Complete Deep Learning in 5 Hours Explained in Hindi

Learn comprehensive deep learning concepts through this Hindi-language video tutorial that covers the complete spectrum from foundational principles to advanced architectures in under 5 hours. Begin with an introduction to deep learning that traces the evolution from artificial intelligence to machine learning to deep learning, explores the differences between machine learning and deep learning, compares biological and artificial neural networks, examines real-world applications, and introduces essential Python frameworks. Master the fundamentals of neural networks including the perceptron model, multi-layer perceptrons, various activation functions, gradient descent and backpropagation algorithms, loss functions, and optimization techniques such as SGD, MiniBatch SGD, RMSProp, and Adam. Dive into convolutional neural networks (CNNs) covering basic concepts, convolution operations, filters and feature maps, pooling layers, dropout techniques, batch normalization, regularization methods, and famous CNN architectures. Explore recurrent neural networks (RNNs) including basic RNN concepts, sequential data and time-series modeling, solutions to vanishing and exploding gradient problems, Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and practical applications. Advance to sophisticated deep learning architectures including autoencoders, Generative Adversarial Networks (GANs), transformer basics with attention mechanisms, and reinforcement learning with Deep Q-Learning, with free downloadable notes provided to supplement the learning experience.

Syllabus

0:00 Introduction to Deep Learning [History of AI → ML → DL, Difference between Machine Learning & Deep Learning, Biological vs. Artificial Neural Networks, Applications of Deep Learning, Basics of Python Frameworks]
35:30 Fundamentals of Neural Networks [Perceptron model, Multi-Layer Perceptron, Activation Functions, Gradient Descent & Backpropagation, Loss Functions, Optimization algorithms SGD, MiniBatch SGD, RMSProp, Adam]
2:22:04 Convolutional Neural Networks [Basics of CNN, Convolution Operation, Filters, Feature Maps,
3:23:06 Recurrent Neural Networks [Basics of RNN, Sequential Data & Time-Series Modeling, Vanishing & Exploding Gradients problem, Long Short-Term Memory, GRU, Applications of RNN, LSTM, and GRU]
4:06:28 Advanced Deep Learning Architectures [Autoencoders, Generative Adversarial Networks GANs, Basics of T ransformers & Attention Mechanism, Reinforcement Learning & Deep Q-Learning]