Within A Neuron Information Typically Flows In Which Direction Order Machine Learning Vs Deep Learning: Here’s What You Must Know!

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Machine Learning Vs Deep Learning: Here’s What You Must Know!

Artificial intelligence (AI) and machine learning (ML) are two words that are thrown around casually in everyday conversations, be it in offices, institutes or technology meetings. Artificial intelligence is said to be the future enabled by machine learning.

Now, Artificial Intelligence is defined as “the theory and development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision making, and language translation.” Simply put, it means making machines smarter to replicate human tasks, and machine learning is the technique (using available data) that makes this possible.

Researchers have been experimenting with frameworks for building algorithms, which teach machines to work with data just as humans do. These algorithms lead to the creation of artificial neural networks that sample data to predict near-accurate outcomes. To help build these artificial neural networks, some companies have released open neural network libraries such as Google’s Tensorflow (released in November 2015), among others, to build models that process and predict application-specific cases. Tensorflow, for example, runs on GPUs, CPUs, desktops, servers, and mobile computing platforms. Some other frameworks are Caffe, Deeplearning4j and Distributed Deep Learning. These frameworks support languages ​​such as Python, C/C++, and Java.

It should be noted that artificial neural networks function like a real brain which is connected by neurons. So each neuron processes data, which is then passed on to the next neuron and so on, and the network continues to change and adapt accordingly. Now, to work with more complex data, machine learning must be derived from deep networks known as deep neural networks.

In our previous blog posts, we have discussed artificial intelligence, machine learning and deep learning at length and how these terms are not interchangeable, even though they sound similar. In this blog post, we will discuss how machine learning differs from deep learning.

LEARN MACHINE LEARNING

What factors differentiate machine learning from deep learning?

Machine learning crunches the data and tries to predict the desired outcome. The neural networks formed are usually shallow and consist of one input, one output, and a barely hidden layer. Machine learning can be broadly classified into two types – supervised and unsupervised. The former includes labeled data sets with specific input and output, while the latter uses data sets without a specific structure.

On the other hand, now imagine that the data that needs to be processed is really huge and the simulations are too complex. This requires a deeper understanding or learning, which is possible through complex layers. Deep learning networks are used for much more complex problems and include a number of layers of nodes that indicate their depth.

In our previous blog post, we learned about the four deep learning architectures. Let’s briefly summarize them:

Unsupervised Pretrained Networks (UPNs)

Unlike traditional machine learning algorithms, deep learning networks can perform automatic feature extraction without the need for human intervention. So unsupervised means no telling the network what is right or wrong, which it will figure out on its own. And pre-trained means using a dataset to train a neural network. For example, training pairs of layers as Restricted Boltzmann Machines. They will then use trained weights for supervised training. However, this method is not effective for solving complex image processing tasks, which brings Convolutions or Convolutional Neural Networks (CNN) to the fore.

Convolutional Neural Networks (CNN)

Convolutional neural networks use replicas of the same neuron, meaning that neurons can be learned and used in multiple places. This simplifies the process, especially during object or image recognition. Convolutional neural network architectures assume that the inputs are images. This allows several properties to be encoded into the architecture. It also reduces the number of parameters in the network.

Recurrent neural networks

Recurrent neural networks (RNNs) use sequential information and do not assume that all inputs and outputs are independent as we see in traditional neural networks. So, unlike feed-forward neural networks, RNNs can use their internal memory to process string inputs. They rely on previous calculations and what has already been calculated. It is applicable for tasks such as speech recognition, handwriting recognition or any similar non-segmented task.

Recursive neural networks

A recursive neural network is a generalization of a recurrent neural network and is generated by applying a fixed and consistent set of weights in an iterative or recursive way to a structure. Recursive neural networks have the shape of a tree, while recurrent is a chain. Recursive neural networks have been used in natural language processing (NLP) for tasks such as sentiment analysis.

In short, deep learning is nothing but an advanced method of machine learning. Deep learning networks deal with unlabeled data that has been trained. Each node in these deep layers automatically learns a set of features. It then aims to reconstruct the input and tries to do so by minimizing guesswork with each passing node. It doesn’t need specific data and is actually so smart that it draws correlations from a set of features to get optimal results. They are able to learn huge datasets with many parameters and form structures from unlabeled or unstructured data.

Now let’s look at the key differences:

Differences:

The future with machine learning and deep learning:

Moving on, let’s look at machine learning and deep learning use cases. However, it should be noted that machine learning use cases are available while deep learning is still in its development phase.

Although machine learning plays a large role in artificial intelligence, the possibilities brought about by deep learning are changing the world as we know it. These technologies will see the future in many industries, some of them are:

Customer service

Machine learning is implemented to understand and respond to customer inquiries as accurately and quickly as possible. For example, it is very common to find a chatbot on product websites, which is trained to answer all customer inquiries regarding the product and additional services. Deep learning goes a step further by measuring user mood, interests and emotions (in real time) and making dynamic content available for more refined customer service.

Car industry

Machine Learning vs. Deep Learning: Here’s What You Need to Know!

Autonomous cars have been constantly in the headlines. From Google to Uber, everyone is trying their hand at it. Machine learning and deep learning sit comfortably at its core, but what’s even more interesting is the autonomous customer care that makes CSRs more efficient with these new technologies. Digital CSRs learn and offer information that is almost accurate and in a shorter time span.

LEARN DEEP LEARNING

Speech recognition:

Machine learning plays a big role in speech recognition by learning from users over time. And Deep Learning can go beyond the role played by machine learning by introducing the ability to classify audio, recognize speakers, among other things.

Deep learning has all the advantages of machine learning and is considered a major driver towards artificial intelligence. Startups, multinational companies, researchers and government bodies have realized the potential of artificial intelligence and started using its potential to make our lives easier.

Artificial intelligence and big data are believed to be trends to watch out for in the future. Today, there are many courses available online that offer comprehensive real-time training in these newer emerging technologies.

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