Machine Learning vs. Deep Learning: Here’s What You Need to Know!
Artificial intelligence (AI) and machine learning (ML) are two words that are thrown around casually in everyday conversations, be it in offices, schools, or tech gatherings. 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.” Simply put, 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 near-accurate results. To help build these artificial neural networks, some companies have released open neural network libraries like Google’s Tensorflow (launched in November 2015), among others, to create models that process and predict specific application 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 derive from deep networks known as deep neural networks.
In our previous blog posts, we have extensively discussed artificial intelligence, machine learning, and deep learning, and how these terms cannot be interchanged, 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 attempts to predict the desired outcome. Trained neural networks are usually shallow and made up of one input, one output, and barely a hidden layer. Machine learning can be broadly classified into two types: supervised and unsupervised. The former involves data sets tagged 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 gigantic, and the simulations are too complex. This requires a deeper understanding or learning, made possible by the use of complex layers. Deep learning networks are for much more complex problems and include multiple layers of nodes indicating their depth.
In our previous blog post, we learned about the four architectures of Deep Learning. Let’s quickly 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 not telling the network what’s right or wrong, which it will figure out on its own. And pre-trained means using a data set to train the neural network. For example, train layer pairs as Constrained Boltzmann Machines. You will then use the trained weights for supervised training. However, this method is not efficient for handling complex image processing tasks, which brings convolutions or Convolutional Neural Networks (CNNs) to the forefront.
Convolutional Neural Networks (CNN)
Convolutional neural networks use replicas of the same neuron, which means 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 you to hardcode some properties 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 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 Recurrent Neural Network and is generated by applying a fixed and consistent set of weights repetitively or recursively on 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 rebuild the input and tries to do so minimizing the guesswork with each passing node. It doesn’t need any specific data, and in fact, it’s so smart that it extracts mappings from the feature set to get 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 answer 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 mood, 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 its core, but what’s even more exciting is 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 very important 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 begun 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.