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Learn deep learning fundamentals through this comprehensive Hindi-language course covering artificial intelligence, machine learning, and neural networks. Master the distinctions between AI, ML, and deep learning while exploring artificial neural networks, activation functions including linear, Heaviside step, sigmoid, tanh, and ReLU functions. Understand feed forward and feedback networks, then dive deep into the back propagation algorithm through multiple detailed parts with solved examples. Explore recurrent neural networks (RNN) across two comprehensive sections and discover the history of the first artificial neuron. Gain essential data science knowledge including the data science lifecycle, sampling techniques (random, systematic, and stratified), point and interval estimates, confidence intervals, and margin of error calculations. Master key performance metrics like Mean Squared Error (MSE) and R-squared through both theoretical explanations and numerical examples. Learn practical concepts such as random state in machine learning, flattening in neural networks, and conclude with an introduction to Generative Adversarial Networks (GANs) for advanced deep learning applications.
