In general, Deep Learning is an artificial intelligence (AI) process that mimics the way the Human brain makes patterns and processes information in order to make decisions. A deep learning network utilizes unstructured or unlabeled data to learn unsupervised from it. Deep neural networks are also referred to as deep neural learning.
- AI in the form of deep learning simulates the way the human brain processes data so that it can detect objects, recognize speech, translate languages, and make decisions.
- With deep learning AI, algorithms can learn without supervision and can use both unlabeled and unstructured data.
- In addition to detecting fraud and money laundering, deep learning can help with machine learning.
Why is Deep Learning Important?
The neural networks are universal function approximates, as some previous responses have mentioned. So practically any continuous function can be approximated well with it. It’s not something I have much experience with, so I can’t go into more detail. Given enough data, a neural network can approximate a blackbox well if the blackbox (a continuous function) can take in an input (such as an image) and return an output (such as a class label).
Life is made easier by it. Unsupervised deep networks are capable of learning features. There has been a lot of classic machine learning work that involved handcrafting features for specific applications (like speech recognition, image classification). The process of deep learning eliminates the need for feature engineering. It is possible to learn abstract features in a deep neural network (the deeper you go in the network, the more abstract the features will be) with enough data and a suitable network architecture (there are several heuristics for crafting such an architecture for a particular problem). If you have a deep network, you can throw it into a lot of applications and let the network learn features on its own.
Carlos was right in saying that ANNs (Artificial Neural Networks) are powerful in that they can be used as universal approximates, but that’s not the only reason why deep learning is a powerful technique. The idea of deep learning goes further than just stacking layers of neurons and training them; it’s about considering compositionality (the idea that the world around you is composed of complex features that are composed of smaller simpler features) which can be useful for generalizing machine learning.
It has become a crucial component of machine learning to apply deep learning techniques. With huge amounts of data being generated every day, powerful algorithms are necessary in order to analyze this data.
These days, images, text, and unstructured data make up most of the data generated. In comparison with the data types mentioned above, numerical data is generated less frequently.
When compared to machine learning, deep learning is better at handling text, images, and other types of data. Examples are autonomous cars.
This is how Deep Learning works…
Deep learning evolved along with digital transformation, a process that has brought about an explosion of data from around the world in all forms. In addition to social media and search engines, big data is drawn from e-commerce platforms and online cinemas, Fintech applications like cloud computing can facilitate the sharing of such vast amounts of data.
The following image represents how deep learning is differ from machine learning.
The data is usually unstructured, but such a vast amount of information can take humans decades to comprehend and glean meaningful information from it. Businesses are increasingly adapting to AI systems for automated support, realizing the huge benefits they can derive from unraveling this wealth of information.
It takes humans decades to process and understand vast amounts of unstructured data, but deep learning can conquer them in months or even hours.
Do you know what makes Deep Learning superior to traditional Machine Learning?
Artificial Intelligence is enraged! Suddenly, everyone is talking about it, regardless of whether they understand it or not. Machine Learning and Deep Learning, two very popular concepts that are essential to understanding artificial intelligence, are the base of contemporary artificial intelligence, Deep Learning is gaining much popularity lately because of its high degree of accuracy when trained using a huge amount of data.
Here is a Google trend for the keyword Deep Learning to give you some idea of its popularity
Today, machine intelligence is becoming increasingly important in the software industry. It has become imperative for every sector to implement Machine Learning as a means of making machines more intelligent. A machine learning algorithm is simply an algorithm that parses data, discovers what it can learn from it, and then uses that knowledge to make intelligent decisions.
Everywhere you look, Machine Learning is being used. Netflix or Facebook know when you take a picture of your friends and which show you will want to watch next. It is possible for customer service representatives to predict if you’ll be happy with their support before you ever fill out the CSAT survey.
It’s important to know that traditional Machine Learning algorithms, no matter how complex they may appear, are still very much like machines. Unlike robots, they require a lot of domain expertise, a human intervention that can only accomplish what they were designed for. The rest of the world, including AI designers, can see some promise with deep learning in this area as well.
The way Deep Learning works is illustrated in the image above:
- At its lowest level, the network is focused on patterns of local contrast.
- In turn, the next layer will be able to determine which objects resemble eyes, noses, or mouths based on those patterns of local contrast
- Lastly, these facial features can be applied to face templates by the top layer.
- As a deep neural network’s layers increase, it is able to produce increasingly complex features.
What does Facebook do with the tagged and labeled persons you upload? Similar to what was stated in the above example, Facebook uses Deep Learning. Hopefully, now that you have understood how Deep Learning works, you will be able to recognize how it can outperform Machine Learning in those cases where we cannot predict all the factors that could influence the outcome. The advantages of Deep Learning over Machine Learning are that it is able to draw inferences from data sets without proper labeling, thus overcoming the drawbacks of machine learning.
A deep learning network is a large neural network
The Deep Learning technologies resulting from the Google Brain partnership were subsequently utilized by many of Google’s services after Andrew Ng from Coursera and Chief Scientist at Baidu Research founded the company.
Deep learning has been discussed extensively by him, so he is a good place to start.
Andrew referred to deep learning as traditional artificial neural networks in early talks on deep learning. As he described deep learning in the 2013 talk titled “Deep Learning, Self-taught Learning and Unsupervised Feature Learning,” deep learning can be described as:
We hope to achieve the following by using brain simulations:, Create better and easier-to-use learning algorithms, Making significant advancements in artificial intelligence and machine learning, We have a great opportunity here to make progress toward real AI, in my opinion
Deep Learning: Top Applications in a Wide Range of Industries
- Autonomous Vehicle
- Detecting fraud news and aggregating news
- Language processing using natural language
- A virtual assistant
- A variety of entertainment
- A visual recognition system
- Detecting and preventing fraud
- The healthcare industry
- Personalized features
- Early Detection of Developmental Delay in Children
- Adding colour to black and white photographs
- Silent movies can be made more interesting by adding sounds
- Machine translation with automatic translation
- The Automatic Generation of Handwriting
- A game that plays automatically
- Translation of language
- Restoration of pixels
- A description of each photo
- Predictions of demographic and electoral trends
- Deep Dreaming
Image Courtesy: TowardsDataScience, and mygreatlearning
Several exciting new neural network technologies are part of deep learning. It is now possible to train neural networks that can process table data, images, text, and audio as well as output data through a combination of advanced training techniques and neural network architecture components. A neural network that is trained in deep learning can be taught hierarchies of information as the human brain can.
Throughout this websites articles, the student will become familiar with classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), and General Adversarial Networks (GAN).
In addition to covering the applications of these architectures to computer vision, time series, security, and natural language processing (NLP), we will also cover data generation. The High Performance Computing (HPC) aspects will demonstrate how deep learning can be manipulated both on Graphical Processing Units (GPUs) and grid computers. This websites articles focuses primarily on application of deep learning. Some theoretical background is introduced, upcoming articles demonstrates how deep learning works with Google TensorFlow and Keras by using the Python programming language. There is no required knowledge of Python prior to this book, but it will be helpful if you have some familiarity with a programming language.