My Blog.

Subject - Artificial Neural Networks

Course Objectives:

  1. To provide students with a basic understanding of the fundamentals and applications of artificial neural networks
  2. To identify the learning algorithms and to know the issues of various feed forward and feedback neural networks.
  3. To Understand the basic concepts of Associative Learning and pattern classification.
  4. To solve real world problems using the concept of Artificial Neural Networks.

Course Outcomes

On completion of the course, learner will be able to CO1: Understand the basic features of neural systems and be able to build the neural model. CO2: Perform the training of neural networks using various learning rules. CO3: Grasping the use of Associative learning Neural Network CO4: Describe the concept of Competitive Neural Networks CO5: Implement the concept of Convolutional Neural Networks and its models CO6: Use a new tool /tools to solve a wide variety of real-world problems

ANN-SyllabusANN-SyllabusUnit I Introduction to ANN Introduction to ANN,History of Neural Network, Structure and working of Biological Neural Network, Neural net architecture, Topology of neural network architecture, Features, Characteristics, Types, Activation functions,Models of neuron-Mc Culloch & Pitts model, Perceptron, Adaline model,Basic learning laws, Applications of neural networks, Comparison of BNN and ANN. Unit II Learning Algorithms Learning and Memory, Learning Algorithms,Numbers of hidden nodes, Error

Unit I Introduction to ANNUnit I Introduction to ANNOverview This unit introduces the fundamental concepts of Artificial Neural Networks. It covers the historical development of neural networks, the structure and working mechanisms of biological neural networks, and the basic architecture of artificial neural networks. You'll explore the various topologies, features, and characteristics of neural networks, different types of neural networks, and the role of activation functions. Additionally, this unit delves into specific neuron models such as

Unit II Learning AlgorithmsUnit II Learning AlgorithmsOverview This unit focuses on the algorithms that enable neural networks to learn and adapt. It starts with an introduction to the concepts of learning and memory, followed by a detailed examination of various learning algorithms. The unit covers the determination of the number of hidden nodes, error correction techniques, and gradient learning algorithms. Supervised learning is explored in depth, particularly the backpropagation algorithm used in multilayered network architectures. Additionall

Unit III Associative LearningUnit III Associative LearningOverview Associative learning is the focus of this unit, which begins with an introduction to the concept and its significance in neural networks. You'll study Hopfield networks and their error performance, as well as simulated annealing processes. The unit covers Boltzmann machines and Boltzmann learning, including state transition diagrams and the problem of false minima. Stochastic update methods and simulated annealing are also discussed. Finally, the unit explores basic functional units of

Unit IV Competitive learning Neural NetworkUnit IV Competitive learning Neural NetworkOverview This unit delves into competitive learning neural networks. It begins with the components of competitive learning networks and explores pattern clustering and feature mapping networks. You'll learn about Adaptive Resonance Theory (ART) networks, their features, and applications such as character recognition using ART networks. The unit also covers Self-Organizing Maps (SOM), detailing two basic feature mapping models, the SOM algorithm, and the properties of feature maps. Computer simu

Unit V Convolution Neural NetworkUnit V Convolution Neural NetworkOverview This unit provides an in-depth look at Convolutional Neural Networks (CNNs), starting with their building blocks and architectures. It covers convolution and pooling layers, padding, strided convolutions, and convolutions over volumes. You'll learn about SoftMax regression and various deep learning frameworks. The unit also addresses training and testing on different data distributions, handling bias and variance, transfer learning, multitask learning, and end-to-end deep learning. Int

Unit VI Applications of ANNUnit VI Applications of ANNOverview The final unit explores various applications of Artificial Neural Networks. It covers pattern classification and recognition tasks, such as the recognition of Olympic Games symbols and printed characters. The unit introduces the Neocognitron model for recognizing handwritten characters and the NET Talk system for converting English text to speech. Other applications discussed include the recognition of consonant-vowel (CV) segments and texture classification and segmentation. This unit

Learning Resources

ANN - Learning ResourcesANN - Learning ResourcesTo help you achieve a deep understanding of Artificial Neural Networks (ANN), I'll structure your learning path in detail, outlining the topics to cover, suggesting resources, and specifying how to take notes and annotate effectively. This plan will guide you through each unit, ensuring a comprehensive grasp of the material. Unit I: Introduction to ANN Topics to Cover 1. Introduction to ANN 1. History of Neural Networks 1. Structure and Working of Biological Neural Networks 1. Neural Net Arch

Data Science Project

For your Data Science project, consider building a "Predictive Health Analytics" application. Here's a step-by-step guide:

  1. Learning Part:
    • Basics: Start with understanding the basics of data science and statistics.
    • Python Programming: Learn Python for data analytics. Platforms like Codecademy or DataCamp offer interactive Python courses.
    • Data Analytics Lifecycle: Understand the lifecycle of data analytics. Online resources and courses, like on Coursera or edX, can guide you through this.ß
  2. Project: Predictive Health Analytics:
    • Objective: Create a tool that predicts potential health issues based on historical health data.
    • Steps:
      • Data Collection: Gather health-related datasets. Kaggle is a good source for datasets.
      • Data Cleaning: Learn to clean and preprocess data using Python libraries like Pandas.
      • Predictive Model: Implement a predictive model using machine learning algorithms (e.g., scikit-learn).
      • Data Visualization: Use Python visualization tools (like Matplotlib or Seaborn) to create interactive health trends and insights.
  3. Advanced Concepts:
    • Big Data: Explore Hadoop for handling large datasets. Online tutorials and documentation can help.
    • Visualization Tools: Dive deeper into advanced visualization tools like Tableau for more sophisticated data presentation.

Question Paper

Extra QuestionExtra QuestionConsider an ART-I network with input vector \[1,1,0,0\], \[0,0,1,0\], \[1,1,1,0\] and \[1,1,1,1\], want to produce clustering with following data, number of inputs n =4, clusters to be formed m = 3 and vigilance parameter ρ = 0.5 , Compute the result of the first iteration and comment on clustering? * Anyone has solve this question? Pasted image 20240520095303.png