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What Is TensorFlow?
TensorFlow is an open-source collection of tools and libraries that helps developers build and train deep learning models.
It has become one of the most widely used software frameworks since it can help build complex Artificial Intelligence (AI) models relatively quickly and easily.
Jad Khalife, Director of Sales Engineering, Middle East & Turkey, at Dataiku, says one of the features that make Tensorflow suitable for machine learning is that it's an end-to-end framework that offers everything from data preprocessing to model deployment.
You may likeTensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.
Developed and released by the Google Brain Team in November 2015, the framework received a major update in 2019 in the form of TensorFlow 2.0.
TensorFlow applications can run on either conventional CPUs or GPUs. Furthermore, Google Cloud users can run TensorFlow on Google's own TensorFlow Processing Unit (TPU) chips, which are designed to speed up TensorFlow tasks.
Uses for TensorFlowTensorFlow has many applications in different industries. It has been used by AirBnB to improve guest experience, by Airbus to detect anomalies in ISS telemetry data, by NASA to hunt for new planets, and to fight illegal deforestation.
Among its most important uses are:
Image recognition: This is one of the most popular uses of TensorFlow. Developers can leverage TensorFlow's pre-trained models or build their own to identify and classify objects within digital images and videos. This technology has applications in fields like medical image analysis, and autonomous driving.
Natural Language Processing (NLP): Developers can use TensorFlow to process and analyze large volumes of textual data. This helps automate language understanding and generation, enabling developers to create chatbots, language translation systems, sentiment analysis tools, and other such NLP-based systems. Not surprisingly, many digital assistants are based on models trained using TensorFlow.
Reinforcement learning: Reinforcement learning (RL) involves an agent that learns to make decisions by interacting with an environment, through trial and error. TensorFlow can be used for this task through its library called TensorFlow Agents (TF-Agents), which provides a framework for building and training RL agents. This is particularly useful in fields such as robotics where TensorFlow can help develop models that enable robots to perceive and interact with their environment, improving tasks like navigation.
Generative Adversarial Networks (GANs): TensorFlow bundles a library called TF-GAN that allows developers to easily implement GANs. This comprehensive library simplifies the setup and training of GAN models. These models can then be used for tasks like generating all kinds of realistic media.
Time Series analysis: TensorFlow provides several methods and models for time series analysis and forecasting. This comes in handy to forecast outcomes, detect anomalies, and for financial modeling. It's widely used in predicting stock prices, weather forecasting, and such. Recommendation engines, such as those used by Netflix, are one of the most common use cases for time series.
Advantages of TensorFlowTensorFlow offers several advantages that make it a popular choice for developing and deploying machine learning models. Here's why it's the preferred choice for many AI developers:
Scalability: TensorFlow is designed to be scalable, which allows it to work efficiently across various devices, from mobile phones to high-end servers. It can also easily handle large datasets and computations, whether on a local machine, distributed across multiple machines, or in a cloud environment.
Support for multiple devices: TensorFlow supports multiple devices, such as CPUs, GPUs, and TPUs. This capability allows models created with TensorFlow to be deployed easily across different platforms without rewriting code.
Parallelism: By distributing its workload across multiple processors or machines, TensorFlow can significantly reduce the time required to train models. This is particularly useful when working with large datasets and complex models that would otherwise take a long time to train on a single device.
Open Source: TensorFlow is open source, which means it's accessible to AI developers all over the world. Being open source also helps foster trust and transparency. Backed by Google, TensorFlow also has a very active and vibrant community of developers, data scientists, and engineers who work together to modify and extend the framework and provide support.
Greater developer control: Although TensorFlow uses Python as a front-end API for building applications with the framework, it offers wrappers in several other programming languages including C++ and Java. This means developers can train and deploy machine learning models regardless of the programming language or platform.
Extensive ecosystem: TensorFlow boasts a rich ecosystem of libraries and tools to help make development faster and easier. This includes TensorFlow Lite for mobile and embedded devices, TensorFlow.Js for web-based applications, the TensorFlow Hub repository of pre-trained models, and a lot more.
TensorFlow componentsThere are a few key components in TensorFlow that help facilitate its functionality as one of the leading machine-learning libraries.
Tensors: As its name suggests Tensors are a crucial aspect of TensorFlow. Think of a tensor as a multi-dimensional array. In TensorFlow, all data is represented as tensors, which are the primary data structures that are used to represent and manipulate data in TensorFlow.
Flows: This is the other critical aspect of TensorFlow. As we know, TensorFlow accepts input in the form of tensors. This input passes through a series of steps. The term "flow" refers to this movement of data through the various stages of model training or inference.
Graphs: One of the reasons for TensorFlow's popularity is its graph-based architecture. All operations in TensorFlow are depicted and executed inside a graph, which helps define how data is processed in the model.
TensorBoard: TensorBoard is a visualization tool that helps developers track, and understand the training of machine learning models in TensorFlow. It is primarily used for monitoring and debugging the machine learning models and provides insights into how the models are learning and performing.
What is TensorFlow Lite?While TensorFlow is a wonderful library to train and infer machine learning models, it requires powerful CPUs, GPUs, or TPUs to work its magic. In 2017, Google released TensorFlow Lite to enable developers to bring machine learning-powered experiences to mobile and embedded devices.
Now called LiteRT, TensorFlow Lite allows developers to deploy machine learning models on devices with limited computational resources, such as smartphones, tablets, and other IoT devices.
"[TensorFlow Lite] enables efficient inference with minimal computational resources, making it ideal for real-time and low-latency machine learning applications," says Khalife.
It is tuned for speed and optimizes power consumption to run efficiently in devices with limited hardware resources. Models created with TensorFlow Lite are lightweight enough to be deployed on embedded devices, like the Raspberry Pi, and at the edge. Like TensorFlow, LiteRT is also open source.
5 Natural Language Processing Libraries To Use
Natural language processing (NLP) is important because it enables machines to understand, interpret and generate human language, which is the primary means of communication between people. By using NLP, machines can analyze and make sense of large amounts of unstructured textual data, improving their ability to assist humans in various tasks, such as customer service, content creation and decision-making.
Additionally, NLP can help bridge language barriers, improve accessibility for individuals with disabilities, and support research in various fields, such as linguistics, psychology and social sciences.
Here are five NLP libraries that can be used for various purposes, as discussed below.
NLTK (Natural Language Toolkit)One of the most widely used programming languages for NLP is Python, which has a rich ecosystem of libraries and tools for NLP, including the NLTK. Python's popularity in the data science and machine learning communities, combined with the ease of use and extensive documentation of NLTK, has made it a go-to choice for many NLP projects.
NLTK is a widely used NLP library in Python. It offers NLP machine-learning capabilities for tokenization, stemming, tagging and parsing. NLTK is great for beginners and is used in many academic courses on NLP.
Tokenization is the process of dividing a text into more manageable pieces, like specific words, phrases or sentences. Tokenization aims to give the text a structure that makes programmatic analysis and manipulation easier. A frequent pre-processing step in NLP applications, such as text categorization or sentiment analysis, is tokenization.
Words are derived from their base or root form through the process of stemming. For instance, "run" is the root of the terms "running," "runner," and "run." Tagging involves identifying each word's part of speech (POS) within a document, such as a noun, verb, adjective, etc.. In many NLP applications, such as text analysis or machine translation, where knowing the grammatical structure of a phrase is critical, POS tagging is a crucial step.
Parsing is the process of analyzing the grammatical structure of a sentence to identify the relationships between the words. Parsing involves breaking down a sentence into constituent parts, such as subject, object, verb, etc. Parsing is a crucial step in many NLP tasks, such as machine translation or text-to-speech conversion, where understanding the syntax of a sentence is important.
Related: How to improve your coding skills using ChatGPT?
SpaCySpaCy is a fast and efficient NLP library for Python. It is designed to be easy to use and provides tools for entity recognition, part-of-speech tagging, dependency parsing and more. SpaCy is widely used in the industry for its speed and accuracy.
Dependency parsing is a natural language processing technique that examines the grammatical structure of a phrase by determining the relationships between words in terms of their syntactic and semantic dependencies, and then building a parse tree that captures these relationships.
Stanford CoreNLPStanford CoreNLP is a Java-based NLP library that provides tools for a variety of NLP tasks, such as sentiment analysis, named entity recognition, dependency parsing and more. It is known for its accuracy and is used by many organizations.
Sentiment analysis is the process of analyzing and determining the subjective tone or attitude of a text, while named entity recognition is the process of identifying and extracting named entities, such as names, locations and organizations, from a text.
GensimGensim is an open-source library for topic modeling, document similarity analysis and other NLP tasks. It provides tools for algorithms such as latent dirichlet allocation (LDA) and word2vec for generating word embeddings.
LDA is a probabilistic model used for topic modeling, where it identifies the underlying topics in a set of documents. Word2vec is a neural network-based model that learns to map words to vectors, enabling semantic analysis and similarity comparisons between words.
TensorFlowTensorFlow is a popular machine-learning library that can also be used for NLP tasks. It provides tools for building neural networks for tasks such as text classification, sentiment analysis and machine translation. TensorFlow is widely used in industry and has a large support community.
Classifying text into predetermined groups or classes is known as text classification. Sentiment analysis examines a text's subjective tone to ascertain the author's attitude or feelings. Machines translate text from one language into another. While all use natural language processing techniques, their objectives are distinct.
Can NLP libraries and blockchain be used together?NLP libraries and blockchain are two distinct technologies, but they can be used together in various ways. For instance, text-based content on blockchain platforms, such as smart contracts and transaction records, can be analyzed and understood using NLP approaches.
NLP can also be applied to creating natural language interfaces for blockchain applications, allowing users to communicate with the system using everyday language. The integrity and privacy of user data can be guaranteed by using blockchain to protect and validate NLP-based apps, such as chatbots or sentiment analysis tools.
Related: Data protection in AI chatting: Does ChatGPT comply with GDPR standards?
Why Enterprises Are Turning From TensorFlow To PyTorch
The deep learning framework PyTorch has infiltrated the enterprise thanks to its relative ease of use. Three companies tell us why they chose PyTorch over Google's renowned TensorFlow framework.
A subcategory of machine learning, deep learning uses multi-layered neural networks to automate historically difficult machine tasks—such as image recognition, natural language processing (NLP), and machine translation—at scale.
TensorFlow, which emerged out of Google in 2015, has been the most popular open source deep learning framework for both research and business. But PyTorch, which emerged out of Facebook in 2016, has quickly caught up, thanks to community-driven improvements in ease of use and deployment for a widening range of use cases.
PyTorch is seeing particularly strong adoption in the automotive industry—where it can be applied to pilot autonomous driving systems from the likes of Tesla and Lyft Level 5. The framework also is being used for content classification and recommendation in media companies and to help support robots in industrial applications.
Joe Spisak, product lead for artificial intelligence at Facebook AI, told InfoWorld that although he has been pleased by the increase in enterprise adoption of PyTorch, there's still much work to be done to gain wider industry adoption.
"The next wave of adoption will come with enabling lifecycle management, MLOps, and Kubeflow pipelines and the community around that," he said. "For those early in the journey, the tools are pretty good, using managed services and some open source with something like SageMaker at AWS or Azure ML to get started."
Disney: Identifying animated faces in moviesSince 2012, engineers and data scientists at the media giant Disney have been building what the company calls the Content Genome, a knowledge graph that pulls together content metadata to power machine learning-based search and personalization applications across Disney's massive content library.
"This metadata improves tools that are used by Disney storytellers to produce content; inspire iterative creativity in storytelling; power user experiences through recommendation engines, digital navigation and content discovery; and enable business intelligence," wrote Disney developers Miquel Àngel Farré, Anthony Accardo, Marc Junyent, Monica Alfaro, and Cesc Guitart in a blog post in July.
Before that could happen, Disney had to invest in a vast content annotation project, turning to its data scientists to train an automated tagging pipeline using deep learning models for image recognition to identify huge quantities of images of people, characters, and locations.
Disney engineers started out by experimenting with various frameworks, including TensorFlow, but decided to consolidate around PyTorch in 2019. Engineers shifted from a conventional histogram of oriented gradients (HOG) feature descriptor and the popular support vector machines (SVM) model to a version of the object-detection architecture dubbed regions with convolutional neural networks (R-CNN). The latter was more conducive to handling the combinations of live action, animations, and visual effects common in Disney content.
"It is difficult to define what is a face in a cartoon, so we shifted to deep learning methods using an object detector and used transfer learning," Disney Research engineer Monica Alfaro explained to InfoWorld. After just a few thousand faces were processed, the new model was already broadly identifying faces in all three use cases. It went into production in January 2020.
"We are using just one model now for the three types of faces and that is great to run for a Marvel movie like Avengers, where it needs to recognize both Iron Man and Tony Stark, or any character wearing a mask," she said.
As the engineers are dealing with such high volumes of video data to train and run the model in parallel, they also wanted to run on expensive, high-performance GPUs when moving into production.
The shift from CPUs allowed engineers to re-train and update models faster. It also sped up the distribution of results to various groups across Disney, cutting processing time down from roughly an hour for a feature-length movie, to getting results in between five to 10 minutes today.
"The TensorFlow object detector brought memory issues in production and was difficult to update, whereas PyTorch had the same object detector and Faster-RCNN, so we started using PyTorch for everything," Alfaro said.
That switch from one framework to another was surprisingly simple for the engineering team too. "The change [to PyTorch] was easy because it is all built-in, you only plug some functions in and can start quick, so it's not a steep learning curve," Alfaro said.
When they did meet any issues or bottlenecks, the vibrant PyTorch community was on hand to help.
Blue River Technology: Weed-killing robotsBlue River Technology has designed a robot that uses a heady combination of digital wayfinding, integrated cameras, and computer vision to spray weeds with herbicide while leaving crops alone in near real time, helping farmers more efficiently conserve expensive and potentially environmentally damaging herbicides.
The Sunnyvale, California-based company caught the eye of heavy equipment maker John Deere in 2017, when it was acquired for $305 million, with the aim to integrate the technology into its agricultural equipment.
Blue River researchers experimented with various deep learning frameworks while trying to train computer vision models to recognize the difference between weeds and crops, a massive challenge when you are dealing with cotton plants, which bear an unfortunate resemblance to weeds.
Highly-trained agronomists were drafted to conduct manual image labelling tasks and train a convolutional neural network (CNN) using PyTorch "to analyze each frame and produce a pixel-accurate map of where the crops and weeds are," Chris Padwick, director of computer vision and machine learning at Blue River Technology, wrote in a blog post in August.
"Like other companies, we tried Caffe, TensorFlow, and then PyTorch," Padwick told InfoWorld. "It works pretty much out of the box for us. We have had no bug reports or a blocking bug at all. On distributed compute it really shines and is easier to use than TensorFlow, which for data parallelisms was pretty complicated."
Padwick says the popularity and simplicity of the PyTorch framework gives him an advantage when it comes to ramping up new hires quickly. That being said, Padwick dreams of a world where "people develop in whatever they are comfortable with. Some like Apache MXNet or Darknet or Caffe for research, but in production it has to be in a single language, and PyTorch has everything we need to be successful."
Datarock: Cloud-based image analysis for the mining industryFounded by a group of geoscientists, Australian startup Datarock is applying computer vision technology to the mining industry. More specifically, its deep learning models are helping geologists analyze drill core sample imagery faster than before.
Typically, a geologist would pore over these samples centimeter by centimeter to assess mineralogy and structure, while engineers would look for physical features such as faults, fractures, and rock quality. This process is both slow and prone to human error.
"A computer can see rocks like an engineer would," Brenton Crawford, COO of Datarock told InfoWorld. "If you can see it in the image, we can train a model to analyze it as well as a human."
Similar to Blue River, Datarock uses a variant of the RCNN model in production, with researchers turning to data augmentation techniques to gather enough training data in the early stages.
"Following the initial discovery period, the team set about combining techniques to create an image processing workflow for drill core imagery. This involved developing a series of deep learning models that could process raw images into a structured format and segment the important geological information," the researchers wrote in a blog post.
Using Datarock's technology, clients can get results in half an hour, as opposed to the five or six hours it takes to log findings manually. This frees up geologists from the more laborious parts of their job, Crawford said. However, "when we automate things that are more difficult, we do get some pushback, and have to explain they are part of this system to train the models and get that feedback loop turning."
Like many companies training deep learning computer vision models, Datarock started with TensorFlow, but soon shifted to PyTorch.
"At the start we used TensorFlow and it would crash on us for mysterious reasons," Duy Tin Truong, machine learning lead at Datarock told InfoWorld. "PyTorch and Detecton2 was released at that time and fitted well with our needs, so after some tests we saw it was easier to debug and work with and occupied less memory, so we converted," he said.
Datarock also reported a 4x improvement in inference performance from TensorFlow to PyTorch and Detectron2 when running the models on GPUs — and 3x on CPUs.
Truong cited PyTorch's growing community, well-designed interface, ease of use, and better debugging as reasons for the switch and noted that although "they are quite different from an interface point of view, if you know TensorFlow, it is quite easy to switch, especially if you know Python."
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