Neural Networks : ONE-SHOT GATE DA | Concept to Combat Session 7 🔥 скачать в хорошем качестве

Neural Networks : ONE-SHOT GATE DA | Concept to Combat Session 7 🔥

Welcome to Session 7 of the Concept to Combat series! This is your ultimate one-stop resource for mastering some of the most high-weight and frequently tested topics in the GATE DA Machine Learning syllabus: Neural Networks. What you'll learn: In this session, we will do a recap of the concepts and then go in deep dive for extensive practice of GATE-level problems. We don’t just stop at the theory; we ensure you can apply every formula and geometric intuition to solve complex questions under pressure. 📘 Click the link below to download all session PDFs: https://drive.google.com/drive/folder... Join our Communities for Notes: Telegram: https://t.me/ManojGateDA Discord:   / discord   ML Module Schedule: Jan 20 – Feb 1. Subscribe to TAAI- Manoj Kumar to join the combat live! Jump to Topics: [00:00] Introduction & Setup: Initial check for audio/video quality and setting the stage for the final machine learning session in the series. [03:10] Perceptron Review: A quick recap of Perceptrons, including pre-activation (aggregation of inputs) and activation functions. [07:15] Sigmoid / Logistic Neuron: Introduction to the Sigmoid neuron, explaining how it differs from the basic Perceptron and its role in producing probability outputs. [10:41] Loss Functions & Optimization: Discussion on how to optimize parameters (weights and bias) using loss functions like Mean Squared Error and Cross-Entropy. [14:15] Partial Derivatives & Gradient Descent: Detailed derivation of partial derivatives for weight updates, focusing on the Chain Rule. [22:13] Complex Neural Networks: Moving from a single neuron to multi-layer networks, explaining how inputs propagate through hidden layers. [30:31] Parameter Counting: A guide on how to calculate the total number of trainable parameters (weights + biases) in fully connected and sparse networks. [40:04] Practice Problems (Parameter Counting): [41:50] Solving specific examples and identifying common "traps" in GATE-style questions regarding network connectivity. [45:01] Multi-Output Regression: How to handle multiple output variables (e.g., predicting both house price and rent) within the same network. [48:41] Multi-Class Classification: Transitioning from binary to multi-class problems using the Softmax Activation Function. [53:02] Cross-Entropy Loss: Explanation of why Cross-Entropy is preferred for classification and how it works with One-Hot encoding. [55:02] Activation Functions Gallery: [55:23] ReLU (Rectified Linear Unit): Definition and its derivative. [56:14] Leaky ReLU: Addressing the "dying ReLU" problem. [58:01] Tanh (Hyperbolic Tangent): Range and zero-centered nature. [59:03] Softmax Derivatives: [01:00:44] Deep dive into the mathematical derivation of Softmax, covering cases for the same and different indices. [01:02:08] Logic-Based Problems [01:03:53]Solving conceptual questions involving ReLU and Sigmoid combinations. [01:09:23] Forward & Backward Propagation: [01:14:08] A comprehensive walkthrough of how data moves forward to calculate error and how gradients move backward to update weights. [01:18:28] Deep Dive: Backpropagation Math: [01:21:57] Step-by-step calculation of derivatives in multi-layer paths. [01:42:01] Advanced Numerical Example [01:45:30]Solving a long-form numerical problem involving Sigmoid, ReLU, and Cross-Entropy loss. [02:00:40] GATE PYQ Analysis: [02:05:44]Solving the GATE DA 2024 2-mark question on neural networks in under 40 seconds [02:11:10] Epochs & Batch Processing: [02:12:27] Understanding how training works in mini-batches and what constitutes an "epoch". [02:15:23] Vanishing & Exploding Gradients. [02:28:29] Session Wrap-up #GATEDA #MachineLearning #LinearRegression #TAAI #ConceptToCombat GATE DA 2026 GATE Data Science AI GATE 2026 preparation GATE DA free course GATE DA practice questions GATE DA MCQ MSQ NAT GATE DA strategy GATE DA toppers preparation GATE DA machine learning Linear algebra for GATE DA Probability for GATE DA GATE DA exam tips GATE DA concepts and questions GATE DA next level preparation TAAI GATE Tomorrow's Architect of AI GATE DA rank oriented GATE DA 2026 full syllabus GATE DA beyond concepts To check out the course- https://www.taai.live/ Join our complete course to boost your GATE DA preparation. 🔹About the complete course: ✅ Complete syllabus coverage for GATE DA. ✅ Concept-focused lectures + regular doubt sessions. ✅ Subject-specific doubt channels. ✅ Expert guidance from our faculty (Manoj Sir, AIR-13 and Sahitya Sir). This course is for anyone who wishes to crack GATE DA, whether you're an absolute beginner or a pro. Join our community: 📌 Website: https://www.taai.live 📌 Telegram: https://t.me/Manoj_Gate_DSAI 📌 Discord:   / discord   📌LinkedIn: https://www.linkedin.com/company/taai... 🔔 Subscribe to our channel and hit the bell icon to get more updates.