What is Deep Learning ??¶

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

Who is the God Father of Deep Learning?¶

• Geoffrey Hinton is the father of Deep Learning
• Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto

• Geoffrey Hinton
• Noam Chomsky
• John McCarthy
• Marvin Minsky
• Allen Newell
• Ray Kurzweil
• Andew Ng
• Yann LeCun

• Deep Learning is a subfield of Machine Learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks (ANN).

Deep Learning is Large Neural Networks¶

• Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.
• He worked at Google, where he founded and was director of Google Brain Deep Learning Project.
• In 2014, he joined Baidu as Chief Scientist, and carried out research related to big data and A.I.

Practical Examples of Deep Learning¶

• Virtual assistants : Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.

• Translations : In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.

• Vision for driverless delivery trucks, drones and autonomous cars : The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.

• Chatbots and service bots : Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.

• Image colorization : Transforming black-and-white images into color was formerly a task done meticulously by human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.

• Facial recognition : Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.

• Medicine and pharmaceuticals : From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.

• Personalized shopping and entertainment : Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.

• Automatic Machine Translation : This is a task where given words, phrase or sentence in one language, automatically translate it into another language.

• Character Text Generation : This is an interesting task, where a corpus of text is learned and from this model new text is generated, word-by-word or character-by-character. The model is capable of learning how to spell, punctuate, form sentences and even capture the style of the text in the corpus. Large recurrent neural networks are used to learn the relationship between items in the sequences of input strings and then generate text.

• Image Caption Generation : Automatic image captioning is the task where given an image the system must generate a caption that describes the contents of the image.

• Colorization of Black and White Images : Image colorization is the problem of adding color to black and white photographs. Deep learning can be used to use the objects and their context within the photograph to color the image, much like a human operator might approach the problem. This capability leverage the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colorization. Generally, the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.

• Advertising : Advertising is another key area that has been transformed by deep learning. It has been used by both publishers and advertisers to increase the relevancy of their ads and boost the return on investment of their advertising campaigns. For instance, deep learning makes it possible for ad networks and publishers to leverage their content in order to create data-driven predictive advertising, real-time bidding (RTB) for their ads, precisely targeted display advertising and more.

• Adding Sounds To Silent Movies.

• Object Classification in Photographs.

• Automatic Handwriting Generation.

• Automatic Game Playing.

Artificial Neural Network Structure¶

An Artificial Neural Network (ANN) is modeled on the brain where neurons are connected in complex patterns to process data from the senses, establish memories and control the body. An Artificial Neural Network (ANN) is a system based on the operation of biological neural networks or it is also defined as an emulation of biological neural system.

Advantages:¶

• A neural network can perform tasks in which a linear program cannot perform.
• When an element of the neural network fails, it can continue without any problem by their parallel nature.
• A neural network does not need to be reprogrammed as it learns itself.
• It can be implemented in an easy way without any problem.
• As adaptive, intelligent systems, neural networks are robust and excel at solving complex problems. Neural networks are efficient in their programming and the scientists agree that the advantages of using ANNs outweigh the risks.
• It can be implemented in any application.

Disadvantages:¶

• The neural network requires training to operate.
• Requires high processing time for large neural networks.
• The architecture of a neural network is different from the architecture and history of microprocessors so they have to be emulated.