Machine learning vs. deep learning: here’s what you need to know!

Artificial intelligence (AI) and machine learning (ML) are two words that are used casually in everyday conversations, whether in offices, institutes or technological 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 capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” In a nutshell, it means making machines smarter to replicate human tasks, and Machine Learning is the technique (using available data) to make this possible.

Researchers have been experimenting with frameworks for building algorithms, which teach machines to handle data like humans do. These algorithms lead to the formation of artificial neural networks that sample data to predict nearly accurate results. To help build these artificial neural networks, some companies have released open neural network libraries like Google’s Tensorflow (released 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 ​​like Python, C/C++, and Java.

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

In our previous blog posts, we have discussed at length about artificial intelligence, machine learning, and deep learning, 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.

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What factors differentiate machine learning from deep learning?

Machine learning analyzes the data and tries to predict the desired result. The neural networks formed are usually shallow and made up of one input, one output, and just one hidden layer. Machine learning can be broadly classified into two types: supervised and unsupervised. The former involves tagged data sets with specific inputs and outputs, while the latter uses data sets without a specific structure.

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

In our previous blog post, we learned about the four Deep Learning architectures. Let’s quickly summarize them:
Unsupervised Pretrained Networks (UPN)

Unlike traditional machine learning algorithms, deep learning networks can perform automatic feature extraction without the need for human intervention. So unsupervised means without telling the network what is right or wrong, which it will figure out on its own. And pre-trained means using a set of data to train the neural network. For example, train pairs of layers as Constrained Boltzmann Machines. You will then use the trained weights for supervised training. However, this method is not efficient in handling complex image processing tasks, which brings convolutions or convolutional neural networks (CNN) to the forefront.
Convolutional Neural Networks (CNN)

Convolutional neural networks use replicas of the same neuron, which means 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 you to hardcode some properties into the architecture. It also reduces the number of parameters in the network.
Recurrent Neural Networks

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

A Recursive Neural Network is a generalization of a Recursive Neural Network and is generated by applying a fixed and consistent set of weights iteratively or recursively over the structure. Recursive Neural Networks takes the form of a tree, while Recurrent is a string. Recursive neural networks have been used in natural language processing (NLP) for tasks such as sentiment analysis.

In a nutshell, deep learning is nothing more than an advanced method of machine learning. Deep learning networks deal with unlabeled data, which is trained. Each node in this deep layer learns the feature set automatically. It then aims to reconstruct the input and tries to do so by minimizing guesswork with each passing node. It doesn’t need any specific data, and in fact it’s so smart that it pulls correlations from the feature set for optimal results. They are capable of learning gigantic data sets with numerous parameters and forming structures from unlabeled or unstructured data.

Now, let’s take a look at the key differences:

Differences:
The future with Machine Learning and Deep Learning:

Moving further, let’s take a look at the use cases of Machine Learning and Deep Learning. However, it should be noted that machine learning use cases are available while deep learning is still in the development stage.

While machine learning plays a huge role in artificial intelligence, it is the possibilities introduced by deep learning that are changing the world as we know it. These technologies will see a future in many industries, some of which are:
Customer service

Machine learning is being implemented to understand and respond to customer queries 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 queries related to the product and subsequent services. Deep Learning goes a step further by measuring customer moods, interests and emotions (in real time) and making dynamic content available for more refined customer service.
Automotive industry
Machine Learning vs. Deep Learning: Here’s What You Need to Know!

Autonomous cars have been in the headlines from time to time. From Google to Uber, everyone is trying to do it. Machine learning and deep learning sit comfortably at their core, but what’s even more exciting is the autonomous customer support that makes CSRs more efficient with these new technologies. Digital CSRs learn and deliver information that is nearly accurate and in a shorter period of time.

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Speech recognition:

Machine learning plays a huge role in speech recognition by learning from users over time. And deep learning can go beyond the role that machine learning plays by introducing abilities to classify audio, recognize speakers, among other things.

Deep learning has all the benefits of machine learning and is considered to become the main driver of artificial intelligence. Start-ups, multinationals, researchers and government agencies have realized the potential of AI and have started to harness its potential to make our lives easier.

Artificial intelligence and big data are believed to be the 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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