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 the course: About the Course
  2. Mathematical Aptitude: संख्या पद्धति / Number System, महत्तम समापवर्तक और लघुत्तम समापवर्त्य / HCF and LCM, Divisibility Rules, प्रतिशत / Percentage, अनुपात और समानुपात / Ratio and Proportion, औसत / Average, लाभ, हानि और बट्टा / Profit, Loss and Discount, समय और कार्य / Time and Work, घन क्षेत्रमिति / Solid Mensuration, साधारण और चक्रवृद्धि ब्याज / Simple and Compound Interest, समय, चाल और दूरी / Time, Speed and Distance, Age Problems, मिश्रण और एलिगेशन / Mixture and Alligation, Algebra, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, Pipe and Cistern, Trigonometry, Statistics, आँकड़ा निर्वचन / Data Interpretation, Arithmetic, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers
  3. Logical Reasoning: श्रेणी / Series, कूट लेखन एवं कूट वाचन / Coding-Decoding, सादृश्यता एवं वर्गीकरण / Analogy and Classification, रक्त संबंध / Blood Relations, दिशा एवं दूरी / Direction and Distance, क्रम एवं रैंकिंग / Order and Ranking, अशाब्दिक तर्क / Non-Verbal Reasoning, बैठक व्यवस्था एवं पहेलियाँ / Seating Arrangement and Puzzles, Syllogisms, वेन आरेख, न्याय वाक्य एवं असमानता / Venn Diagrams, Syllogism and Inequalities, गणितीय संक्रियाएँ एवं लुप्त संख्या / Mathematical Operations and Missing Numbers, वर्णमाला एवं संख्या परीक्षण / Alphabet and Number Test, कथन आधारित तार्किक विवेचन / Statement-Based Reasoning, Analytical Reasoning (Counting Figures Reasoning), घन, पासा एवं आकृतियों की गिनती / Cubes, Dice and Figure Counting, कैलेंडर एवं घड़ी / Calendar and Clock, Clock
  4. General Knowledge: वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research, Chemistry, Biology, History, Geography, Polity, Indian Economy, समसामयिकी / Current Affairs, Static General Knowledge, राज्य विशेष सामान्य ज्ञान / State-Specific General Knowledge
  5. Art of Teaching: शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, आकलन एवं मूल्यांकन / Assessment and Evaluation, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development, शैक्षिक नीतियाँ, अधिनियम एवं प्रशासन / Educational Policies, Acts and Administration, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, निर्देशन एवं परामर्श / Guidance and Counselling, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, Micro-Teaching & Teaching Skills, पाठ योजना / Lesson Planning
  6. Environmental Science: Environment
  7. Unit 1 | Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  8. Unit 2 | COA: Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Programming the Basic Computer
  9. Unit 3 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  10. Unit 3 | Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. Unit 4 | Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  12. Unit 5 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  13. Unit 6 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Transaction Management, Concurrency Control
  14. Unit 7 | 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
  15. Unit 8 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  16. Unit 9 | Object Oriented Programming: OOPS with C++
  17. Unit 10 | Web Development: HTML Basics, CSS, JavaScript-Basics, XML, Node.js, Git & Github, PHP
  18. Unit 11 | Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines
  19. Unit 12 | IoT: Emerging Tech Trends, Cloud Computing, कंप्यूटर नेटवर्क / Computer Networks, Internet of Things
  20. Unit 13 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  21. Unit 14 | E-Commerce: Electronic Data Interchange and E-Commerce, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Physical Security and Cryptography Basics, Cyber Safety and Security
  22. Unit 15 | Multimedia: Multimedia
  23. Unit 16 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  24. Full Length Mock Tests — General Paper:
  25. Full Length Mock Tests — Computer Science (Paper II):
  26. Full Length Mock Test – General + Computer Science:
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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