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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.