There are certain technologies and their applications that hold the power to change the world we live in. Deep learning is one such technology. So what is deep learning and why is it important?
Deep learning is a subset or a branch of machine learning. The main idea behind deep learning is to teach computers to learn the way we learn.
How do humans learn? We learn by example, by trial and error and by life experiences. You can do something similar to a computer. You can teach it by showing a huge data set and essentially training the computer. Deep learning techniques use neural networks to train the computer.
Deep learning is being used in everything from driverless cars to home automation systems. You can train a computer to recognize various objects on the road and what to do when it comes across each object. You can train it to recognize cancer cells. You can also train it to translate speech to text and perform an action based on the speech.
Since all of these applications have the potential to change the world we live in for the better, deep learning techniques definitely require your utmost attention. Here are the top 7 deep learning algorithms that you should definitely learn in 2018:
1. Decision Trees:
Decision trees, as the name suggests are visually similar to a tree with branches. Each node represents a decision, or a question and each branch or leaf that extends from the node is a course of action or a method for classification. Decision trees are used in deep learning for classification. They are supervised algorithms that are algorithms where you supply the input and the target output.
2. K-Means Clustering:
K-means clustering is an unsupervised classification algorithm. You only supply the inputs, and the algorithm identifies the inherent structure present in the input. K-means clustering divides the data set into K clusters based on their similarities. Each cluster has a centroid, and all points in that cluster are closest to that centroid. This is a powerful and yet, simple way of classifying data in deep learning.
3. Back Propagation:
A neural network trains the computer by passing different data set through a neural network. But how does the neural network know how to perform the classifications? This is where backpropagation comes into the picture. When a node processes the information and passes it on to the next layer, it is also given information from that layer about the error in its decision. The network can adjust itself to minimize the error. Since the error is propagated back through the network, the method is called backpropagation. This method helps the network perform accurate classifications.
4. Batch Normalization:
This algorithm is used to improve the speed and stability of the neural networks used in deep learning. In deep learning, we need to deal with huge datasets, and therefore, speed is extremely important. The input of each layer needs to be normalized so that the mean of the output activation is zero and its standard deviation is one. Each layer in the neural network is the input layer to the next layer. The normalization needs to be performed at every layer. This not only improves the training speed of the network, but it also simplifies the deep learning process.
Skip-gram is an algorithm used in word-embedding. The basic idea is that two words that have the same context are similar and therefore, you only need to consider one of them and you can skip the other while training the network. The network should then be able to predict the skipped terms.
6. Transfer Learning:
It takes a lot of time to train a network from scratch. Transfer learning is a workaround to this problem. You have a network that has been trained for one purposeand you can reuse it for another by just removing the last few layers and retraining the network with the new data set. The retraining gets done much faster, and you can save a lot of time. Since some of the applications of deep learning are very similar, transfer learning is a really good way to use the knowledge you already have and apply it to a different purpose.
7. Stochastic Gradient Descent:
Stochastic Gradient Descent (SGD) is an iterative algorithm used to adjust the parameters so that you can find the minimum of the function. In deep learning, it is used to minimize the errors. Stochastic gradient descent works by finding the path to the minimum that offers the least resistance, that is, the patch with the steepest descent in the directing of the decrease of the function. Unlike other minimization techniques, SGD uses a sample set and not the whole set for each iteration. It also uses a learning rate to ensure that change in each iteration is just the right size and not too small or too large. SGD is faster and provides a more accurate result when compared to the other minimization techniques.
In the tech industry, things are constantly evolving and the only way to stay on top of the game is by keeping yourself up to date about the latest trends and technologies. Deep learning is here to stay and the sooner you get familiarized with it, the better it will be.