Introduction Deep Learning

Introduction to Deep Learning

Deep Learning is a part of machine learning, which is a subset of Artificial Intelligence. It enables us to extract information from the layers present in its architecture. It is used in Image Recognition, Fraud Detection, News Analysis, Stock Analysis, Self-driving cars, and Healthcare like cancer image analysis, etc. By inputting more data into the network, the layers get trained very well. They can be classified into Supervised, Semi-Supervised, and Unsupervised categories. Each layer is known for extracting information specifically. For example, in Image recognition, the first layer will find the edge, lines, etc, second layer like the eye, ear, nose, etc.

Applications of Deep Learning

Given below are the applications of Deep Learning:

1. Healthcare

From Medical image analysis to curing diseases, Deep Learning played a huge role, especially when GPU processors are present. It also helps Physicians, Clinicians, and doctors to help patients out of danger, and also they can diagnose and treat the patients with apt medicines.

2. Stock Analysis

Quantitative Equity Analysts are getting more benefits, especially in finding the trends for a particular stock, whether it will be bullish or bearish, and they can use many more factors like no of transactions made, no of buyers, no of sellers, previous day closing balance, etc when training the deep learning layers. Qualitative Equity Analysts use factors like return on equity, P/E ratio, Return on Asset, Dividend, Return on Capital Employed, Profit per Employee, Total Cash, etc, when training the deep learning layers.

3. Fraud Detection

These days, hackers, especially those based out of the dark- web, have found ways to steal money digitally across the globe using different software. Deep learning will learn to find these types of fraudulent transactions on the web using a lot of factors like Router information, IP addresses, etc. Autoencoders also help financial institutions save billions of dollars in terms of cost. These types of fraudulent transactions can also be detected by finding outliers and investigating the same.

4. Image Recognition

Suppose say the city police department has a people database of the city, and they wanted to know in public gatherings who is involved in crimes, and violence using public webcam available in the streets this deep learning using CNN (Convolution Neural networks) helps a lot in finding the person who was involved in the act.

5. News Analysis

These days the government makes a lot of effort, especially in controlling the spread of fake news and its origin of it. Also, during poll surveys like who would win elections in terms of popularity, which candidate has been shared by most people on social media, etc, and analysis of tweets made by country people using all these variables, we can predict the outcomes in deep learning, but also there are some limitations to it, we don’t know the data authenticity whether its genuine or fake. etc, or whether the necessary information has been spread by bots.

6. Self-Driving Cars

Self-driving cars use Deep Learning by analyzing the data captured in the cars made in different terrains like mountains, deserts, Land, etc. Data can be captured from sensors, public cams, etc which will be helpful in testing and implementation of self-driving cars. The system must be able to ensure all the scenarios been handled well in training.

Why do we Use Deep Learning?

To help improve the efficiency of predictions, to find the best possible outcomes, and for model optimization. When the data is huge, it reduces the cost in the company in terms of insurance, sales, profit, etc. Deep learning can be very useful when there is no particular structure to data means to analyze data from audio, video, image, numbers, document processing, etc.

Characteristics of Deep Learning

Given below are the characteristics of Deep Learning:

1. Supervised, Semi-Supervised or Unsupervised

When the category labels are present while you train the data, then it is Supervised learning. Algorithms like Linear regression. Logistic regression and decision trees use Supervised Learning. When category labels are not known while you train data, then it is unsupervised learning. Algorithms like Cluster Analysis, K means clustering, and Anomaly detection uses Unsupervised Learning. The data set consists of both labeled and unlabelled data then we call it is Semi-Supervised learning. Graph-based models, Generative models, Cluster assumptions, and Continuity assumptions use Semi-Supervised learning.

2. Huge Amount of Resources

It needs advanced Graphical Processing Units for processing heavy workloads. A huge amount of data needs to be processed, like Big data in the form of structured or unstructured data. Sometimes more time is also required to process the data, it depends on the amount of data fed in.

3. Large Amount of Layers in Model

A huge amount of layers like input, activation, the output will be required, sometimes the output of one layer can be input to another layer by making a few small findings, and then these findings are summed up finally in the softmax layer to find out a broader classification for final output.

4. Optimizing Hyper-parameters

Hyperparameters like no epochs, batch size, no of layers, and learning rate, need to be tuned well for successful model accuracy because it creates a link between layer predictions to final output prediction. Over-fitting and under-fitting can be well handled with hyper-parameters.

5. Cost Function

It says how well the model performs in prediction and accuracy. For each iteration in Deep Learning Model, the goal is to minimize the cost when compared to previous iterations. Mean absolute error, Mean Squared Error, Hinge loss, and Cross entropy are different types according to different algorithms used.

Advantages of Deep Learning

• Solve Complex problems like Audio processing in Amazon echo, Image recognition, etc, reduce the need for feature extraction, and automated tasks wherein predictions can be done in less time using Keras and TensorFlow.
• Parallel computing can be done, thus reducing overheads.
• Models can be trained on a huge amount of data, and the model gets better with more data.
• High-Quality Predictions when compared with humans by training tirelessly.
• Works well-unstructured data like video clips, documents, sensor data, webcam data, etc.

Conclusion

When a company adopts Deep learning, lots of benefits are present, especially in the future. Most of the manual jobs will be eliminated, complete automated processes in manufacturing, like robots doing the assembly process. Even for the government to adopt better financial, and economic policies, help financial institutions decrease fraudulent transactions, etc. Help courts for speedy process of cases and loopholes in the law.

This has been a guide to Deep Learning. Here we discuss the introduction, applications, characteristics, and advantages of deep learning. You may also have a look at the following articles to learn more –

1. Deep Learning Technique
2. Perceptron Learning Algorithm
3. Machine Learning Software
4. Guide to Deep Learning Toolbox