What is Deep Learning: Basics That Every Beginner Should Know
Deep Learning (DL)is a part of the field of Artificial Intelligence (AI)and an emerging area of Machine Learning (ML). It involves neural networks and complex computational calculations and helps ‘train’ machines to become better learners.
DL helps machines find patterns to assist in making automated decisions and produce results that mimic human behavioral patterns.
Companies like Google, Toyota, and Nvidia are investing billions in AI and ML research.
Machine Learning — Your Intro To Deep Learning
To understand deep learning better, you need to have a basic grasp of Machine Learning.
When a machine learns concepts, it becomes “intelligent” and the study of algorithms involving real-life examples and experiences is what summarizes machine learning. Examples of machine learning include a robot that can identify different objects and classify them according to their class or find the exact coordinates by using a snapshot of the environment.
The Different Sides Of Machine Learning
There are various types of machine learning algorithms:-
- Supervised learning — It involves supervising the entire computation procedure, providing the machine set results and inputs, and “teaching” it to produce accurate results.
- Unsupervised learning — It involves letting the computer find patterns by itself and produce results without explicit supervision.
- Reinforcement learning — It involves a reward-based system where you teach a machine to perform certain behaviors in order to maximize its rewards. Teaching APIs to play games or learn new skills are great examples of this.
Deep Learning Vs Feature Engineering
Feature engineering forms a part of the machine learning curriculum. It involves extracting data, building models, and letting the machine classify the data and produce certain outputs. The problem with this is, that the more complex the data gets, the more of a hassle it becomes to rebuild models, classify further, and reiterate output scenarios.
Deep learning gains an edge over this through the use of Artificial Neural Networks (ANNs). ANN can build upon their complexities through the use of multiple hidden layers, feature extractions, and classifications.
In comparison to feature engineering, when you learn deep learning, you essentially learn how to mentor machines in extracting data, building their own models, and processing computations all by themselves. You don’t need to feed the machines with raw and user-filtered data for processing. Through the use of multiple layers, machines can recall past data and even recognize new patterns based on previous experiences.
This essentially saves time and additionally, deep learning concepts can be applied to wide areas of life. Just like how you’d teach a brain to think and execute tasks on its own, you teach machine-human thought paradigms through deep learning, the only difference being more layers of classifications are involved.
Deep learning, in a nutshell, is the branch of AI that uses neural networks to teach machines to become better learners and perform complex computations. Let’s talk about Neural Networks and their importance in Deep Learning.
Neural Networks And Pattern Complexity
Pattern recognition is at the forefront of deep learning and involves teaching machines how to recognize patterns using various classification methods. Classification helps machines “classify” data structures according to various properties unique to different objects.
There are several techniques of machine learning and the use of neural networks is one of the most adaptive and state-of-the-art ways.
Neural networks are structures that consist of a set of interconnected web of nodes with hidden layers between them. Bearing resemblance to the complex architecture of the human brain, these nodes process input data based on weights and biases and output results accordingly.
Simply put, neural nets are a way of teaching machines how to learn. The more complex the data inputs are, the more activations these nodes undergo. Neural networks improve machine perception and allow machines to make complex computational decisions based on environmental inputs. The more complex the data, the greater the deep net.
For shallow computations, basic classification tools like SVM and logistic regression work for simple pattern recognition.
Pre-Requisites for Deep Learning
Before diving deep into deep learning methods and applications, one needs to have an understanding of university-level math concepts and have some programming experience with Python in order to progress with deep learning and get started.
Here is what you need to have a basic understanding of, before getting started with Deep Learning:-
- Python
- Knowledge of the Numpy, Scipy, Pandas, and Matplotlib stack
- Foundations of Machine Learning and Statistics
- Linear Algebra (matrices, vectors, etc.)
- Calculus (differentiation, partial derivatives, integration, etc.)
- Numerical computation
- Probability and Information Theory
Having a basic understanding of machine learning foundations and concepts will enable you to advance in your deep learning studies since deep learning is a part of machine learning. Some excellent courses geared towards a strong deep learning introduction for absolute beginners are the Stanford University CS231n course on Convolutional Neural Networks for Visual Recognition and Andrew NG’s Coursera course on Machine Learning.
Deep Learning Applications
Applications of deep learning span across several mediums from facial recognition, geolocation, and visual recognition. Some of the most common applications which involve deep learning examples are:
- Colorization of black and white images
- Finding the geographic coordinates of a given background image
- Building APIs like chatbots and news filtering tools
- Automated sentence correction
- Medical imaging and seismic interpretation
- Converting male portraits to female counterparts and vice versa
- Building bots to play specific games like Super Mario Bros.
According to the deep learning wiki, other applications of deep learning principles include the use of deep learning architectures like recurrent neural networks, and deep belief networks in fields such as natural language processing, social network filtering, machine translation, and bioinformatics.
Other real-world applications of deep learning involve the creation of the boardgame API AlphaGo by Google Deepmind participation in the World Go Championships and the invention of Deep Blue which is an API that surpassed human scores in the World Chess Championships.
Why Learn Deep Learning?
The future of AI is a precursor to why deep learning is integral to its advancement. With University of Illinois scientists achieving a breakthrough in HIV research with the use of Tesla processors to learn how the human genome wraps around the nucleus of a cell, these are but a few of the many feats achieved through the use of deep learning in AI technology.
The structure of deep neural networks and the flexibility of having multiple layers to process various data structures is what makes deep learning very appealing. It gives machines the ability to make decisions and mimic human behavior in terms of various processes.
Conclusion
Creating programs to solve problems is a great feat in itself but there’s no greater feat than being able to teach a machine to think for itself and solve problems on its own without human intervention or assistance. This is one of the multifold reasons to delve into deep learning and why deep learning leads to greater advances and innovations in the field of AI technology.
