Artificial intelligence, machine learning, and the fundamentals of deep learning may sound like a complex subject, but what are they really? You may not be fully aware of it, but there's a good chance you're contributing data to AI unknowingly.
If you don't understand this concept, don't fret. Deep learning is still a pretty complex topic. However, you'd be surprised that humans depend on it. From automatically organizing your phone photos, translating web pages, self-driving cars, or getting you where you need to be using navigation, this concept plays a big part in how our modern world operates. So, find the best Deep Learning Course for yourself. Or just dive into this post to find out how to master Deep Learning!
Ready to dig "deep"? Read on to explore the fundamentals of deep learning.
What is Deep Learning?
Artificial Intelligence (AI) is the intelligence machine displays after humans give it a command. Technically, intelligence is developed when machines are tasked with something that usually requires human intelligence and problem-solving skills.
Machine Learning falls under AI. It is when a machine learns a skill or overcomes a problem by experience without any human intervention.
Deep learning falls under machine learning. It uses artificial neural networks inspired by and designed to mimic the human brain. Algorithms use large amounts of data to help solve the tasks with minimal or no human interaction. The "deep" in Deep learning refers to the layers in the artificial neural network, which consist of input and output layers, and in between, are what's referred to as hidden layers.
The data is filtered through multiple layers, with each time it passes through a layer it compares its previous output, making adjustments and improvements along the way until it reaches its final output layer. Similar to how we humans learn as children, through trial and error.
Deep learning is still a pretty complex area of study, but huge strides have been made in recent years. With advances in computing power and a surge in our data generation that has been estimated to be at around 2.6 quintillion bytes every day, deep learning has become more prominent especially in recent years.
Deep Learning vs. Machine Learning
Machine learning requires data to solve and perform a task. It uses provided data to decide, learn, and choose the correct solution to help solve a problem. Machine learning is capable of learning skills and doing tasks based on the data it has been fed without any more human interaction.
Deep learning uses an artificial neural network to teach itself. Deep learning algorithms require large amounts of data to learn. Although it takes more time for it to learn, it requires no human intervention, and depending on the computing power available, it is faster and much more efficient.
To demonstrate, if you had two machines working on the same problem of separating apples from oranges:
- The machine learning algorithm would use data provided by a user to make the correct choices. It will base all its decisions on the data provided, such as the shape of the fruit, color, or size.
- With a deep learning algorithm, the choice between an apple and an orange are made based on large amounts of data in an artificial neural network without any human interaction.
A downside to deep learning is that it takes more time to process all data. It could take between an hour, a day, a week, even a month for the algorithm to process.
History of Fundamentals of Deep Learning
The 1960s, 70s, and 80s
- 1960: The background of Back Propagation (the use of errors for deep learning training), the fundamentals of deep learning, and the groundwork for a neural network are developed by Henry J. Kelley.
- 1962: Stuart Dreyfus with an active area of research in backpropagation develops a more simple version of the chain rule or what would become a neural network.
- 1965: Earliest manual efforts to create algorithms for deep learning by Alexey Grigoryevich Ivakhnenko.
- The 1970s: An almost decade-long era without any substantial leap forward in research and development for deep learning except for a few independent researchers. Due to limitations in hardware and computing power during this time, it was considered a pretty complex and difficult subject.
- 1979: Kunihiko Fukushima develops the first “convolutional neural networks”. He called his convolutional neural network the “Neocognitron”, the design had multiple layers arranged by hierarchy. Fukushima’s active area of research and design allowed machines to recognize and learn patterns visually.
- 1985: Rumelhart, Hinton, and Williams showed “interesting” distribution results when they demonstrated backpropagation in a neural network. This study sparked a reinvigoration of neural networks and their potential implications and applications for future systems.
- 1989: Yann LeCun while working at Bell Labs, combined convolutional neural networks and backpropagation demonstrating the first practical application of backpropagation. LeCun taught the system to read and recognize handwritten numbers which would be later developed as the system to assist in reading handwritten checks.
The 1990s through 2000s Deep Learning
- 1995: Dana Cortes and Vladimir Vapnik developed a system for mapping and recognizing similar data, they called it the support vector machine.
- 1997: Sepp Hochreiter and Jurgen Schmidhuber developed the long short-term memory or LSTM for recurrent neural networks.
- 1999: Computers became faster. The introduction of graphic processing units or GPUs increased computing speeds a thousandfold that span for a decade. The excitement sparked another reinvigoration of neural networks and research for more applications.
- 2001: Gartner, previously known as the META Group conducted a research study on data growth. It outlined the challenges, opportunities, the escalating level of data, the speed at which data travels, and basically issuing a warning for the oncoming Big Data. In the 2000s deep learning development was about to get a big boost due to the Big Data surge that was just about to begin.
- 2009: An AI professor at Stanford, Fei-Fei Li launched ImageNet. More than fourteen million images were labeled and collected into a database. This free database of labeled images is then used to train a neural network which made the whole process simpler, more efficient compared to being pretty complex and difficult before. The 2000s era was fraught with database management due to the big surge of data at the beginning of the decade.
2011 to Present
- 2011: GPU speed and performance improves significantly. Convolutional neural networks can now be trained minus the pre-training that was done before with each layer. Deep learning thrives in a world with fast computing speed and it has proven faster and more efficient.
- 2012: The results of The Cat Experiment, a research study by Google Brain was released. The study delved into the possibilities and challenges of unsupervised learning. The experiment was shown to be 70% more successful than its predecessors in learning from unlabeled data.
Major Concepts and Modern Applications
Probably the easiest way in understanding the fundamentals of deep learning is to follow what kind of product or service is already using these technologies. Here are several major concepts that the rest of us can understand.
Self-driving cars are truly a vision of the future, but we're actually almost there. Companies such as Tesla and Nissan have incorporated deep learning into their cars, ultimately making travel safer by eliminating human error, providing diagnostics, and computer-aided safety measures.
Diagnostics, monitoring, and prediction of medical needs based on patient data are only a few examples of medicinal applications for deep learning. AI is creating a whole new potential future for managing the population’s health where automated warning and detection are available for common people. And where computer-assisted diagnosis tools aid health professionals in making better decisions.
Voice Recognition Systems
AI might be a difficult subject to grasp but this is probably the most common brush with deep learning that almost everyone has had. Through voice-activated assistants from companies such as Google or Apple. Voice search and intelligent voice-activated assistants can be found in nearly all smartphones and modern devices today. Although deep learning is still evolving in terms of this application, the future is bright with voice recognition and its exciting potential implications.
We have all used the translation button that companies such as Google provides. Instantly changing the language of a webpage to a language you can understand. A lot of improvements have been done and deep learning has seen increased efficiency in both text and image translations.
Automated text generation is achieved by using neural networks that study and learn how a group of words is formed and create its own text based on that model. It is capable of copying the style of the piece it studied, the use of punctuations, sentence formation, and spelling. Implications for this could be paving the way on how future literature is made. We could be on the cusp of seeing an AI William Shakespeare.
We've all seen this on our mobile phones and modern devices. The most pedestrian of applications is facial recognition, which has become commonplace. Making it much easier to let your friends know they're in a photo you just posted on social media.
Automatic Image Colorization
This is paving the way to a new age of crisper images and unprecedented levels of image reconstruction and colorization. Deep learning is capable of adding colors to black and white images or photographs. What used to be a highly specialized and complicated field of image colorization is now another AI task. The same way as a child would look at a page of a coloring book, deep learning approaches the task in a similar manner.
Deep learning has now made it possible for agencies to create tailor-made ads for a specific audience. Using data to train machines, advertisers have increased the effectiveness of their ad campaigns. Boosting the exposure by ensuring their advertisements reach the intended target market and in turn, increase the return on their investments.
Earthquake Early Warning
Deep learning has been used by Harvard scientists to conduct and compute complex viscoelastic readings. Viscoelastic computations are utilized in earthquake early warning systems and predictions. The use of deep learning has dramatically reduced the time needed to finish the calculations by a factor of 50,000%.
Wrap up: The Future of Deep Learning
Modern machine learning, reinforcement learning, python fundamentals, calculus, and other subjects that get used in these fields may seem like a difficult subject to grasp, but as you've seen in the examples above, the details of inventions using AI are exciting nonetheless (even for the rest of us who find AI a complicated field.
Currently, the processing of the mass amounts of "Big Data" relies heavily on deep learning as it has proved to be the more efficient option. Of course, it is a continuously-developing concept and you can expect a wide array of fields still underway.
The implications of deep learning on the future of AI cannot be stated enough. As technologies and hardware continue to evolve, we will continue to see exciting developments and new applications.
Today, Deep learning has become an integral part of how we interact with our world and with each other and it will undoubtedly play a huge factor in how our future will unfold.