Deep Learning

“Predicting the future isn’t magic, it’s artificial intelligence.” ~Dave Waters

Well said, but is predicting future that easy? What’s Deep Learning and how is it connected to this. Whenever Deep Learning is discussed we come across statements like Computational systems that operate internally as humans do. A algorithm working same as our mind works.

We have heard a lot more of such kinds of definitions when it comes to Artificial Intelligence, Deep Learning or Machine Learning. But ever wondered what does this actually mean, what does this field seriously do or how can a program be so efficient that it works same as our human brain.

If we actually dive into this discussion and properly see what is this field about, we will need to first see what Artificial Intelligence is, what Machine Learning is so that we get a clear idea about what is Deep Learning and how is it different from all these.

Artificial Intelligence (AI)

“We can build a much brighter future where humans are relieved of menial work using AI capabilities.” ~Andrew Ng

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

AI is the power to Make Artificial things capable of doing stuff equal to human beings, making them think wisely, take decision or predict some outcome in a random scenario and get to a solution effectively is what AI does.

Machine Learning (ML)

Field of study that gives computers the ability to learn without being explicitly programmed. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn.

It is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like.

Deep Learning (DL)

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain. Basically in the field the algorithm working to predict outcomes is replicated same as the working of an Human Brain with all the neurons and data processing and transferring in brain cells. Here a long webbed structure or network is formed from single neurons at each step and output in predicted by passing the given input from this network.

Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning from data that is unstructured. Also known as deep neural learning or deep neural network.

Deep Learning is Hierarchical Feature Learning

One of the benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning.

Now what do we mean by hierarchical feature learning, in this type of feature learning your designed algorithm or designed network learns the given input(For eg.- Image) in a series of Hierarchy. In the case of an Image input, when it is passed through an network or a deep learning model, it first learns the edges of that image then layer by layer dives into the image by learning other important features like the texture, saturation, color, Hue, etc. Reaching the last part of the network the algorithm or the Neural Network learns the complete image and performs the expected functions from it.

See this for a better understanding.

Deep Learning vs. Machine Learning

When it comes to comparing two of the most strong, life changing fields its pretty difficult to do so. There is no one out of these two better, rather both have its own importance and both are important in different scenarios.

One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data. If a digital payments company wanted to detect the occurrence or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

Deep learning, a subset of machine learning, utilizes a hierarchical level of neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, 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 enables machines to process data with a nonlinear approach.

DL requires a lot of unlabeled training data to make concise conclusions while ML can use small data amounts provided by users.

ML requires features to be accurately identified by users while DL creates new features by itself.

ML divides tasks into small pieces and then combine received results into one conclusion while DL solves the problem on the end-to-end basis and unlike DL, ML can provide enough transparency for its decisions.

Benefits of Deep Learning

1. Creating New Features

One of the main benefits of deep learning over various machine learning algorithms is its ability to generate new features from limited series of features located in the training dataset. Therefore, deep learning algorithms can create new tasks to solve current ones.

2. The deep learning architecture is flexible to be adapted to new problems in the future.

3. The same neural network based approach can be applied to many different applications and data types.

Disadvantages of Deep Learning

1. It requires very large amount of data in order to perform better than other techniques.
2. There is no standard theory to guide you in selecting right deep learning tools as it requires knowledge of topology, training method and other parameters. As a result it is difficult to be adopted by less skilled people.
3. It is extremely expensive to train due to complex data models. Moreover deep learning requires expensive GPUs and hundreds of machines. This increases cost to the users.

Conclusion

So this is what actually AI, ML and DL means, these are one of the most useful fields in todays life and the most emerging fields for the coming years. Advancement in these field decide and design our future, we are completely going to live on their basis and they will surely play a vital role in shaping and ruling our future generation. Honestly Deep Learning is a very interesting topic and as I mentioned about a human brain like algorithm working called Neural Networks for implementing deep learning, so in my coming blogs we’ll dive deep into this for a better understanding that will give you guys a great exposure of this.