Training of DHN Part 3

Duration: 18 min

This video lesson is available to enrolled students.

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Inside: a video lesson and guided study material.

Module outline

  1. About Course: UPPSC Course Overview
  2. Interview Preparation & Guidance: Interview Preparation
  3. Paper 1 | General Hindi: हिन्दी की रूप रचना, वर्णमाला, समास, Multi-Topic Content (Cross-Concept Material), हिन्दी की ध्वनि और स्वनिम व्यवस्था, प्राचीन और मध्यकालीन भारतीय आर्यभाषाएँ, काव्य के लक्षण, हेतु और प्रयोजन, हिन्दी शब्द रचना, हिन्दी साहित्येतिहास दर्शन, भक्तिकालीन काव्यधाराएँ, भारतीय काव्यशास्त्र की परंपरा, भारतीय नवजागरण और स्वाधीनता आंदोलन, अन्य गद्य विधाएँ एवं प्रवासी साहित्य, निर्धारित उपन्यासों का तुलनात्मक अध्ययन, गोदान, हिन्दी कहानी का विकास, स्वातंत्र्योत्तर और नयी कहानी, हिन्दी निबंध का विकास, शुक्लोत्तर निबंध, द्विवेदी और शुक्ल युग के निबंध, हिन्दी की संवैधानिक स्थिति, आधुनिक भारतीय आर्यभाषाएँ और हिन्दी का उद्भव, हिन्दी भाषा प्रयोग के विविध रूप, रीतिकाल, जीवनी, द्विवेदी युग और छायावाद, रीति, वक्रोक्ति और औचित्य सिद्धान्त, शब्दशक्ति और ध्वनि सिद्धान्त, रस सिद्धान्त और साधारणीकरण, उत्तर आधुनिकतावाद, लोहिया दर्शन, हिन्दी उपन्यास का विकास, हिन्दी नवजागरण, अंबेडकर दर्शन, आदिकाल, प्रयोगवाद और नयी कविता, छायावादी काव्य: प्रसाद और निराला, प्रगतिवाद से समकालीन कविता, निर्गुण भक्ति काव्य, द्विवेदीयुगीन काव्य, रीतिकालीन काव्य, आरंभिक हिन्दी उपन्यास, स्वातंत्र्योत्तर उपन्यास, आत्मकथा, स्वाधीनता-पूर्व प्रेमचंदोत्तर उपन्यास, निर्धारित कहानियों का तुलनात्मक अध्ययन, साठोत्तरी नाटक, प्रसाद और प्रसादोत्तर युग के नाटक, भारतेन्दु युगीन नाटक, स्वातंत्र्योत्तर नाटक, भारतेन्दु युगीन निबंध, संस्मरण और रेखाचित्र, व्यंग्य, एक साहित्यिक की डायरी, संस्कृति के चार अध्याय, सगुण भक्ति काव्य, हिन्दी नाटक, एकांकी और रंगमंच, आधुनिक गद्य और हिन्दी नवजागरण, अलंकार सिद्धान्त, हिन्दी आलोचना का विकास, भक्ति आंदोलन, कल्पना, बिम्ब, प्रतीक, मिथक और फंतासी, नयी कविता का विस्तार और साठोत्तरी कविता, उत्तर-छायावादी और प्रगतिवादी काव्य, काव्यगुण और काव्यदोष, प्लेटो, अरस्तू और पाश्चात्य परंपरा, मार्क्सवाद, स्वच्छन्दतावादी काव्यशास्त्र, आधुनिक पाश्चात्य आलोचना, भाषा और भाषा परिवार, देवनागरी लिपि
  4. Paper 2 | General Studies: State Wise GK / राज्यवार सामान्य ज्ञान (GK), Polity, Geography, History, GK- Current Affairs
  5. Paper 1 | Programming Language: Language Design, C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, OOPS with C++
  6. Paper 1 | Database Management Systems: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, ORDBMS, NoSQL
  7. Paper 1 | Cyber Security: Computer Fundamentals and Hardware, प्रत्यक्ष बोध / Literal Comprehension, Computer Software, निष्कर्षात्मक बोध / Inferential Comprehension, आलोचनात्मक बोध / Critical Comprehension, Internet and World Wide Web, Email and Online Communication, Cyber Security, अपशिष्ट प्रबंधन / Waste Management, Digital Ethics, IPR and Society, डिजिटल एवं नव माध्यम / Digital and New Media, शोध नैतिकता / Research Ethics, Emerging Technologies, ICT in Education and Governance, Society, Law & Ethics, Cyber Threats and Malware Attacks, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Electronic Data Interchange and E-Commerce, Physical Security and Cryptography Basics, Intrusion Detection Systems (IDS), Cyber Safety and Security, Ethical Hacking, Protection of Geographical Indications / भौगोलिक संकेतों का संरक्षण
  8. Paper 1 | Computer Organization and Architecture: Basics of COA, Programming the Basic Computer, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design
  9. Paper 1 | Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  10. Paper 1 | Design and Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  11. Paper 1 | Data Structures: Introduction to DS, Array, Linked List, Stack, Queue, Tree, Graphs, Hashing
  12. Paper 1 | Discrete Structures and Theory of Logic: क्रमचय, संचय एवं प्रायिकता / Permutation, Combination and Probability, Set Theory, Relations, Functions, Propositional and Predicate Logic, Graph Theory, Group Theory
  13. Paper 1 | Software Project Management: Software Project Management
  14. Paper 2 | Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS, Linux OS, Security
  15. Paper 2 | Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment, Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Pushdown Automata & CFG, Complexity Theory
  16. Paper 2 | Artificial Intelligence & High Performance Computing: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Planning, Multi Agent Systems, Natural Language Processing, Cloud and Distributed Computing
  17. Paper 2 | Image Processing: Digital Image Fundamentals, Image Enhancement, Image Restoration, Color Image Processing, Image Compression
  18. Paper 2 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  19. Paper 2 | Computer Graphics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing
  20. Paper 2 | Web Technologies: Website, Browser & Services, Internet Fundamentals, Web Design & Development, Protocols & Services, Web Publishing & Hosting, Server Side & Security, Emerging Technologies, HTML Basics, JavaScript-Basics, Java
  21. Paper 2 | Soft Computing: Fuzzy Sets, Artificial Neural Networks, Genetic Algorithms
  22. Paper 2 | Distributed System: Distributed Systems
  23. UPPSC Live Class Recordings: UPPSC Polytechnic Lecturer 2025 (CS)
  24. Previous Year Papers:
  25. Mock Test:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video lecture covers the testing and recall process of a Discrete Hopfield Network, followed by an introduction to Self-Organizing Maps (SOM). The first half demonstrates the asynchronous update algorithm where a network recalls a stored pattern from a partial input vector. The instructor calculates net inputs for individual units, applies activation functions, and checks for convergence. The second half transitions to SOM, defining it as a competitive learning method for dimensionality reduction, explaining the 'winner-takes-all' mechanism, and illustrating the network structure with input and output grids.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a calculation already in progress on the screen. The equation shows a scalar addition: $1 + [1 \ 0 \ 1 \ 0] imes ext{column vector} = 2$. The text below explains the logic: 'Since here $y_{in3} > 0$; applying the activation function we get the updated value of $y_3 = 1$. Thus no updation required.' This indicates the unit was already in the correct state or the net input confirms the current state. The instructor then transitions to the next step in the asynchronous update process, stating, 'Let us choose $y_2$ for updation and testing now.' The slide introduces the formula for calculating the net input for the second unit: $y_{in2} = x_2 + \sum_{j=1}^4 y_j W_{j2}$. This formula sums the external input $x_2$ and the weighted sum of inputs from other units $y_j$ connected via weights $W_{j2}$. The instructor is guiding the student through the step-by-step verification of the Hopfield network's recall capability.

  2. 2:00 – 5:00 02:00-05:00

    The instructor proceeds to solve the equation for $y_{in2}$. The slide displays the calculation: $0 + [1 \ 0 \ 1 \ 0] imes ext{column vector} = 0 + 2 = 2$. The result is positive, leading to the conclusion: 'Since here $y_{in2} > 0$; applying the activation function we get the updated value of $y_2 = 1$.' The instructor explains that this value is then broadcasted to all other units, resulting in the updated vector $y_i = [1 \ 1 \ 1 \ 0]$. A key check is performed: 'Since $y_i = ( ext{original}) x_i$; Thus it has converged.' This confirms the network has successfully recalled the stored pattern from the partial input. The slide also includes a 'Points to note' section, stating the purpose of a Hopfield net is to store patterns and recall full patterns based on partial input. The instructor emphasizes that the network can store one or more patterns and recall them even when the input is corrupted.

  3. 5:00 – 10:00 05:00-10:00

    The video rewinds to the beginning of the testing algorithm to show the setup. The slide displays the weight matrix $W$ initialized with zeros on the diagonal, reflecting the rule that 'we don't have self connections.' The matrix is a $4 imes 4$ grid with values like $1, -1$. The input vector is defined as $X = [0 \ 0 \ 1 \ 0]$, representing a corrupted or partial version of the stored pattern. The initial activation of the network is set equal to this external input vector: $y_i = x_i$, so $y_i = [0 \ 0 \ 1 \ 0]$. The instructor then selects a unit randomly for the first update, choosing $y_1$. This sets the stage for the asynchronous update process where units are updated one by one until convergence. The slide explicitly shows the weight matrix $W$ with the first row being $[0 \ 1 \ 1 \ -1]$.

  4. 10:00 – 15:00 10:00-15:00

    The calculation for the net input of unit $y_1$ is performed. The formula is $y_{in1} = x_1 + \sum_{j=1}^4 y_j W_{j1}$. The slide shows the substitution: $0 + [0 \ 0 \ 1 \ 0] imes ext{column vector} = 0 + 1 = 1$. Since the net input $y_{in1}$ is greater than 0, the activation function updates the value of $y_1$ to 1. The instructor notes that this value is broadcasted, resulting in the updated vector $y_i = [1 \ 0 \ 1 \ 0]$. However, the slide explicitly states, 'Still it has not converged to the vector X.' This highlights that the network requires multiple iterations of updating different units to reach the stable state. The instructor then prepares to choose $y_3$ for the next updation step. The slide shows the vector $y_i$ changing from $[0 \ 0 \ 1 \ 0]$ to $[1 \ 0 \ 1 \ 0]$.

  5. 15:00 – 18:25 15:00-18:25

    The lecture transitions to a new topic: Self-Organizing Maps (SOM). The slide defines SOM as a discretized representation of the input space, often two-dimensional, used for dimensionality reduction. It contrasts SOM with other neural networks by stating they apply 'competitive learning' rather than 'error-correction learning.' The text explains that in competitive learning, output neurons compete to be activated, resulting in a 'winner-takes-all' neuron. A diagram illustrates the SOM structure with input units connecting to a 2D grid of output units. The text mentions that SOM was introduced by Teuvo Kohonen in the 1980s and is sometimes called a 'Kohonen feature map.' It also notes that neighborhood constraints are imposed on output units, meaning the winning neuron and its neighbors are updated. The diagram shows 2 inputs ($x_1, x_2$) and 49 outputs arranged in a grid.

The lecture provides a comprehensive walkthrough of Hopfield Network testing, demonstrating how a network can recall a complete pattern from a partial input through asynchronous updates. The instructor meticulously calculates net inputs for each unit, applies threshold activation functions, and verifies convergence against the original stored vector. This practical example reinforces the theoretical concept of associative memory. The video then pivots to Self-Organizing Maps (SOM), introducing them as a dimensionality reduction tool based on competitive learning. Key distinctions are made between error-correction learning and competitive learning, where neurons compete to become the 'winner.' The lecture concludes by describing the SOM architecture, highlighting the 2D grid of output units and the concept of neighborhood constraints, which are essential for preserving the topological properties of the input space.

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