Deep Learning

Deep Learning

Deep learning is a very complex subject and it may be difficult to understand. Let’s try to make it easy.

What is Deep Learning?

Google and Facebook are, however, trying to identify words spoken to categorize them. They are also trying to help different machines identify the relationship between alternate objects in the training data set and assess the relationship between different variables or data points.

For example, if you want a computer to interpret “this is an elephant” exactly that way instead of “this is a collection of pixels”, you must determine a way to map some features of the elephant to other complex features. For instance, you can convert a line, curve, pixels, sounds of alphabets, and much more if you know how to transform the features of that entity into ones that can be recognized by the machine. It can then use indexing or inference to predict the output. This type of learning is called “deep learning”.

How does Deep Learning work?

The neural network architecture is used in most deep learning techniques, and it is for this reason that deep learning models are known as deep neural networks. The term “deep” refers to the many hidden layers in the network. A traditional neural network can only have up to two hidden layers, but a deep neural network can have close to 150 of them. Deep learning models use large data sets called the training data set and neural networks to learn features from the data, and due to this, there is no need for the engineer to manually extract features from the data to train the machine.

One of the most common types of deep networks is called the convolutional neural network, or CNN. This type of network is well suited to process two-dimensional data, such as images. A CNN combines the features it has learned with the features in the input data and uses the two-dimensional convolutional layers to process information.

CNN eliminates the need to extract features from data manually. This means that the engineer does not need to classify the features or identify the images to assist the network in categorizing them. The CNN extracts the features directly from the images or input data. The engineer does not train the data to choose some features from the information provided to it. The CNN learns what features it needs to look for when the engineer feeds it the training data set. It is for this reason that computers or models with CNN are used to classify objects.

CNN learns to identify the different characteristics and structures of an image. It does this by using the many hidden layers within the network. Every hidden layer identifies complex features of the image, and the complexity increases as the hidden layers increase. For example, the initial hidden layer can detect colors in the image, while the last layer can identify different objects in the background.

Why Deep Learning Is Better than Traditional Learning Methods?

Deep learning is a type of machine learning. Let us consider an instance where an engineer is training a machine using a machine learning and deep learning model to categorize images. In machine learning, the engineer trains the model by extracting the relevant features from the training data set and providing that information to the machine. The machine then uses these features to categorize objects present in the images. In addition to this, deep learning also performs “end-to-end” learning. In this process, the network is given the training data set and is asked to perform a task, like classification, and the network learns to do this without the help of the engineer.

Another difference is that shallow learning algorithms converge with the increase in data, while a deep learning algorithm will scale with data. In other words, the hidden layers in the deep neural network continue to learn and improve in their functioning as the size of the data set increases. Shallow learning algorithms refer to those machine-learning algorithms that plateau at a specific performance level when the engineer adds more training data or examples to the network.

Therefore, in machine learning, the engineer must provide the machine with a classifier and feature to sort images, while with deep learning, the machine learns to perform these functions by itself.

What are the applications of Deep learning?

If neural networks approach the way that humans think, deep learning takes the idea a step further.

  • Adding Sound to Silent Movies
    In this task, the deep neural network must develop or recreate sounds that will match a silent video. Let’s look at the following example. We need the network to add sounds to a video where people are playing drums. The engineer will provide the network with close to 1,000 videos with the sound of the drum striking many surfaces. The network will identify the different sounds and associate the video frames from the silent video or movie with the pre-recorded sounds and then select the sound from the database that matches the video in the scene. This system is then evaluated using a Turing test for which human beings were asked to differentiate between the real and synthesized video. Both CNN and LSTM neural networks are used to perform this application.
  • Object Detection and Classification in Images
    In this application, the deep neural network identifies and organizes the objects in an image by classifying the images into a set of previously known objects. Very large CNNs have been used to achieve accurate results when compared to the benchmark examples of the problem.
  • Automatic Colorization of Images
    You can now use deep learning networks to automatically add color to black and white photographs. A deep learning network will identify the objects in the image and their context within the photograph, then adding color to the image using that information. This is a highly impressive feat. This capability increases the use of large convolutional and high-quality neural networks like ImageNet. This approach involves the use of supervised layers and CNNs to recreate an image by adding color to it.
  • Automatic Translation
    Deep neural networks can translate words, phrases, or sentences from one language to another automatically. This application has existed for a long time now, but the introduction to deep neural networks has helped it achieve great results in certain areas of the translation of images and text.

    For the translation of a text, the engineer does not have to feed the deep neural network with a pre-processing sequence. This allows the algorithm to identify the dependencies between the words in a sentence and map them to a new language. The stacked networks in a large LDTM recurrent neural network are used for this purpose.

    Once the network identifies these letters, it can transform them into text, translate the text into a different language, and recreate the image with the translated text. This process is known as instant visual translation.

  • Automatic Handwriting Generation
    For this application, the engineer must feed the deep neural network with a few handwriting examples. This helps the network generate new handwriting for a given word, phrase, or sentence. The data set that the engineer feeds the network should provide a sequence of coordinates that the writer uses when writing with a pen. From this data set, the network identifies and establishes a relationship between the movement of the pen and the letters in the data set. The network can then generate new handwriting examples. What is fascinating is that the network can learn different styles and mimic them whenever necessary.
  • Automatically Playing a Game
    This is an application where a machine learns how it can play a game using only the pixels on the screen. Engineers use deep reinforcement models to train a machine to play a computer game. DeepMind, which is now a part of Google, works primarily on this application.
  • Automatic Text Generation
    For automatic text generation, the engineer will feed the network with a data set that only includes text. The network learns it and can generate new text character-by-character or word-by-word. The network can learn how to punctuate, spell, form sentences, differentiate between paragraphs, and capture the style of the text from the data set.

    An engineer will use large recurrent neural networks to perform this task. The network establishes the relationship between the items in the many sequences in the data set and then generates new text. Most engineers choose the LSTM recurrent neural network to generate text since these networks use a character-based model and generate only one character at a time.

  • Automatic Image Caption Generation
    As the name suggests, when given an image, the model must describe the contents of the image and generate a caption. In 2014, many deep-learning algorithms used the models for object detection and object classification in images to generate a caption. Once the model detects objects in the image and categorizes them, it will need to label those objects and form a coherent sentence. This is an impressive application of deep learning. The models used for this application utilize large CNNs to detect the objects in the images and a recurrent neural network like the LSTM to generate a coherent sentence using the labels.

What is the future of Deep learning?

Deep learning is getting more and more practical applications with each passing day as some long-standing problems in various industries that were simply too costly to do any other way are now being reexamined. Finally, neural networks and deep learning can provide genuinely novel insights that were applied and can boost productivity beyond human capabilities.

The analysis showed that China has the fastest rise in deep learning-related business ideas, with European countries falling behind and France being the very worst. The explanation for this effect is that Chinese business, research, and manufacturing sectors exist in tightly clustered regions, with the Chinese government having lax regulations on any research that promotes business growth and advances Chinese supremacy on the global market of ideas. This kind of industriousness does tend to produce items of subpar quality but fosters innovation, cost-cutting, and quick turnaround.

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