Deep Learning – A very young and limitless field. Its a class of machine learning where theories of the subject aren’t strongly established and views quickly change almost on daily basis.
“I think people need to understand that deep learning is making a lot of things, behind the scenes, much better” – Sir Geoffrey Hinton
This is part 1 of 2 parts story on DeepLearning & basic terms which revolve (may evolve around as well) around it.
Why Deep Learning?
- Human Brains have deep architecture.
- Humans organize their ideas hierarchically, through the composition of simpler ideas
- Insufficiently deep architectures can be exponentially inefficient.
- Deep architectures facilitate feature and sub-feature sharing.
What is Deep Learning?
With lots of noise I can say “Deep learning is undeniably mind-blowing” and “deep learning can be used with too much of ease to predict the unpredictable”. In my personal opinion “We all are so busy in creating artificial intelligence by using a combination of non-bio-neural networks and natural intelligence”.
I hope & pray that there would be no time when we need to do the reverse where will need Artificial Intelligence to create Natural Intelligence in future.
That said, it is my impression that the representation-focused perspective of artificial neural networks is presently very popular. Deep learning is a learning scheme that approaches the learning problem by learning the underlying representations; too much of learning. I think that’s why it also called as representation learning.
Because this is young and limitless field as on date thus theories, conclusions aren’t strongly established and views quickly change almost on daily basis. Some people also call it as cutting edge emerging technology and a breakthrough in AI domain. This depicts what machines can do and its just a start.
Deep Learning (DL) has revolutionised, image processing & classification and also speech recognition with high accuracy. Business leaders and developers community absolutely need to understand what it is, what it can do and how it works.
What is Neuron
On a high level, we have 3 types of neuron in the human body e.g sensory neurons, motor neurons and relay neurons. Please note we are not at all claiming or making any efforts to demonstrate any knowledge about biological science here, we neither have any idea nor we claim so.
The information is here taken from open internet to just make a small understanding of the artificial neuron. The typical structure of Human Neuron as below.
Artificial neural networks are originally designed to approximate biological neurons. They are mathematical constructs, Each “neuron” is a relatively simple element — for example, summing its inputs and applying a threshold to the result, to determine the output of that “neuron”.
Artificial Neural Networks (ANN)
As per Dr Robert Hecht-Nielsen, the inventor of one of the first neurocomputers, a neural network or artificial neural network (ANN) is “A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”
Artificial Neural Networks (ANN) are inspired by the human brain. The structure in this neuron is now used for machine learning and artificial intelligence. With these artificial neuron networks, various problems especially where layering is needed for refinement and getting more granular details are needed now getting solved.
The neural networks are now part of research subject like Neuroinformatics (a research field concerned with the organization of neuroscience data by the application of computational models and analytical tools) and it requires extensive training before implementing in real time problem-solving environment. they can solve problems.
Working Method of Neural Network
Neural Network for real can work as a visual guide for data evolution strategies also would be a highly scalable alternative to deep reinforcement learning. Information flows in neural network happens in two ways. Below mentioned methods are a common design for a feedforward network.
- At the time of it’s learning or “being trained”
- At the time of operating normally or “after being trained”
Patterns of information are fed into the network via the input units, which trigger the layers of hidden units, and these, in turn, arrive at the output units. Though this subject is large and complex because there are many different types of artificial neural network models. DL’s most common model is backpropagation. This has become the foundation for most of the others, is the 3-layer fully-connected.
Please note: In DL chain, not all units get triggered all the time. Each unit receives inputs from the units to its left, and the inputs are multiplied by the weights of the connections they travel along.
Characteristics of Deep Learning that make it different from Machine Learning
Though machine learning has laid down the foundation for deep learning to grow and evolve. At the same time, Deep learning has taken key features from the machine learning model. Interesting fact & truth here is it takes it to step further by constantly teaching itself new abilities and adjusting existing ones.
- Deep learning is cultured technology with the highest degree of accuracy for self-learning capabilities. This makes the results from this often in near accurate results and faster processing.
- It learns high-level, non-linear features necessary for accurate classification.
- Deep Neural Networks are the first family of algorithms within machine learning that do not require manual feature engineering, rather, they learn on their own by processing and learning the high-level features from raw data.
- Deep neural networks are fed through raw data; which learns to identify the object on which it is trained or “being trained”.
This unique type of algorithm (deep learning) has far surpassed any previous benchmarks for classification of images, text, and voice.
In the next post, we will talk about below important aspect of this topic
- Deep learning Computational Models i.e Feed forward propagation and backward propagation
- Deep learning Algorithms – High-level of view
- Implementation of Deep Learning Models
- Notable Use Cases & Applications
- Deep learning limitations
Lighter moment (Joke)
Let’s taste the real AI and how it will amaze us soon. Below joke has been floating over social media. It paints an excellent picture and reality of the future ( For real) impact of deep learning technology in our lives, business and personal works. Note this joke is taken from one of the social media platforms.
- Hello! Somi’s pizza?
- No sir it is Naomi’s pizza
- So is it a wrong number?
- No sir, Naomi bought it
- OK. take my order, please.
- Well, sir, you want the usual?
- The usual? You know me?
- According to your caller ID, in the last 12 times, you ordered pizza with cheeses, sausage and thick crust
- OK! This is it
- May I suggest to you this time ricotta, arugula with dry tomato?
- What? I hate vegetables
- But sir your cholesterol is not good
- How do you know? -through the subscribers guide we have the result of your blood tests for the last seven years.
- Okay, but I do not want this pizza please. I already take medicine
- But sir you have not taken medicine regularly, four months ago, you only purchased a box with 30 tablets at Tony’s drug store
- I bought more from another drugstore
- It is not showing on your credit card
- I paid in cash
- But sir you did not withdraw that much cash according to your bank statement
- I have other sources of cash
- This is not showing as per you last Tax form unless you bought them from undeclared income source
- WHAT THE HELL?
- Enough! I am sick of google, facebook, twitter, whats App. I am going to an Island without the internet, where there is no cell phone and no one to spy on me.
- I understand sir, but you need to renew your passport as it has expired five weeks ago… !!!!
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