A Neural Network: Explained

A neural network is an ensemble of neurons connected to each other by synapses. This connection comprises three primary parts - the input layer, the hidden layer, and the output layer.

An artificial neural network, however, comprises, several inputs, termed as features, which produce a single output, known as a label.

The Human Mind and an Artificial Neural Network

It is believed, in the field of science, that a living being’s brain processes information via a biological neural network. For instance, a human brain functions through around 100 trillion synapses which are activated in particular patterns when functioning.

In the theatre of Deep learning, a neural network works much like a human brain and scientists use these to teach computers to perform tasks on their own. Deep Learning and Neural networks are gradually becoming a popular course. So, don’t forget to check the Deep learning Certification in Gurgaon and Neural Networks Training in Delhi.

There are many kinds of deep learning and neural networks:

1. Feedforward Neural Network – Artificial Neuron
2. Radial basis function Neural Network
3. Kohonen Self Organizing Neural Network
4. Recurrent Neural Network (RNN) – Long Short Term Memory
5. Convolutional Neural Network
6. Modular Neural Network
7. Generative adversarial networks (GANs)

Some points to be remembered while building a strong Neural Network

Adding Regularization to Fight Over-Fitting

The predictive models mentioned above are prone to a problem of overfitting. This is a scenario whereby the model memorizes the results in the training set and isn’t able to generalize on data that it hasn’t seen.

In neural networks, regularization is the technique that fights overfitting by adding a layer in the neural network. It can be done in 3 ways:

·         L1 Regularization

·         L2 Regularization

·         Dropout Regularization

Out of these, Dropout is a commonly used regularization technique. In every iteration, it adds a Dropout layer in the neural network and thereby, deactivates some neurons. The process of deactivating neurons is usually random.

Hyperparameter Tuning

Grid search is a technique that you can use to experiment with different model parameters to obtain the ones that give you the best accuracy. This is done by trying different parameters and returning those that give the best results. It helps in improving model accuracy.

Conclusion

Neural Network is coping with the fast pace of the technology of the age remarkably well and thereby, inducing the necessity of courses like Neural Network Machine Learning Python, Neural Networks in Python course and more. Though these advanced technologies are just at their nascent stage, they are promising enough to lead the way to the future. 

In this article, Building and Training our Neural Network is shown. This simple Neural Network can be extended to Convolutional Neural Network and Recurrent Neural Network for more advanced applications in Computer Vision and Natural Language Processing respectively.

To continue reading, click here: www.dexlabanalytics.com/blog/what-is-a-neural-network

Interested in a career as a Data Analyst?

To learn more about Data Analyst with Advanced excel course — Enrol Now.
To learn more about Data Analyst with R Course — Enrol Now.
To learn more about Big Data Course — Enrol Now.
To learn more about Machine Learning Using Python and Spark — Enrol Now.
To learn more about Data Analyst with SAS Course — Enrol Now.
To learn more about Data Analyst with Apache Spark Course — Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course — Enrol Now.