Technology has empowered machines to think and act like humans. With the advent of artificial intelligence, machines can perform all cognitive functions such as learning or problem solving, just like how humans do. Artificial intelligence, machine learning and deep learning has made the impossible possible.
So what exactly are they?
In simple words artificial intelligence is any computer program or software that is smart as humans. AI is to make computer systems perform tasks that usually requires human help such as hearing or seeing, decision making etc. without any human intervention.
Machine learning is a subset of AI. Just like how humans learn from their experience, Machine learning allows the machine to learn and improve by performing the tasks again and again without hard coding them.
Deep learning is a subset of machine learning. Deep learning is also called Deep Neural networks. They mimic the performance of neuron networks in the human brain using algorithms.
We will see in detail about Deep learning.
What are Artificial neural networks?
Just like how our brains have neurons, in artificial neural networks – a computing system is made up of many processing elements, and they process information just like how a brain functions. Here a particular element is invoked (like a particular neuron is invoked by the brain) to process any information dynamically.
Deep Learning Process:
Just like any machine learning process, deep learning has the following stages.
Step 1: Understanding the problem
Step 2: Identify the data
Step 3: Select the Deep learning algorithm suited for the problem
Step 4: Implement a model based on the algorithm
Step 5: Train the model
Step 6: Test the model.
Working of Deep Learning Algorithms:
All deep learning algorithms have three layers. An input layer, a hidden layer and an output layer.
Input layer as the name suggests is where the input signals are given for processing and output layer is where we get the processed output signal or the final result.
So, the middle layer – hidden layer is where the actual processing happens. Hidden layer has so many processing elements (neurons), and is often not a single layer, but a combination of many layers. The hidden layer processing elements are interconnected, and their connection strength is called weights.
Types of Neural Networks:
Based on connection type:
Feed-forward Neural Networks:
In this type of neural networks, the data is travelled in one direction. There won’t be any loops in the hidden layer.
Recurrent Neural Networks:
In this type of neural networks, there will be many feedback loops in the hidden layer.
Based on number of hidden layers:
Single layer neural network:
As the name suggests this has only one hidden layer.
Multi-layer neural network:
Most of the deep learning neural networks are multi-layered and they have multiple hidden layers.
Based on Memory:
Static neural network:
Static neural network is one where there won’t be any memory. The output is based on the current input given to the algorithm. Feed-forward neural networks are static neural networks.
Dynamic neural network:
Dynamic neural networks are the one the output is based on current input as well as the previous output (feedback).
Based on weights:
Fixed neural network:
In a fixed neural network, the weight (strength between neurons in hidden layers) is fixed.
Adaptive neural network:
In adaptive neural networks, the Weights are not fixed, and they are changed during the training phase.
Other notable types in neural networks are modular neural network, convolution neural network and radial basis function neural network.
Modular neural network:
In this type of neural network, many independent layers contribute to the processing result. Many sub-tasks are performed in the neural networks. There is no interaction in between these networks and contribute to accurate results.
Convolution neural network:
In convolution neural networks, input features are taken in batches e.g. the images are stored in parts. Then they are passed through a filter, going with our example all the images are classified according to their color scale and passed on to a filter say grayscale. Convolution neural networks have more applications in computer vision, image and signal processing.
Radial Basis function Neural Network:
Radial basis function neural network is very similar to feed-forward neural network – but with one change here radial basis function (distance from the center point) is used.
Learning Techniques in neural networks:
Just like other machine learning algorithms, deep learning algorithms also use supervised learning, unsupervised learning and reinforcement learning techniques.
Why Deep learning?
Data we consume every day is increasing manyfold. Deep learning algorithms are powerful tools to compute, process, predict and analyze large volumes of data. Deep learning algorithms are 25% more accurate than normal machine learning algorithms in voice recognition, 27% more accurate in facial recognition and 41% more accurate in image recognition.
Practical Applications of Deep Learning:
1. Voice Recognition & Response
Virtual assistants are the classic example of this one. Even virtual assistants online are based on deep learning where they recognize your language, understand what you say and respond when you interact with them.
2. Translation Software
There are many AI – Deep learning-based translation software, where you can input your query, the same is processed and output is given in requested language – either by text or by voice. This is very useful for travelers.
3. Self-Driven Vehicles
Deep learning has become a game changer when it comes to self-driven vehicles, as they have capacity to read the signs (images) and process them. For example, if the road is temporarily closed due to some work, these cars can read the traffic sign and take the detour or next best route possible to reach the destination.
Lots of customer service work has been reduced because of deep learning. Chat bots or message bots created using deep learning, gives human-like interactions, otherwise what felt like a programmed response.
5. Face recognition
Facial recognition is said to be the highest form of security. With deep learning, the process has become very swift. Even if you grow a beard or sport a new pair of glasses, your phone is able to recognize you, all thanks to deep learning.
6. Medical Industry
Deep learning has become highly sought after in the medical industry. Especially in the field of diagnostics, without any second guesses, the results are accurate when deep learning is used.
Google and every other search engine can predict and complete your query by using deep learning algorithms. Facebook, amazon etc. most of their shopping experience of predicting and suggesting relevant items are using deep learning algorithms.
8. Finance & Marketing
Risk assessment is the most valuable thing in the fintech world. Predictions made using deep learning algorithms are more accurate. Personalization of customer service as mentioned above will help in improved marketing solutions.
As you can see, neural networks aka deep learning is just a drop in the ocean of artificial intelligence. To learn in depth about various deep learning concepts, contact us and enroll in our comprehensive deep learning courses!