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ANN - Resources 2

Structured Learning Path for Artificial Neural Networks (ANN)

Unit I: Introduction to ANN

  1. Introduction to ANN

  2. History of Neural Networks

  3. Structure and Working of Biological Neural Networks

    • Objective: Understand the biological basis and how it inspires ANN.
    • Resources:

  4. Neural Net Architecture & Topology

  5. Features, Characteristics, and Types of Neural Networks

  6. Activation Functions

  7. Models of Neuron: McCulloch-Pitts, Perceptron, Adaline Model

    • Objective: Study the basic neuron models.
    • Resources:
      • Book: "Neural Networks: A Comprehensive Foundation" by Simon Haykin (Chapters on McCulloch-Pitts, Perceptron, Adaline)
      • Video: Perceptron and Adaline

  8. Basic Learning Laws

  9. Applications of Neural Networks

  10. Comparison of Biological and Artificial Neural Networks (BNN vs. ANN)

Unit II: Learning Algorithms

  1. Learning and Memory

    • Objective: Understand the concepts of learning and memory in neural networks.
    • Resources:

  2. Learning Algorithms

  3. Number of Hidden Nodes

  4. Error Correction and Gradient Learning Algorithms

    • Objective: Learn about error correction and gradient-based learning methods.
    • Resources:

  5. Supervised Learning Backpropagation

  6. Multilayered Network Architectures

  7. Feedforward and Feedback Neural Networks

Unit III: Associative Learning

  1. Introduction to Associative Learning

  2. Hopfield Network

  3. Error Performance in Hopfield Networks

  4. Simulated Annealing

  5. Boltzmann Machine and Boltzmann Learning

    • Objective: Study the Boltzmann machine

and its learning algorithms.

  1. State Transition Diagram and False Minima Problem

  2. Stochastic Update and Simulated Annealing

  3. Basic Functional Units of ANN for Pattern Recognition Tasks

Unit IV: Competitive Learning Neural Network

  1. Components of CL Network

  2. Pattern Clustering and Feature Mapping Network

  3. ART Networks and Features of ART Models

  4. Character Recognition using ART Network

  5. Self-Organizing Maps (SOM)

  6. SOM Algorithm and Properties of Feature Map

  7. Learning Vector Quantization (LVQ)

  8. Adaptive Pattern Classification

Unit V: Convolution Neural Network (CNN)

  1. Building Blocks of CNNs

    • Objective: Understand the basic building blocks of CNNs.
    • Resources:
      • Book: "Deep Learning with Python" by François Chollet (Chapter 5)
      • Video: CNN Basics

  2. CNN Architectures

  3. Convolution/Pooling Layers, Padding, Strided Convolutions

  4. Convolutions over Volumes, SoftMax Regression

  5. Deep Learning Frameworks

  6. Training and Testing on Different Distributions

  7. Bias and Variance with Mismatched Data Distributions

  8. Transfer Learning, Multitask Learning, End-to-End Deep Learning

  9. Introduction to CNN Models: LeNet-5, AlexNet, VGG-16, Residual Networks

.youtube.com/watch?v=AkQskwzK0Uk)

Unit VI: Applications of ANN

  1. Pattern Classification - Recognition of Olympic Games Symbols

  2. Recognition of Printed Characters

  3. Neocognitron - Recognition of Handwritten Characters

  4. NET Talk: Convert English Text to Speech

  5. Recognition of Consonant-Vowel (CV) Segments

  6. Texture Classification and Segmentation

Additional Resources

  • Research Papers: Use databases like IEEE Xplore, Google Scholar, and ResearchGate to find relevant research papers.
  • Blogs and Articles: Follow platforms like Towards Data Science, Medium, and Analytics Vidhya for the latest articles and blogs.
  • YouTube Channels: Subscribe to channels like 3Blue1Brown, Sentdex, and Two Minute Papers for in-depth videos and explanations.
  • Courses and Tutorials: Enroll in online courses from Coursera, edX, and Udacity to get structured learning experiences.

Note-Taking and Annotation Tools

  • Obsidian: Utilize Obsidian for structured note-taking with plugins for mind maps, backlinks, and tags.
  • Zotero: Use Zotero for managing research papers and annotating PDFs.
  • Hypothesis: Use Hypothesis for web annotations, allowing you to highlight and take notes on articles and blogs.

Mind Maps and Visual Tools

  • XMind: Create mind maps to visually organize and recall information.
  • Lucidchart: Use Lucidchart for creating diagrams and flowcharts to illustrate neural network architectures and algorithms.

Structured Learning TipsStructured Learning TipsStructured Learning Tips for Artificial Neural Networks (ANN) 1. Start with Overviews Purpose:** Getting a high-level overview helps in setting the context and provides a broad understanding of the topic before diving into specifics. Action Steps:** * Watch Introductory Videos: Begin each unit by watching short introductory videos to grasp the fundamental concepts. * Example Videos: "Neural Networks in 5 Minutes" by Simplilearn, "Introduction to Convolutional Neural Networks" by Deeplear

  • Start with Overviews: Begin each unit by getting a high-level overview through videos and introductory articles.
  • Deep Dive into Specific Topics: Follow up with detailed articles, book chapters, and research papers.
  • Practice and Implementation: Implement the concepts through coding exercises and projects using frameworks like TensorFlow and PyTorch.
  • Regular Review: Periodically review your notes and mind maps to reinforce your understanding.
  • Engage in Discussions: Join forums and discussion groups like Reddit, Stack Overflow, and specialized LinkedIn groups to discuss and clarify doubts.

By following this structured learning path and utilizing the resources and tools mentioned, you can gain a deep and comprehensive understanding of Artificial Neural Networks.