Single Perceptron

Duration: 12 min

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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: भाषा, लिपि, ध्वनि, वर्ण, अक्षर, स्वर, व्यंजन, मात्रा और संयुक्ताक्षर, वर्तनी, वर्ण-विन्यास, अनुस्वार, अनुनासिक, चंद्रबिंदु और ‘र’ के रूप, शब्द, पद और शब्द-भेद, संज्ञा और सर्वनाम, विशेषण और क्रिया, लिंग, वचन, पुरुष और कारक, काल, वाच्य, वाक्य और वाक्य-भेद, वर्णमाला, समास, संधि, विलोम शब्द, पर्यायवाची शब्द, सामान्य अशुद्धियाँ, अनेक शब्दों के लिए एक शब्द, मुहावरे और लोकोक्तियाँ, अपठित गद्यांश और अपठित पद्यांश, Multi-Topic Content (Cross-Concept Material), शब्द-भंडार, वर्ण-विचार, हिन्दी की ध्वनि और स्वनिम व्यवस्था, प्राचीन और मध्यकालीन भारतीय आर्यभाषाएँ, काव्य के लक्षण, हेतु और प्रयोजन, अशुद्धि-शोधन, शब्द-रचना, शब्द-भेद, हिन्दी की रूप रचना, रूप-रचना, 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, 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: 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
  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: Permutation and Combination, 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, Emerging Tech Trends
  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 provides a comprehensive introduction to single perceptrons, covering their history, structure, mathematical formulation, and limitations. It begins with the definition of a perceptron as a single-layer feed-forward neural network introduced by Frank Rosenblatt in 1957. The instructor explains the basic neuron model, including inputs, weights, and an activation function that produces a binary output. The lecture progresses to the mathematical representation of the perceptron, detailing the net input calculation and the use of step or signum activation functions. A key concept discussed is the limitation of single-layer perceptrons to learning only linearly separable patterns, illustrated with a gender classification example based on height and weight. Finally, the video introduces the vectorization of the perceptron equation, the role of bias, and the fundamental learning rules used to update weights and bias for classification tasks.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a title slide reading "Single Perceptrons". The instructor introduces the topic as single-layer feed-forward neural networks. She states that the perceptron was introduced by Frank Rosenblatt in 1957 and proposed a learning rule based on the original MCP neuron. She draws a diagram of a neuron with three inputs and an output, labeling the summation as $\Sigma w_i x_i$. She explains that the output is determined by an activation function, which compares the sum to a threshold. If the sum is greater than the threshold, the output is 1; otherwise, it is 0. This establishes the basic binary classification mechanism of the perceptron.

  2. 2:00 – 5:00 02:00-05:00

    The instructor defines a perceptron as an algorithm for supervised learning of binary classifiers. She notes that this algorithm enables neurons to learn and processes elements in the training set one at a time. She distinguishes between two types of perceptrons: single-layer and multilayer. A critical limitation is highlighted: single-layer perceptrons can learn only linearly separable patterns. To illustrate binary classification, she writes "spam mail" with a decision of "Yes" or "No". She draws a step function graph to visualize the output transition from 0 to 1. The activation function is identified as typically being either a signum function $sgn(x)$ or a step function $step(x)$.

  3. 5:00 – 10:00 05:00-10:00

    A diagram of a single-layer neural network is shown with inputs $x_1, x_2, ..., x_n$ and weights $w_1, w_2, ..., w_n$. The instructor writes the net input formula $in_j = \sum_{i=0}^{n} w_{i,j} a_i$. She explains that the activation function determines the final output, which can be represented as "1" or "0", or "1" and "-1". A "Simple Linearly Separable classification problem" is introduced using height and weight to classify gender. A scatter plot shows data points for males (purple dots) and females (red crosses) on axes ranging from 4.6 to 5.8 for height and 40 to 80 for weight. A decision boundary line is drawn to separate the classes. The general equation of a straight line $ax + by + c = 0$ is written. The instructor explains that substituting a point on the line gives 0, a point above gives a value greater than 0, and a point below gives a value less than 0.

  4. 10:00 – 12:00 10:00-12:00

    The lecture focuses on vectorizing the perceptron equation for mathematical convenience. The line equation is rephrased as $w_0 x_0 + w_1 x_1 + w_2 x_2 = 0$ where $x_0 = 1$ represents the bias. The instructor explains that bias allows the line to move up or down to fit the data better. She defines vector $X$ as $[x_0, x_1, x_2]^T$ and vector $W$ as $[w_0, w_1, w_2]^T$. The classification logic is formalized: if a sample is Male ($Y=1$), then $X \cdot W > 0$; if a sample is Female ($Y=-1$), then $X \cdot W < 0$. The session concludes by introducing "Learning Rules" as a method to improve the Artificial Neural Network's performance by updating weights and bias levels when the network simulates in a specific data environment.

The video provides a structured introduction to single perceptrons, starting with their historical context and basic structure. The instructor explains that perceptrons are single-layer feed-forward networks capable of binary classification, introduced by Frank Rosenblatt in 1957. The core mechanism involves summing weighted inputs and applying an activation function, typically a step or signum function, to produce a binary output. A key limitation discussed is the inability of single-layer perceptrons to handle non-linearly separable data, demonstrated through a gender classification example using height and weight. The mathematical formulation is then detailed, showing how the decision boundary is represented by a linear equation. The concept of bias is introduced to shift this boundary, and the equation is vectorized to simplify the representation of inputs and weights. Finally, the video sets the stage for learning rules, which are essential for training the network by adjusting weights and bias to minimize classification errors.

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