Introduction to Deep Learning
AI Adoption changes the way of working for all industries. Various approaches can be used to implement AI. One of these approaches is Deep Learning. Its algorithms can give highly accurate outputs. But still, the adoption in the Finance industry is limited. The primary reason behind this is not algorithms’ performance. No doubt, it provides us with more precise and accurate results than other ML models. But the reason is its nature; Its model having black-box functioning means the system is not answerable for its decisions. Due to the opaque nature of its algorithms, users feel a hindrance to adopting them.
Why is Deep Learning Important?
In today’s generation, the usage of smartphones and chips has increased drastically. Therefore, more and more images, text, videos, and audio are created day by day. But, as we know that a single-layer neural network can compute complex functions. On the contrary, for the computation of complex features, Deep Learning is needed. It is because deep nets within the deep learning method can develop a complex hierarchy of concepts. Another point is that when unsupervised data is collected and machine learning is executed, manually labeling the human being must perform data. This process is time-consuming and expensive. Therefore, to overcome this problem, deep learning is introduced as it can identify particular data.
Introduction to Deep Learning Neural Network
Various methods are introduced to analyze log files, such as pattern recognition methods like K-N Algorithm, Support Vector Machine, Naive Bayes Algorithm, etc. Due to the many log data, these traditional methods are not feasible to produce efficient results. Log Analysis using Deep Learning and AI shows excellent performance in analyzing the log data. It consists of good computational power and automatically extracts the features required for the solution of the problem. Deep learning is a subpart of Artificial Intelligence. It is a deep-layer learning process of the sensor areas in the brain.
What are the best Deep Learning Techniques?
Different techniques of Deep Learning are described below -
Convolutional Neural Networks
It is a type of network that constitutes learning weight and biases. Every input layer is composed of a set of neurons where at every input, a dot product is performed and moves further with the concept of non-linearity. It is a fully connected type of network that uses the SVM/Softmax function as a loss function.
Restricted Boltzmann Machine
it is a stochastic neural network consisting of one layer of visible units, one layer of hidden units, and a bias unit. The architecture is developed so that each visible unit is connected to all hidden units, and bias units are attached to all visible and hidden units. During the learning process, the restriction is developed so that no visible unit is connected with any visible units, and no hidden unit is connected with any hidden unit.
Recursive Neural Network
It is the type of deep learning neural network that uses the same weights recursively for performing structure prediction about the problem. The stochastic gradient is beneficial for training the network using the backpropagation algorithm.
5 Amazing Applications of Deep Learning
Biological Analogs
In the Artificial Neural Network case, the lowest layer can extract only the data set's essential features. Therefore, the convolutional layer is used with the combination of pooling layers. It is performed to increase the robustness of feature extraction. The highest convolutional layer is developed from the features of previous layers. These top layers are responsible for the detection of highly sophisticated features.
Image Classification
To recognize the human face, first, the edges are detected by the Deep Learning Algorithm to form the first hidden layer. Then, by combining the sides, the next shapes are generated as a second hidden layer. After that, shapes are combined to create the required human face. In this way, other objects can also be recognized.
Natural Language Processing
Reviews of movies or videos are gathered together to train them using Deep Learning Neural Networks to evaluate films' reviews.
Automatic Text Generation
In this case, a large Recurrent Neural Network is used to train the text to determine relationships between the sequence of strings. After learning the model, the text is generated word by speech/character by character.
Drug Discovery and Data Leakage
Deep Learning Neural Network is trained on gene expression levels, and activation scores are used to predict therapeutic use categories.
Data Used for Deep Learning
Deep Learning can be applied to any data such as sound, video, text, time series, and images. The features required within the data are:
- The data should be relevant according to the problem statement.
- To perform the proper classification, the dataset should be labeled. In other words, labels have to be applied to the raw data set manually.
- Deep Learning accepts vectors as input. Therefore, the input data set should be in the form of vectors and the same length. This process is known as Data Processing.
- Data should be stored in one storage place, such as a file system, HDFS (Hadoop Distributed File System). If the data is stored in different locations that are not interrelated, then Data Pipeline is needed. The development and processing of the Data Pipeline is a time-consuming task.
Deep Learning Application Areas
Deep learning neural network plays a major role in knowledge discovery, knowledge application, and last but least knowledge-based prediction. The benefits of deep learning are below -
- Power image recognition and tagging
- Fraud Detection
- Customer recommendations
- Used for analyzing satellite images
- Financial marketing
- Stock market prediction and much more
What are the Challenges of Deep Learning?
Especially in credit risk assessment, where it is required for a financial institution to give the reason when they decline a credit or loan application according to the “Equal Credit Opportunity Act” and “Fair Credit Reporting Act.” But if they use the model here, due to the model’s opaque nature, financial institutions will not give the reason behind the decision as the model doesn’t explain. Also, not able to detect bias and fairness.
In neural networks, some techniques can explain the model using feature importance; these are LIME (Local Interpretable Model-Agnostic Explanations), Deep Lift, Integrated Gradient, etc.
Though these techniques provide explanations, the finance industry is still not using them in credit assessment because it is still difficult to answer some questions. The following two questions are shared that resist financial organizations from using the Deep learning model in some of their systems. These are:
- For Trust: Do these methods provide an accurate or interpretable explanation?
- Reliable: How consistent is a system to produce trustworthy explanations?
What are the Solutions for Deep Learning?
For these questions and to increase user’s trust and satisfaction, it will discuss the approaches that can answer the questions discussed. It will check the trustworthiness and reliability of these approaches to let lenders adopt Deep Learning.
So users can apply these approaches for checking the trust and reliability of neural networks.
Trustworthiness of Deep Learning
It will check the trustworthiness of the approaches used to provide interpretability. An ML model will be used to predict the credit risk. First, it will calculate the Global feature importance using its weights. And then find the local feature importance using LIME, integrated gradient, and deep lift, etc.
In the above case, Deep Lift and Integrated Gradients give similar feature importance as Global Explanation is giving. Deep lift and Integrated Gradients have four similar features. Yet LIME has only one similar feature. This result is extracted only for one local observation. Therefore, Integrated Gradients and Deep Lift have more chances to accept than LIME.
Reliability in Deep Learning
Reliability allows checking the consistency of methods to provide a trustworthy result. To check the reliability, it will check the baseline. In our use case, there is no baseline; therefore, we will take a reference point for justification. How reliable an approach is to select a random baseline. The more an approach can respond to the random observations, the more reliable it is.
Conclusion
Interpretability allows us to make a fair and trustable system. Lime, Deep Lift, and integrated gradient are three approaches we discussed to explain the neural network’s decisions. We have discussed the methods to compare and check the trustworthiness and reliability of the approaches. The best way to select an approach is to first work on properties that must always be in the system. SHAP (SHapley Additive exPlanations), one of the Explainable AI libraries, has the multi-framework implementation of deep-lift and Integrated Gradients.
- Read more about the Ethics of Artificial Intelligence
- Explore the Challenges and Solutions in AI Adoption
