1- What’re neural networks?

In simple words, neural networks can be considered mathematical models loosely modeled on the human brain. Neural networks engage in two distinguished phases. First, comes the learning phase where a model is trained to perform certain tasks. These could be how to perform language translations or how to describe images to the blind. And second comes the application stage where the trained model is utilized. You can think of Spotify sending you a weekly-playlist created by analyzing your music taste. Neural networks come with some fundamental building blocks that include neurons, input, outputs, weights, and biases. Here, each neuron comes with one or multiple inputs together with a single output.

You can use this output as an input to one or multiple neurons or as the entire network’s output. The most intelligent thing about neural networks is the self-learning during the training period of the models. Here, a neural network is given a dataset of inputs (could be text, speech, or images – but everything has to be translated to numbers) and a true answer accompanying every observation set. Now the model learns to find out the true answer based on the inputs it has been presented with. Throughout the learning process, the model would estimate second-hand-values continuously and compare those to the true values. If there’s a large difference, the model parameters get automatically updated to push those estimates closer to true second-hand-values. This process gets repeated until the average difference between true and assigned values becomes adequately small.

2- What’s deep learning?

You can think of deep learning as the absolute cutting edge of AI (artificial intelligence). Here, the machine trains itself to process, as well as, learn from data. With deep learning, you don’t need to teach machines to process and learn from data, which is the working method of machine learning.

Parting Thoughts

The difference between deep learning and neural networks remains in the model’s depth where the former phrase is used to mention complex neural networks. A deep learning system is simply a self-teaching one that keeps on learning by filtering information via multiple hidden layers, much like the way the human brain works. It’s being assumed by some people that deep learning will automate a significant number of tasks and might replace many human workers in the future. But it’s also important to understand that implementation of deep learning might replace someone who works on repetitive, manual tasks but it just can’t replace the engineer or the scientist developing and maintaining a deep learning application.

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To learn more about deep learning, click here and read our another article.

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You can consider an artificial neural network as a computational model which is based on the human brain’s neural structure. Neural networks are capable of learning to perform tasks such as prediction, decision-making, classification, visualization, just to name a few.

An artificial neural network contains processing elements or artificial neurons and is organized in different interconnected layers namely input, hidden, and output. In deep learning, different types of neural networks are used. Since the emergence of big data, the field of deep learning has been gaining steady popularity as the performance of neural networks has improved by working with more amounts of data than ever before.

A lot of neural networks are there, each with its unique strengths. Different principles are used by different types of neural networks to determine their own rules. Let’s have a look at the most common ones.

• Convolutional neural network or CNN: A convolutional neural network comes with one or multiple convolutional layers, which can either be pooled or completely interconnected and utilizes a variation of multilayer perceptrons. Before the result is passed on to the next layer, a convolutional operation on the input is used by the convolutional layer. This operation lets the network to be deeper but with much fewer parameters. Convolutional neural networks demonstrate excellent results in speech and image applications.
• Recurrent neural network or RNN: A recurrent neural network is capable of remembering the past the decisions are influenced by what it has learned in the past. In simple words, each node of a recurrent neural network acts as a memory cell while performing computations and carrying out operations. LSTM or Long Short-Term Memory is a specific RNN architecture which was designed to model temporal sequences, as well as, their long-range dependencies more accurately compared to traditional RNNs. The capability of recurrent neural networks suggests that they can make better predictions by learning the temporal context of input sequences. Sequence prediction problems may come in different forms and can be best described by the types of outputs and inputs supported. Some instances of sequence prediction problems may include One-to-Many, Many-to-One, and Many-to-Many. LSTMs, in particular, have received a huge success when working with deep learning. It includes both sequences of spoken language and sequences of text. In general, recurrent neural networks are used for text data, speech data, regression prediction problems, classification prediction problems, and generative models.

Final Takeaway

As you may have understood from the above, a recurrent neural network is the best suited for temporal data in working with deep learning. Neural networks are designed to truly learn and improve more with more usage and more data. And that’s why it’s sometimes said that different kinds of neural networks will be the next-generation AI’s fundamental framework.

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To learn more about deep learning, click here and read our another article.

The post What is the best neural network model for temporal data in deep learning? appeared first on Magnimind Academy.