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DEEP LEARNING IN MACHINE LEARNING

Deep Learning in Machine Learning

Deep Learning is a sub-division of machine learning consists of algorithms stimulated by the structure and function of the brain called artificial neural networks.

If the user is just starting out in the field of deep learning or the user had some experience with neural networks, then the user might get confused.

The experts in the field have idea of what deep learning is and these exact and refined perspectives shed a lot of light on what deep learning is all about.

In this article, the user will discover exactly what deep learning is by hearing from a range of experts in the field.

DEEP LEARNING

Deep Learning has evolved hand-in-hand with the digital eon, which has brought about an eruption of data in all forms and from every region of the world. This data, known simply as Big Data, is drawn from sources such as social media, internet search engines, e-commerce platforms, online cinemas, and much more. This massive amount of data is readily accessible and can be shared through FinTech applications such as cloud computing. However, the data, which usually is unstructured, is so huge that it could take decades for humans to understand and extract relevant information. Organizations realize the incredible potential that can result from unravelling this wealth of information, and are increasingly adapting Artificial Intelligence (AI) systems for automated support.

One of the most common AI techniques used for processing Big Data is Machine Learning, a self-adaptive algorithm that gets progressively better analysis and patterns with experience or with new added data. The computational algorithm built into a computer model will process all the transactions happening on the digital platform, find patterns in the data set and identifies glitches detected by the pattern.

Deep learning, a subdivision of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems allows machines to process data with a nonlinear approach. A traditional approach to identifying fraud might depend on the amount of transaction arises, while a deep learning nonlinear technique would include time, geographic location, IP address, and other features that is likely to point to a fraudulent activity. The first layer of the neural network processes a raw data inputs the amount of transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user’s IP address and passes on its result. The next layer takes the second layer’s information and includes raw data like geographic location and makes the machine’s pattern even better. This continues across all levels of the neuron network.

DEEP LEARNING

Using the fraud detection system with machine learning, the user can create a deep learning example. If the machine learning system creates a model with parameters built around the amount of dollars a user sends or receives, the deep learning method can start building on the results offered by machine learning. Each layer of its neural network builds on its previous layer with added data such as retailer, sender, user, credit score, IP address and a host of other features that may take years to connect together if processed by a human being. Deep learning algorithms are skilled to not just create patterns from all transactions, but to also know when a pattern is signalling the need for a fraudulent investigation. The final layer transmits a signal to an analyst who may freeze the user’s account until all pending investigations are confirmed.

Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open source platforms with consumer recommendation apps that explore the possibility of reusing for new ailments are a few of the examples of deep learning incorporation.

WHAT IS THE DIFFERENCE BETWEEN FLUX AND REDUX ?

What is the difference between Flux and Redux?

Redux does not have a dispatcher. It relies on pure functions called reducers. It does not need a dispatcher. Each action is handled by one or more reducers to update the single store. Since data is immutable, reducers return a new updated state that updates the store Flux makes it unnatural to reuse functionality across stores in Flux, stores are flat, but in Redux, reducers can be nested via functional composition, just like React components can be nested. Redux store your state at only one place. While you can have many in Flux

WHAT IS LOGISTIC REGRESSION?

What is logistic regression?

Logistic Regression is also known as the logit model. It is a technique to forecast the binary outcome from a linear combination of predictor variables.

 
 
 
 
 
 
 
 
 
 
 
 
 

WHAT IS THE DIFFERENCE BETWEEN SUPERVISED LEARNING AN UNSUPERVISED LEARNING?

What is the difference between Supervised Learning an Unsupervised Learning?

If an algorithm learns something from the training data so that the knowledge can be applied to the test data, then it is referred to as Supervised Learning. Classification is an example for Supervised Learning. If the algorithm does not learn anything beforehand because there is no response variable or any training data, then it is referred to as unsupervised learning. Clustering is an example for unsupervised learning.

 
 
 
 
 
 
 
 
 
 
 
 

WHY DATA CLEANING PLAYS A VITAL ROLE IN ANALYSIS?

Why data cleaning plays a vital role in analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.
 
 
 
 
 
 
 
 
 
 
 

WHY IS CHAR[] PREFERRED OVER STRING FOR PASSWORDS?

Why is char[] preferred over String for passwords?

Strings are immutable. That means once you have created the String, if another process can dump memory, there is no way (aside from reflection) you can get rid of the data before garbage collection kicks in.

With an array, you can explicitly wipe the data after you are done with it. You can overwrite the array with anything you like, and the password won’t be present anywhere in the system, even before garbage collection.

So yes, this is a security concern – but even using char[ ] only reduces the window of opportunity for an attacker, and it’s only for this specific type of attack.

As noted in comments, it’s possible that arrays being moved by the garbage collector will leave stray copies of the data in memory. I believe this is implementation-specific – the garbage collector may clear all memory as it goes, to avoid this sort of thing. Even if it does, there is still time during which the char[ ] contains the actual characters as an attack window.

HOW TO SPECIFY A SUDO PASSWORD FOR ANSIBLE IN NON-INTERACTIVE WAY?

How to specify a sudo password for Ansible in non-interactive way?

We can pass variable on the command line via–extra-vars “name=value”.
Sudo password variable is ansible_sudo_pass.
So your command would look like:
ansible-playbook playbook.yml -i inventory.ini –user=username \
–extra-vars “ansible_sudo_pass=yourPassword”
 
 
 
 
 
 
 
 
 
 
 

WHAT IS THE DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED MACHINE LEARNING?

What is the difference between supervised and unsupervised machine learning?

Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data and need to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly.