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  • Piyush Pathania

Deep Learning with Tensorflow.js


Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning an unsupervised form of data that is unstructured or unlabeled that works on the structure and functions similarly to the human brain. It learns from data that is unstructured and uses complex algorithms to train a neural network.

Usually, deep learning is unsupervised or semi-supervised. Instead of using task-specific algorithms, it learns from representative examples. For example, to build a model that recognizes dogs by species, it needs to prepare a database that includes a lot of different dog images. Primarily we use neural networks in deep learning, which is based on AI in which we train networks to recognize text, numbers, images, voice, and so on. Unlike traditional machine learning, the data here is far more complicated, unstructured, and varied, such as images, audio, or text files.


Core Components of Deep Learning in Neural Network

  1. Input Layer: The input layer accepts large volumes of data as input to build the neural network. The data can be in the form of text, image, audio, etc.

  2. Hidden Layer: This layer processes data by performing complex computations and carries out feature extraction. As part of the training, these layers have weights and biases that are continuously updated until the training process is complete. Each neuron has multiple weights and one bias. After computation, the values are passed to the output layer.

  3. Output Layer: The output layer generates predicted output by applying suitable activation functions. The output can be in the form of numeric or categorical values.

Deep learning also has many libraries that are available for deep learning and machine learning programming. Some of the most common libraries are keras, theano, TensorFlow, DL4J, and torch. These are some libraries that are available to the user but in this tutorial, we will only discuss Tensorflow.js which is an open-source library currently popular among users. Keras was also once a popular choice, but it has been integrated with Tensorflow. Before discussing TensorFlow, let's discuss what Tensor is to make it more understandable.


What is Tensor?


A tensor is a generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base data types. Each element in the Tensor has the same data type, and the data type is always known.


For deep learning, especially in the training process, large amounts of data exist in a very complicated format. It helps when to put, use, or store it in a compact way, which tensors provide, even if they appear in multi-dimensional arrays. So the data is stored in tensors and fed into the neural network as shown below:


What is TensorFlow.js?


TensorFlow.js is a JavaScript library developed by Google for training and using machine learning (ML) models in the browser. TensorFlow accepts data in the form of multi-dimensional arrays of higher dimensions called tensors. Multi-dimensional arrays are very handy in handling large amounts of data.


TensorFlow works based on data flow graphs that have nodes and edges. The execution mechanism is in the form of graphs, so it is much easier to execute TensorFlow code in a distributed manner across a cluster of computers while using GPUs.


Features of TensorFlow

  • It includes a feature that defines, optimizes, and calculates mathematical expressions easily with the help of multi-dimensional arrays called tensors.

  • It includes programming support of deep neural networks and machine learning techniques.

  • It includes a high scalable feature of computation with various data sets.

  • TensorFlow uses GPU computing, automating management. It also includes a unique feature of optimization of the same memory and the data used.

Use Cases of Deep Learning Using TensorFlow

  1. Voice/Sound Recognition: Voice and Sound recognition applications are the most well-known use cases of deep learning. If the neural networks have the proper input data feed, neural networks are capable of understanding audio signals. There is language understanding, which is another common use case for Voice Recognition. Furthermore, speech-to-text applications can be used to determine snippets of sound in greater audio files, and transcribe the spoken word as text.

  2. Text-Based Applications: Text-based applications are very popular use cases of deep learning. Text-based applications such as sentiment analysis (for customer relationship management (CRM) and social media), threat detection (for social media and government), and fraud detection (insurance and finance). For example, Google Translate supports 100 languages.

  3. Image Recognition: Image recognition is the first deep learning application that made deep learning and machine learning popular. Social Media, Telecom, and Handset Manufacturers mostly use image recognition. Furthermore, image recognition is used for: face recognition, image search, motion detection, machine vision, and photo clustering.

  4. Time Series: Deep learning is using time series algorithms for analyzing time-series data to extract meaningful statistics. For example, it can be used to predict the stock market. So, deep learning is used for forecasting non-specific time periods in addition to generating alternative versions of the time series.

  5. Video Detection: Deep learning algorithms can be used for video detection. So, this is mainly used in motion detection, real-time threat detection in gaming, security, airports, and user experience/ user interface (UX/UI) fields. Some researchers are working on large-scale video classification datasets such as YouTube to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.

  6. Create Abstract Art: Though this example is less “useful” for the real world, this is one of the best examples.

  7. Generate Realistic Images: thispersondoesnotexist.com recently made the news for using a generative adversarial network to generate images of completely new people. This website explains how a neural network developed by Google “finds” objects in unrelated images.

  8. Play Games: Having AI players in video games isn’t a new idea, and there are already examples in TensorFlow.js. This project uses TensorFlow.js to automate the Chrome Dinosaur game.

  9. Recommend Content: Content recommendation through AI is fairly popular and used by most media platforms. With TensorFlow.js, content recommendations can be handled on the client-side.

  10. Self Driving Cars: TensorFlow 3D contains state-of-the-art models for 3D deep learning with GPU acceleration. These models have a wide range of applications from 3D object detection (e.g. cars, pedestrians, etc) to point cloud registration.

Major Players Using TensorFlow


What is the Future?


TensorFlow.js, as there has been a lot of hyperbole, mostly by web developers, that this will make JavaScript (and the Web) the best place to develop and deliver models. This is far from the truth: TensorFlow.js is closer to TensorFlow Lite and Core ML and aims to solve the same problems. There are gold-rush opportunities for imaginative commercial developers and scientists to take advantage of TensorFlow’s enhanced capabilities in all kinds of ways.


As Deep learning is getting acknowledged widely these days, many of the advanced automation capabilities now found in enterprise AI platforms are due to the rapid growth of machine learning (ML) and deep learning technologies. With the use of machine learning constantly increasing   and with JavaScript development becoming ever more popular TensorFlow.js seems to get an increase in popularity in the near future. From the day TensorFlow was released (9th November 2015) till now in 2021, TensorFlow has a lot of updates in which the latest release is v2.4.1 and it is getting even much better every day with various updates and features. TensorFlow combines javascript and deep learning to get a platform which works on browsers for various applications. If your organization is interested in using audio, video, image, or free-text data, deep learning is worth exploring. Tensorflow makes things easy for the user as mostly users have to install software to run the application for deep learning but now with the help of Tensorflow they can use it on their browser which also makes it more convenient to use and also take less time to load and give output which is incredible.


References:

Keywords: Computer Science, Programming Language, Node.js, Computer Language, Java, PHP, ASP.NET, JavaScript, Tensorflow.js, Deep Learning, Neural Network


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