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: About the Course
  2. Paper 1 | General Studies: State Wise GK
  3. Paper 1 | Information and Communication Technology: Role of Internet and Web Services, Security in Mobile and Wireless Computing
  4. Paper 1 | Current Affairs: GK- Current Affairs
  5. Paper 2 | Discrete Mathematical Structure: प्रतिशतता / Percentage, अनुपात, समानुपात, साझेदारी एवं मिश्रण / Ratio, Proportion, Partnership and Mixtures, औसत / Averages, आँकड़ा विश्लेषण एवं शोध सांख्यिकी / Data Analysis and Statistics in Research, क्रमचय, संचय एवं प्रायिकता / Permutation, Combination and Probability, श्रेणी / Series, आँकड़े एवं आलेखीय निरूपण / Data and Graphical Representation, आँकड़ा निर्वचन: तालिका / Data Interpretation: Tables, Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic, Descriptive statistics, exploratory data analysis, Standard discrete and continuous univariate distributions, Univariate and Joint Distributions, Markov Chains
  6. Paper 2 | Digital Computer Organization And Architecture: Number System, Number Representation, Sequential Circuits, Combinational Circuit, Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer
  7. Paper 2 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  8. Paper 2 | Programming Languages & Object-Oriented Programming Concepts: Java, OOPS with C++, Java Fundamentals, Array, String, IO & References
  9. Paper 2 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  10. Paper 2 | Data Structure And Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Minimum Spanning Trees, Advanced Algorithms
  11. Paper 2 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS
  12. Paper 2 | 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, Complexity Theory
  13. 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
  14. Paper 2 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, Transaction Management, Concurrency Control, Database Recovery, Database Security & Authorization, Enhanced Data Models
  15. Paper 2 | Computer Network And Information Security: Introduction to CN, Data Communication, Application Layer, Hardware basics, Cyber Threats and Malware Attacks, Network Security, 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
  16. 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
  17. Paper 2 | Current Trends & Technologies: कंप्यूटर की मूल बातें और हार्डवेयर / Computer Fundamentals and Hardware, पारितंत्र और जैव विविधता / Ecosystems and Biodiversity, मेमोरी, भंडारण और डेटा निरूपण / Memory, Storage and Data Representation, कंप्यूटर सॉफ्टवेयर / Computer Software, निरुपाधिक प्रतिज्ञप्तियाँ / Categorical Propositions, ऑफिस अनुप्रयोग / Office Applications, संकेत विज्ञान एवं अर्थ / Semiotics and Meaning, मल्टीमीडिया और फ़ाइल प्रारूप / Multimedia and File Formats, कंप्यूटर नेटवर्क / Computer Networks, संचार की बाधाएँ / Barriers to Communication, उच्च शिक्षा संस्थानों के प्रकार / Types of Higher Education Institutions, इंटरनेट और वर्ल्ड वाइड वेब / Internet and World Wide Web, ई-मेल और ऑनलाइन संचार / Email and Online Communication, शिक्षण विधियाँ / Methods of Teaching, साइबर सुरक्षा / Cyber Security, डिजिटल नैतिकता, बौद्धिक संपदा और समाज / Digital Ethics, IPR and Society, शोध-प्रबंध एवं लेख लेखन / Thesis and Article Writing, डिजिटल एवं नव माध्यम / Digital and New Media, उभरती प्रौद्योगिकियाँ / Emerging Technologies, प्राकृतिक प्रकोप और आपदा प्रबंधन / Natural Hazards and Disaster Management, शिक्षा और शासन में ICT / ICT in Education and Governance, क्लाउड और वितरित कंप्यूटिंग / Cloud and Distributed Computing, Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Planning, Multi Agent Systems, Fuzzy Sets, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms, Data Warehousing & Mining, Electronic Data Interchange and E-Commerce
  18. Paper 2 | Full Length Mock Tests:
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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