Machine Learning for Neural Networks

Machine Learning serves as the sub-core of Artificial Intelligence.The concept of Machine learning helps computers to use the self-learning mode without the need for specific programming. The idea of Machine Learning evolved from pattern recognition and computational theory and that is of use in Artificial Intelligence.

Machine learning deals with the development and study of algorithms that allows you to make predictions on data. Machine learning is of extensive use in computing tasks.

Neural network consists of a pool of simple processing units that communicate by sending signals to each other over a large number of weighted connections. In neural networks, many units perform computation at the same time and hence the system is inherently parallel computing.

Machine learning helps to solve the problems that cannot be solved numerically. Neural networks adapt well to machine learning when the number of inputs is huge or gigantic.

Neural networks effectively tune themselves using the techniques that are similar to gradient descent in principle. The original goal of the neural network approach was to solve problems in the same way as that of a human brain. Over time, backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information evolved.

Applications of Neural networks include a variety of tasks, like computer vision, speech recognition, machine translation, social network filtering, playing board and video games, medical diagnosis and in many other domains.

Applications of Machine Learning includes web search, credit scoring, spam systems, fraud detection, stock trading and computer vision and much more. Machine learning makes an enormous contribution to the industry as it automates the process of data mining.

Why Machine Learning for Neural Networks?

Machine learning for neural networks supports Robotics including direct manipulators and allows you to perform regression analysis, time series prediction, modeling and fitness approximation.

Machine learning is a form of artificial intelligence in which the computer learns and complete a task by itself. Machine learning for neural network will change the ways humans operate more than any technology that's ever existed before.

Machine learning for neural networks is sure to bring huge benefits to humanity as a whole. This enables a world of machines which will work tirelessly, innovate, and improve themselves.

Why should I learn Machine Learning for Neural Networks?

Neural networks, posess remarkable ability to extract meaning from complicated or imprecise data. They make use of this data to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.

A trained neural network is known as an "expert" in the category of information that has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.

Gaining knowledge on Machine Learning for neural networks allows you to understand adaptive learning, perform real time operation and self organize the data.

What will I learn?

• Learn about Machine learning and its types
  
• Understand the Neural networks algorithms
  
• Learn to do tasks based on the data given for training experience
  
• Use ANN and create its own representation or organisation of information
  
• Gain skills in performing real time operations using ANN
  
• Determine the procedure for backpropogation
  
• Understand about the Deep Belief Network, Restricted Boltzmann Machines and Recurrent Neural Network
  

Requirements

• Basic knowledge of Computer programming terminology
  
• Basic experience/ awareness of any programming language
  
• Familiarity with mathematics and statistics
  

Audience

• Anyone interested in the data science field and willing to become a Data Scientist
  
• Software Engineers ready to change their career to the field of Data Science and Machine Learning
  

Course Curriculum

Lesson 1: Introduction to Machine Learning

Machine learning serves a significant role in the present data world. Machine learning is applicable in web search results, mobile devices, email spam filtering, web pages advertisements, network intrusion detection, image recognition and pattern or handwriting recognition.

In this lesson, you will gain knowledge on machine learning and their uses.

Class 1.1:

• Machine Learning Overview
  
• Benefits of Machine Learning
  
• Applications of Machine Learning
  

Lesson 2: Types of Machine Learning

Supervised learning is the commonly used method to Machine learning. In supervised learning, the data consist of labeled inputs and known outputs, like mapping "x" inputs to "y" outputs.

In Unsupervised learning, Machine Learning provides output data without any input data. Unsupervised learning is more typical of human and animal learning. Unsupervised learning is more applicable than supervised learning as in this learning there is no requirement for a human expert to label the data manually.

Reinforcement learning is less commonly used and is useful for learning to reward data for training the machine within a particular context.

• Supervised learning
  
• UnSupervised learning
  
• Reinforcement learning
  

Lesson 3: Perceptron Learning Procedure

This chapter details the main types of neural network architecture.

Class 3.1:

• Types of Neural Networks
  
• Perceptrons
  
• Geometrical view of Perceptrons
  

Lesson 4: Backpropagation

This lesson explains you the method of Backpropogation which is a method to calculate the gradient of the loss function in regards to the weights in an Artificial Neural network. The optimization algorithm in backpropogation repeats a two phase namely propogation and weight update.

Class 4.1:

• Learn the weights of a linear neuron
  
• Identify the error surface for a linear neuron
  
• Learn the weights of a logistic output neuron
  

Class 4.2:

• The backpropagation algorithm
  
• Using the derivatives computed by backpropagation
  
• Forward Propagation in Neural Networks
  

Lesson 5: Artificial Neuron Networks

Artificial neural networks(ANNs)are computing systems motivated by the biological neural networks that constitute animal brains. ANN are very useful in applications that are difficult to express in a traditional computer algorithm using rule-based programming.

An ANN is based on a collection of connected units called artificial neurons, Each connection between neurons transmits a signal to another neuron. Neurons has state, generally represented by real numbers, typically between 0 and 1. Neurons and synapses also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.

Class 5.1:

• Definition of ANN
  
• Structure of ANN
  
• Feed Forward Pass
  
• Gradient descent and optimization
  

Lesson 6: Object recognition with Neural Networks

Class 6.1:

• About Object Recognition
  
• Convolutional network for digit recognition
  
• Convolutional network for object recognition
  

Class 6.2:

• Feature selection 
  
• Training/ Test curves
  
• Adding other features
  
• Regression ML block diagram
  

Lesson 7: Implementing the neural network in Python

This chapter teaches the method to scale data, create datasets, create the neural network using Python and maintain acuuracy of the trained model.

Class 7.1:

• Scaling data
  
• Create datasets
  
• Setup the outer layer
  
• Create the neural network
  
• Maintain accuracy of the trained model
  

Lesson 8: Recurrent Neural Networks

The neural network that involves a sequence of inputs is known as recurrent neural networks. Recurrent neural networks include connections which have loops, add feedback and memory to the networks over time. Memory allows the network to learn and generalize across sequences of inputs than individual patterns.

A dominant type of Recurrent Neural Network called the Long Short-Term Memory Network is available for a deep configuration.

Class 8.1:

• Introduction to RNN
  
• Application of RNNs
  
• Long Short-Term Memory(LSTM)
  
• Recursive Neural Tensor Network Theory
  
• Working with Neural Network Model
  

Lesson 9: DBNs and RBMs

Deep Belief Network (DBN) is a deep architecture that contains a stack of Restricted Boltzmann Machines (RBM). The advantage of deep architecture is that each layer learns more complex features than layers before it. DBN and RBM serve as a feature extraction method and also used as neural network with initially learned weights.

DBN provides a better a performance than the traditional neural network due the initialization of the connecting weights than just random weights in NN. The Deep Belief network is a generative model that mixed undirected and directed connections between variables. The top two layers is an RBM and other layers form a Bayesian network. The Deep Belief network is not a Feed forward network.

Class 9.1:

• About Deep Belief network
  
• Boltzmann machine learning 
  
• Restricted Boltzmann Machines
  

Class 9.2:

• Examples of RBM learning 
  
• RBMs for collaborative filtering 
  
• Clustering and Classification using DBNs