place.
Graphs within TensorFlow are extremely portable and can be accessed via mobile operating systems. They can also be saved so that they can be run again in the future.
Computations take place by connecting tensors. Each tensor contains both a node and an edge. The node drives the operation as well as generating an endpoint. Computations in graphs take place by connecting tensors. Every tensor has an edge and a node. The edge explains the input and output relationship of different nodes.
More on Graphs
For this tutorial, we are using Tensorflow 2.3. Now back to graphs! Graphs are data structures that contain a set of tf.Operation objects. As already stated, graphs are of central importance. They are very portable since they can be saved, run, and restored without the original Python code.
The computational structure is defined by using a TensorFlow graph. TensorFlow graphs have three main objects. These objects are used in operations. The first of these is the constant. The constant is set at a given value which is not changed. Then, there is the variable. Variables will output their current value.
This enables them to retain their value over multiple executions of a graph. Lastly, we have the placeholder. Placeholders come into play at runtime and feed in future values into the graph. These are useful when a graph requires external data.
The Advantages of Tensorflow
Compatibility
One of the biggest advantages of TensorFlow is its flexibility. Regardless of whether you’re using a computer, laptop, or operating from the cloud, Tensorflow is accessible. Recently Tensorflow Light has been introduced, further increasing flexibility. This version of the language is to test mobile applications, and can be used on both Android and IOS.
Adaptability
It’s simple to integrate Tensorflow with many different AI programs. This allows it to be used alongside other deep-learning applications. In addition to this, as already mentioned, Tensorflow was developed by Google. This means it allows you to gain access to Google’s vast library of resources.
Building Computational Graphs in TensorFlow
To create any Tensorflow program, you need to carry out two steps: Create a Computational Graph and run it.
What is a Computational Graph?
Graph computation is a term associated with a graph that displays equational data. Each graph displays a mathematical expression, for example, a postfix, infix and prefix calculation. The graph contains multiple codes each with a variable, operation or equation.
Graph computation is useful in Tensorflow because it provides a visualization of the tensorboard. You can take a closer look at a graph and generate insights.
Advantages of Computational Graphs
Better Performance
A computational graph acts as a useful alternative for visualizing calculations that take place within Tensorflow. Operations that have been assigned to nodes can be performed side by side. This allows for better computation performance.
The Node Link Structure
Because of their node-link structure, computational graphs can store a great deal of information. In addition to this, both network-based and relational issues can only be represented by this format.
The Right System
Before you can carry out any deep learning within Tensorflow, you need the right system. Without the right computing power, your system will fail to carry out any deep learning. Your people will need the following stats if it is to support deep learning:
- Windows 10 or Ubuntu OS.
- NVIDIA GeForce GTX 960, or higher.
- Intel Core i3 Processor.
- 8 GB of RAM.
If you have the right system, you can begin performing algorithms.
Should You Choose Pytorch or Tensorflow?
What is Pytorch?
Of course, Tensorflow is not your only choice of deep learning language. Another option that is growing in popularity is PyTorch. Similar to Tensorflow, PyTorch is an open-source platform that can be used for deep learning training.
A sign of its popularity is that the platform now has an active developer community. We’ve seen the creation of libraries that have been optimized for a variety of user cases. This includes the computer vision and NLP.
Again, like Tensorflow, one of the major benefits of PyTorch is the ease of use. It can run and iterate code extremely quickly. As the name suggests, the platform is most commonly used alongside Python. It can, however, also speak to other programming languages, including CUDA and C++. It can also integrate with Python-based data science tools.
PyTorch provides an intuitive framework, allowing for the creation of computational graphs. These can be smoothly iterated whilst running. This makes the language ideal for powering machine learning even if you are unsure of your resource requirements.
How is it Different from Tensorflow?
There are a variety of differences between PyTorch and Tensorflow. Let’s look at two major differences.
Graph Definition
Tensorflow allows a user to create a stateful dataflow graph before running the model. PyTorch, on the other hand, makes use of dynamic graphs
This makes Pytorch a favorite within the research community, allowing for the easy creation of bespoke models. As the platform is dynamic, it can be run at the same time as the model. This means that the computation graph is generated at each execution stage. It also allows a user to make changes to a graph when needed.
Visualization
Tensorflow allows visualization using the tensorboard. This allows users to better understand deep learning models using graphs, distributions, scalers, histograms and graphs.
Pytorch uses a system called Visdom, a simple tool that allows easy visualization of data. This option is flexible and easy to use but features less options than the Tensorboard. It should be noted that the Tensorboard can now be integrated with PyTorch, allowing for the use of both tools.
Is Tensorflow Right For You?
Does tensorflow meet your needs? There is no definitive answer to this question. You will need to take time to consider your plans for AI development. As we’ve explored, there are other alternatives for programming languages. Ultimately, the question centers on your specific requirements.
There’s no denying that AI and deep learning are the future. It seems that the technology is destined to touch every aspect of our lives. This ranges from integration with IP telephone systems to innovations in cybersecurity. (What is an ip phone? It’s an internet-based phone system also referred to as VoIP). If you want to be at the heart of this, you need the right tools. There’s no denying that Tensorflow can help shape innovations.
