Neural Networks and Deep Learning

Introduction

Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. It is a collection of statistical techniques of machine learning for learning feature hierarchies that are based on artificial neural networks. So basically, deep learning is implemented with the help of deep networks, which are nothing but neural networks with multiple hidden layers.

Use Cases

• Image Recognition and Prediction (Manufacturing, Medical, Research, Agriculture, etc.)
• Texts Classification (Sentiment analysis, translation, or contextual entity linking)
• Automotive and Self-Driving Cars
• Retail and Popular Voice Assistants
• Voice-to-Voice Translators for Business & Travel
• Predictive Advertising
• Recommend Engines

What are Neural Networks?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Types of Neural Networks in Deep Learning

Artificial Neural Networks (ANN)

• Feedback ANN
• Feed-Forward ANN

Convolution Neural Networks (CNN)

CNN has so many types here listed a few important types:

• VGG
• ResNet
• AlexNet
• Inception v2

Recurrent Neural Networks (RNN)

• One-to-one
• One-to-Many
• Many-to-One
• Many-to-Many

Types of Algorithms used in Deep Learning

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTMs)
• Generative Adversarial Networks (GANs)
• Self Organizing Maps (SOMs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptron (MLPs)
• Deep Belief Networks (DBNs)
• Restricted Boltzmann Machines( RBMs)
• Autoencoders

Deep Learning Frameworks

• TensorFlow
• Keras
• PyTorch
• Theano
• Caffe
• Deeplearning4j
• MXNet
• Chainer

Check my other blogs…

K-Nearest Neighbor (KNN)-Using Python

Statistics For Data Science — Part 1

Statistics For Data Science — Part 2

Zero-Padding in Convolutional Neural Networks

Image classification and prediction using transfer learning

Have doubts? Need help? Contact me!

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