Difference Between Neural Networks vs Deep Learning
With the huge transition in today’s technology, it takes more than just Big Data and Hadoop to transform businesses. The firms of today are moving towards AI and incorporating machine learning as their new technique. Neural networks or connectionist systems are the systems which are inspired by our biological neural network. These kinds of systems are trained to learn and adapt themselves according to the need. For example, in case of image recognition, once they are identified with cats, they can easily use that result set to separate images with cats with the ones with no cats. While doing this they do not have any prior knowledge about the characteristics of cat but they develop their own set of unique features which is helpful in their identification. Another term which is closely linked with this is deep learning also known as hierarchical learning. This is based upon learning data representations which are opposite to task-based algorithms. It can further be categorized into supervised, semi-supervised and unsupervised learning techniques. There are several architectures associated with Deep learning such as deep neural networks, belief networks and recurrent networks whose application lies with natural language processing, computer vision, speech recognition, social network filtering, audio recognition, bioinformatics, machine translation, drug design and the list goes on and on. Let us discuss Neural Networks and Deep Learning in detail in our post.
Head to Head Comparison Between Neural Networks and Deep learning (Infographics)
Below is the top 3 Comparison Between Neural Networks and Deep Learning:
Key Differences Between Neural Networks and Deep learning
The differences between Neural Networks and Deep learning are explained in the points presented below:
- Neural networks make use of neurons that are used to transmit data in the form of input values and output values. They are used to transfer data by using networks or connections. Deep learning, on the other hand, is related to transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain.
- Application areas for neural networking include system identification, natural resource management, process control, vehicle control, quantum chemistry, decision making, game playing, face identification, pattern recognition, signal classification, sequence recognition, object recognition, finance, medical diagnosis, visualization, data mining, machine translation, email spam filtering, social network filtering, etc. whereas application of deep learning includes Automatic speech recognition, image recognition, visual art processing, Natural language processing, drug discovery and toxicology, customer relationship management, recommendation engines, Mobile advertising, bioinformatics, Image restoration etc.
- Criticism encountered for Neural networks includes those like training issues, theoretical issues, hardware issues, practical counterexamples to criticisms, hybrid approaches whereas for deep learning it is related with theory, errors, cyber threat, etc.
Neural Networks and Deep Learning Comparison Table
Below is some key comparison between Neural Network and Deep Learning.
| Basis for comparison | Neural Networks | Deep Learning |
| Definition | Class of machine learning algorithms where the artificial neuron forms the basic computational unit and networks are used to describe the interconnectivity among each other | It is a class of machine learning algorithms which uses non-linear processing units’ multiple layers for feature transformation and extraction. It also represents concepts in multiple hierarchical fashions which corresponds to various levels of abstraction. |
| Components | Neurons: Neuron which is labeled as j receives input from predecessor neurons often in the form of identity function to provide an output. Connections and weights: The connection is a vital component between the output neuron i and the input neuron j. Each connection is then identified by a weight ij. Propagation function: It is used to provide an input for the resulting output. Learning rule: It is used to modify the parameters of neural network so as to result in a favorable output. |
Motherboard: The motherboard chipset is a component related to deep learning which is particularly based upon PCI-e lanes. Processors: The kind of GPU required for Deep learning should be based upon the socket type, number of cores and cost of the processor. RAM, physical memory, and storage: The deep learning algorithms require great CPU usage, storage, and memory area and so having a rich set of these components is a must. PSU: With the increase in memory, CPU and storage area it also becomes important to use a large PSU enough to handle huge power. |
| Architecture | Feed Forward Neural Networks: The commonest kind of architecture contains the first layer as the input layer while the last layer is the output layer and all the intermediary layers are the hidden layers. Recurrent networks: This kind of architecture consists of directed cycles in the connection graph. The biologically realistic architectures can also take you back from where you started. These are complicated to train and are extremely dynamic. Symmetrically connected networks: Symmetrical connection holding architecture which is more or less like the recurrent networks. They are restricted in nature due to their use of energy function. Symmetrically connected nets with hidden networks are known as Boltzmann machines whereas the ones without the hidden network are known as Hopfield nets. |
Unsupervised Pretrained Networks: In this architecture, we talk about no formal training but the networks are pretrained using past experiences. This includes autoencoders, deep belief networks, and generative adversarial networks. Convolutional Neural networks: It aims to learn higher order features using convolutions which betters the image recognition and identification user experience. Identification of faces, street signs, platypuses and other objects become easy using this architecture. Recurrent neural networks: They come from the family of feedforward which beliefs in sending their information over time steps. Recursive neural networks: It also marks variable length input. The primary difference between recurrent and recursive is that the former has the ability to a device the hierarchical structures in the training dataset while the latter also poses the information about how that hierarchical structure is maintained in the dataset. |
Conclusion
AI is an extremely powerful and interesting field which only will become more ubiquitous and important moving forward and will surely have huge impacts on the society as a whole. These two techniques are some of AI’s very powerful tools to solve complex problems and will continue to develop and grow in future for us to leverage them.
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