Tutorial-45:Adam optimizer explained in detail | Simplified | Deep Learning скачать в хорошем качестве

Tutorial-45:Adam optimizer explained in detail | Simplified | Deep Learning

Connect with us on Social Media! 📸 Instagram: https://www.instagram.com/algorithm_a... 🧵 Threads: https://www.threads.net/@algorithm_av... 📘 Facebook:   / algorithmavenue7   🎮 Discord:   / discord   In this video, we break down Adam (Adaptive Moment Estimation) — the most widely used optimization algorithm in deep learning. 🚀 You’ll learn: ✅ Why Adam is preferred over RMSProp and SGD ✅ The intuition behind momentum (1st moment) and adaptive learning rates (2nd moment) ✅ The full update rule explained step by step ✅ The role of hyperparameters like lr, beta1, beta2, and eps ✅ Bias correction and why it’s important ✅ Practical examples of Adam in PyTorch Whether you’re new to machine learning or brushing up on deep learning fundamentals, this tutorial will give you the complete picture of Adam Optimizer. 👉 If you found this useful, don’t forget to Like , Share , and Subscribe for more awesome content! #adamoptimizer #adam #deeplearning #machinelearning #ai #artificialintelligence #neuralnetworks #gradientdescent #optimizationalgorithm #adaptiveoptimizers #pytorch #tensorflow #keras #backpropagation #aiexplained #deeplearningforbeginners #mlforbeginners #neuralnetworktraining #optimizersindeeplearning #adaptivelearningrate #sgd #rmsprop #adagrad #optimizerscomparison #aicommunity #airesearch #datascience #computervision #nlp #mlengineer