Training of DHN Part 2

Duration: 4 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 details the testing phase of a Hopfield network, focusing on recovering missing binary data. The instructor begins by displaying a trained weight matrix and defining an incomplete input vector. She then walks through the initialization of the network state and the selection of units for asynchronous updates. The core of the lesson involves introducing the net input formula, which combines external inputs and weighted sums to drive the network towards a stable state. The instructor uses handwritten annotations to distinguish between known and unknown components, providing a clear visual guide for the mathematical process.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video opens with a lecture on neural networks, specifically focusing on the testing phase of a Hopfield network. The screen displays a PDF document showing a weight matrix W = [1 1 0 -1; -1 -1 -1 0] with the annotation "Training complete" written in red ink. The instructor explains that given a binary representation for inputs [1 1 1 0], the goal is to perform asynchronous updation of weights for the first two missing components, resulting in the vector [0 0 1 0]. She writes "Following the testing algorithm" and proceeds to initialize the weights. A new weight matrix W is displayed: a 4x4 matrix with diagonal elements as 0 and off-diagonal elements as 1 or -1. The instructor writes "Bipolar" and "Binary" next to matrices to distinguish representations. She manually writes the vector [0 0 1 0] and circles the first two zeros, labeling them "unknown" and the last two as "known". This setup prepares the student for the iterative process of recovering missing data. The matrix W is explicitly shown as: [0 1 1 -1; 1 0 1 -1; 1 1 0 -1; -1 -1 -1 0]. This initialization is critical for the subsequent calculations.

  2. 2:00 – 3:41 02:00-03:41

    The lecture progresses to the execution of the testing algorithm. The text on screen states: "Input Vector X = [0 0 1 0] and we make initial activation of the network equal to the external input vector X as follows: y_i = x_i". The instructor writes this vector out, emphasizing that the initial state y_i is [0 0 1 0]. She explains the random selection of units for updation, stating, "We choose the unit y_i randomly let us say we choose y_1 for the updation and testing". She then introduces the calculation for the net input of the network for y_1 using the formula: y_in1 = x_1 + sum_{j=1}^4 y_j W_j1. This formula is crucial for determining the new state of the unit. The instructor writes the vector [0 0 1 0] again, marking the '1' as "known" and the '-1' (likely referring to a weight or state) as "eff". She underlines "unknown" to reinforce that the first two components are the target of the recovery process. The visual focus remains on the matrix W and the vector X as the instructor sets up the mathematical framework for the asynchronous update rule. The formula involves summing the products of the current states y_j and the weights W_j1, adding the external input x_1.

The video provides a step-by-step walkthrough of applying a Hopfield network to recover missing binary data. It starts by establishing the trained weight matrix and defining the incomplete input vector. It then transitions to the testing algorithm, initializing the network state to the input vector and selecting a specific unit for the first update. The core of the lesson is the introduction of the net input formula, which combines external inputs and weighted sums of other units to drive the network towards a stable state that matches the original training pattern. The instructor emphasizes the distinction between known and unknown components, guiding the viewer through the initial setup required for the asynchronous update process. This detailed explanation helps students understand how neural networks can be used for pattern completion tasks.

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