What are the differences between Machine Learning and Deep Learning?

What are the differences between Machine Learning and Deep Learning?
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The technological marvel, Artificial Intelligence, has evolved significantly to give rise to two other ingenious technologies – Machine learning and Deep Learning. Both of these technologies have created a buzz in the software market and are setting new trends by executing unthinkable tasks. ML and DL are opening up new avenues for new-age entrepreneurs by making way for intelligent and intuitive software solutions. Entrepreneurs, these days, are roping in a Machine Learning Company for designing disruptive solutions for them.
Although Machine Learning and Deep Learning are subsets of the same technology – Artificial intelligence – they are quite different from each other. And, new-age businesses planning to leverage the technical benefits of these amazing technologies, must understand their differences well, so that they are able to implement these technologies correctly.
This post provides deep insights into Machine Learning and Deep Learning and explores their differences.

Machine Learning: An Overview

Machine Learning is a subset of Artificial Intelligence. It provides a system with the capacity to learn as well as improve from the experience gained, without the need for being programmed to that level. Data is employed for training and then finding the correct outcome. Machine Learning solutions perform a function using the data fed to it and progressively improve with time.
This technology is used for executing all types of automated tasks across several industrial domains right from data security companies for identifying malware to finance businesses who want to receive alerts for favorable trades.
Machine Learning is classified into 3 categories
Supervised Learning: This approach involves a wholly governed learning process, wherein the result is predicted based on a set of training samples provided with training labels also called the classifying data point. Here machine learning developers tell the algorithm what to predict during the training time, hence the name supervised learning.
Unsupervised Learning: This approach does not get training labels for the training samples. Here, the algorithms are created in such a manner that they are capable of finding suitable patterns and structures within the data provided. Similar data points are assembled together after the consistent patterns become apparent. Various data point appears in different clusters. It projects high-dimensional data into low-dimensional ones, for visualizing or analyzing.
Reinforced Learning: This approach involves a robot-like agent that performs actions and quantifies outcomes to learn how it should behave within a given environment. It follows the MDP (Markov Decision Process) – receives a reward point for making a correct response. This expedites the confidence level of the agent and encourages it to take up more such functions.
When ML is applied to an on-demand music streaming service, its task is to find out what new songs/artists to suggest to specific groups of listeners. For making decisions about such recommendations, an ML algorithm relates the user’s preferences with those of other users with similar musical tastes.

Deep Learning: An Overview

Deep learning, a subset of ML, is a technology where recurrent neural network and artificial neural network comes together. The formation of algorithms is quite similar to that of ML, only with the difference that there are more algorithms levels involved. All of these networks combine to form a layered structure of algorithms termed the artificial neural network – it’s just like the biological network of neurons present inside a human brain. Deep learning solutions continuously analyze data with a logical structure, just like the processing that happens inside a human brain to draw conclusions.
Deep Learning applications can solve complicated problems by processing the algorithms and is way more capable than the standard ML models.
Multiple layers that are stacked between the input and output layer
  • Input layer consisting of a time series data or pixels of an image
  • Hidden Layer called weights; it’s learned while the neural network is being trained
  • The output layer is the final layer that provides a predictive analysis based on the input that has been fed into the network.
The Google-developed gaming app named AlphaGo is a perfect example of Deep Learning implementation. A computer program has been created using a neural network for playing this abstract board game against professional players. And, AlphaGo has successfully defeated world-famous players of the Go game – an instance of artificial intelligence defeating human intelligence.
Deep learning is also used for functions like translation, speech recognition, and operating self-driving cars.

Key Differences between Machine Learning and Deep Learning

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Let’s now explore the key differences between Machine Learning and Deep Learning based on the following parameters.
Basic Functioning Principle
Machine learning is a super-set of Deep learning that takes in data as an input, then parses the data and makes decisions based on the learning while being trained. Deep learning, on the other hand, is a subset of ML, here data is accepted as an input for making intelligent and intuitive decisions using a layer-wise stacked artificial neural network.
Machine learning solutions are apt for solving problems that are simple or partly complex; whereas Deep Learning models are suitable for solving more complex problems.
The Type of Data involved and the Problem Solving Technique
Machine learning solutions usually deal with structured data and hence, employ traditional algorithms such as linear regression. Deep learning models can work with structured as well as unstructured data as they depend on the layers of an artificial neural network. Machine Learning algorithms parse data in parts and after processing these parts separately, integrate them to produce the final outcome. Contrarily, Deep learning systems follow an end-to-end approach – take in the input for a problem and produces the end-result directly.
For example, a program has to identify specific objects – license plates of cars parked in a lot – within an image; find out the objects’ identity and location. With an ML solution, this task will be executed in two steps – detecting the object and then recognizing it. Using a Deep Learning application, the task will be completed at one go – you input the image and the identified objects along with their location appear in a single result.
Data Dependencies and Output
Machine Learning handles thousands of data points and its outputs include numerical values or classifications. Deep learning, on the other hand, handles millions of data and its outputs range from numerical values to free-form elements like text and speech.
ML depends on a large amount of data, yet can function smoothly with a smaller amount of data as well. But this is not the case with deep learning models – they perform well only if humongous data is fed to them.
Algorithm Usage
ML employs different kinds of automated algorithms for parsing data and turns them into model functions for predicting future actions or making informed decisions based on the learning acquired from collected and processed data. Data analysts detect these algorithms for examining particular variables within sets of data.
Deep Learning structures the algorithms in layers to build an artificial neural network. With this approach, data passes through several processing layers for interpreting data features and relations. This neural network is capable of learning and then forming intelligent decisions on its own.
Hardware Requirement
ML programs are less likely to be complex as compared to deep learning algorithms. Machine learning programs need a CPU to process and so, can function on conventional computers or low-end machines without the need for high computing power. Deep learning algorithms, on the other hand, require way more powerful hardware as well as resources; because of the complex nature of the mathematical calculations involved and the need for processing a huge amount of data. They use hardware like GPUs or graphical processing units, and this increases the demand for power. GPUs possess high bandwidth memory and hide latency while transferring memory on account of thread parallelism.
Feature Extraction Methodology
The Deep learning mechanism is an ideal way of extracting meaningful functions out of raw data and is not dependant on hand-crafted features such as a histogram of gradients, binary patterns, etc. Moreover, the feature extraction methodology is hierarchical – features are learned layer-wise. As a result, it learns low-level features from the initial layers and as it goes up the hierarchy, more abstract data representation is learned.
However, ML is not a suitable option when there is a need to extract meaningful features from data. This is because, for good performance, it is highly dependent on hand-crafted features provided as input.
The degree of human intervention needed
ML needs continuous human intervention for obtaining the best results. Deep learning does involve a more complex set-up procedure, but once set up requires very less human intervention.
Execution Time involved
Machine Learning algorithms consume much lesser time for training the model, but testing the model is time-consuming. On the contrary, Deep learning applications take much lesser time to test the model but take a bit longer to train the model.
Industry Readiness
It’s easy to decode ML algorithms and it can interpret which parameters were picked and why those parameters were chosen. Deep learning algorithms, on the contrary, are simply a blackbox and are capable of outshining humans in regards to performance. Thus, ML solutions are better bait for industry application as compared to Deep learning solutions.

Final Verdict

Machine Learning and Deep Learning are here to stay. Both of these technologies possess a huge potential in transforming every industry vertical. Dangerous tasks such as working within harsh eco-systems, activities concerning space travel, etc. are expected to be replaced by ML and DL models in the near future. So it’s high time to be well versed with these outstanding technologies.
However, developing and implementing ML and DL solutions is no cakewalk and so, it’s advisable to hire experienced professionals for this purpose. For technical assistance in designing, deploying, and maintaining, ML/DL models, Biz4Solutions, a highly experienced and competent outsourcing software company in India, would be a good choice. We have extensive experience and expertise in dealing with ML and DL systems for global clients.

How Machine Learning Solutions are transforming the World of Financial Services?

How Machine Learning Solutions are transforming the World of Financial Services?
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The Fintech sector has progressed beyond imagination. Just a few years ago, it took several weeks to get loans approved, but today, everything is processed online and it takes barely a day. Likewise, financial frauds used to occur very often and the financial safety of the user was a big concern worldwide. However in recent times, such fraudulent transactions have reduced considerably, though, online transactions have increased immensely.
How did this happen? What changed?
The mobile revolution and the emergence of trending technologies like machine learning and artificial intelligence have brought a paradigm shift in the fintech industry. Machine learning technology has undoubtedly transformed financial services in a big way.
The algorithm used by Machine Learning solutions is used to identify correlations and work patterns within the large amount of data used in sequences, operations, and events. Hence it is used successfully in process automation, customer support optimization, financial monitoring, portfolio management, algorithmic trading, etc., and much more!
So, in this blog, we will outline how machine learning services has impacted the fintech sector! Rather, we will have a glimpse at the machine learning applications in this sector. Let’s commence.

Significant Role of Machine Learning Solutions in the Fintech Sector!

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Risk Management
In the Fintech industry, risk management refers to the process of identifying, analyzing, and migrating or accepting uncertainties i.e. potential risks while making any investment. It also includes taking precautionary steps to reduce such financial risk. Machine learning solutions along with AI and deep learning can help in making informed decisions about financial reports or loan applications. The predictive analysis used in Machine learning can predict potential risks from this unstructured data, and curb financial fraud to some extent.
Detecting and Preventing Banking Frauds
Today, banks and other financial organizations are paying heavily to prevent fraudulent practices. A plethora of large financial institutes are still using outdated security measures and have no systems to deal with new kinds of threats. However, machine learning algorithms are proving beneficial in this scenario. These can be used to find patterns involved in thefts and identify the hack-prone areas or loopholes in the banking systems. Besides, machine learning models are used in image processing, data mining, language processing, etc. Machine learning provides a predictive model depending on historical data for making vital business decisions using result-oriented predictions.
Complying with Ever-changing Rules and Regulations
Adherence to regulatory compliance like HIPAA is vital for financial firms and they spend billions to comply with them. Yet, new rules keep popping up, and following all these rules every time becomes almost impossible, despite spending a lot of time, money, and effort. But, AI and ML platforms can help automate this process as they can read through the pile of regulatory documents and guidelines. They can track and monitor any regulatory updates faster while minimizing risks related to non-compliance.
Robo-advisors are digital advisors or platforms that provide algorithm-driven, automated financial planning services with very little or no human intervention. The Robo-advisor collects data like age, financial status, current income, future goals, etc. from clients and uses this data to provide vital advice to them about the kind of investment they need to do based on their future goals. These Robo-advisors do a predictive analysis based on current market trends and the clients’ goals and find the best-suited investment plan for them. Likewise, they offer easy account setup, account services, robust goal planning, portfolio management, comprehensive education, attentive customer service, and security features, etc. Some experts also predict that these digital advisors could completely wipe out the human advisors in near future and save huge costs for Fintech app development services.
Chatbots are being used by several financial institutes and they work as full-scale robotic assistants. These chatbots respond immediately to the customers, hence the customers feel satisfied. Most of the time, the customers are not even aware whether they are interacting with a non-human assistant.
Sentiment Analysis
Sentiment analysis is yet another application of machine learning in financial services; also called news analysis. Machine learning applications are majorly dependent on data. But these applications cannot depend just on the data from stock and trade prices. So in recent times, these are evolving to gather additional data from social media trends, news trends, etc. to do sentiment analysis. The algorithms can do predictive analysis using the available data, also taking into consideration the thoughts and opinions of the users, etc.
Safety and Security of User Data
Needless to say, users’ data is the biggest concern for every financial firm. If this sensitive data leaks or gets hacked by any chance, it can not only lead to financial losses but also result in defamation in the market.
Traditionally, the users are being provided with a username and password for secure access. In case of account recovery or lost password, mobile number validation is required or some security questions are asked. But in the future, with the use of machine learning and artificial intelligence, anomaly detection apps can be developed that make use of biometric data like voice recognition, facial recognition, or retina scan. Several machine learning development companies are shifting their focus towards this area. Such measures will surely improve the data security of the users to a much better level.
Influencing Stock Markets
Stock markets are impacting the Fintech sector too. Every day, huge trading operations are being carried out and they gather tons of historical data. Machine learning algorithms take advantage of this data and identify patterns of stock market dynamics to make predictions. The traders make use of such vital data and accordingly decide the strategies. Based on these patterns, they can make trading forecasts and get benefitted in the stock market world.

Final Words:

Machine learning development services have influenced the world of financial services in myriad ways. It is playing a significant role in improving several processes like loan approval, risk management, portfolio management, financial analysis, etc. It prevents fraudulent practices and helps in stock forecasts too. Besides these benefits, machine learning developers and experts all over the world are experimenting to invent several other applications that can empower the fintech industry to serve their customers better, outsmart the competition, enhance productivity, and save costs effectively.
So this read ends here! I hope it was insightful.
Have you leveraged Machine learning services in your business endeavours? We would like to hear about your experiences.
In case of any technical assistance concerning the development of machine learning solutions reach out to Biz4Solutions, a highly experienced outsourcing software company in India. We are a leading Machine learning Company in India and USA and excel in delivering high-end services to the global clientele.