Back Propagation

Duration: 9 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
  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):
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video provides a structured explanation of the backpropagation algorithm in neural networks, focusing on how weights and biases are updated to minimize error. It begins by introducing the challenge of adjusting network parameters after forward propagation, where actual outputs are compared to expected values. The instructor emphasizes that backpropagation involves propagating the error backward through the network, adjusting weights using gradient descent principles. A key formula shown on screen is 'w = w - η * ∂E/∂w', illustrating the weight update rule, where η represents the learning rate and ∂E/∂w is the partial derivative of error with respect to weight. The process is described as iterative, repeating thousands of times until the network achieves accurate predictions. This foundational concept is then applied to a practical problem from UGC NET December 2023, involving a feedforward neural network with four input neurons, three hidden neurons, and two output neurons. The problem specifies weight matrices for connections from the input to hidden layer (W1, W2, W3) and from the hidden to output layer (W4, W5), along with input values [0.5, 0.8, 0.2, 0.6]. The solution proceeds through a step-by-step forward propagation process: first calculating weighted sums for each hidden neuron (e.g., h1 = 0.5*0.1 + 0.8*0.2 + 0.2*0.3 + 0.6*0.5), then applying the sigmoid activation function to obtain hidden layer activations, and finally computing output layer values using the same method. The video demonstrates intermediate calculations, including h2 = 0.5*0.4 + 0.8*0.6 + 0.2*0.5 + 0.6*0.7, and shows the application of sigmoid activation to each neuron. The final output is rounded to two decimal places, with multiple-choice options provided for the answer. Throughout, the instructor uses handwritten annotations and on-screen text to highlight key steps, reinforcing the computational flow. The video concludes by emphasizing that this process is a standard exam question format requiring precise calculation of forward propagation in neural networks, with the correct output derived from systematic application of weights and activation functions.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video introduces the backpropagation algorithm as a method for updating weights and biases in neural networks. The instructor poses the question, 'How do we get to the updated weights and biases?' and explains that backpropagation involves comparing actual outputs from forward propagation with expected values, then adjusting weights backward through the network to reduce error. On-screen text highlights key phrases such as 'Back propagation Algorithm' and 'Update w = w - η * ∂E/∂w', illustrating the weight update rule. The instructor emphasizes that this process is repeated thousands of times until the network can accurately predict outputs, with handwritten annotations reinforcing the iterative nature of training.

  2. 2:00 – 5:00 02:00-05:00

    The video presents a UGC NET December 2023 exam question involving a feedforward neural network with four input neurons, three hidden neurons, and two output neurons. The problem specifies weight matrices W1, W2, W3 for input-to-hidden connections and W4, W5 for hidden-to-output connections, along with input values [0.5, 0.8, 0.2, 0.6]. The instructor begins the solution by outlining forward propagation steps, focusing on calculating weighted sums for hidden layer neurons using the sigmoid activation function. On-screen text displays the problem statement, weight matrices, and multiple-choice options for the output layer values. The teaching cues include step-by-step computation of intermediate activations, with handwritten annotations showing the application of sigmoid functions and weighted sums.

  3. 5:00 – 9:05 05:00-09:05

    The video demonstrates the forward pass of a neural network, showing detailed calculations for hidden layer neuron values h1 and h2 using the formula h1 = 0.5*0.1 + 0.8*0.2 + 0.2*0.3 + 0.6*0.5 and h2 = 0.5*0.4 + 0.8*0.6 + 0.2*0.5 + 0.6*0.7. After computing the weighted sums, the sigmoid activation function is applied to each hidden neuron output. The instructor then proceeds to calculate the final outputs of the network by applying weights from the hidden layer (W4, W5) to the activated hidden neurons. The process includes summing weighted inputs for each output neuron and applying sigmoid activation again. On-screen text reinforces the structure of the network, input values, weight matrices, and the requirement to round the final answer to two decimal places. The video concludes with a focus on the correct output value, derived from systematic computation of forward propagation.

The video systematically teaches the backpropagation algorithm by first explaining its conceptual basis—adjusting weights to minimize error through iterative gradient descent—and then applying it to a concrete exam-style problem. The progression moves from theoretical explanation to practical computation, demonstrating how forward propagation works in a feedforward network with specified layers and weights. Key elements include the use of sigmoid activation functions, weighted sum calculations at each neuron, and step-by-step evaluation leading to a final output. The problem is framed as a UGC NET question, emphasizing the importance of precise calculation and understanding of neural network architecture. The video effectively bridges theory and application, showing how mathematical operations in the forward pass are essential for training networks. The repeated emphasis on iterative processes and error minimization underscores that backpropagation is not a one-time adjustment but a repeated optimization procedure. The use of on-screen text and handwritten annotations supports visual learning, making complex concepts more accessible. Overall, the video serves as a comprehensive revision tool for students preparing for exams that test neural network computation skills.

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