Optimizers in Deep Learning ⚡ SGD, Momentum & Adam Explained скачать в хорошем качестве

Optimizers in Deep Learning ⚡ SGD, Momentum & Adam Explained

Choosing the right optimizer can make or break your deep learning model 🚀. Optimizers control how your model learns, how fast it converges, and whether it generalizes well or not. In this video, we’ll break down the most important optimizers in deep learning — explained for both beginners and professionals. 🔑 What you’ll learn in this video: ✅ What an optimizer is and why it’s crucial for training neural networks ✅ Stochastic Gradient Descent (SGD) — simple, efficient, and often better for generalization ✅ Momentum — accelerates learning by smoothing gradients ✅ Adam — the most popular optimizer with adaptive learning rates & fast convergence ✅ Comparison of speed, convergence, memory, and generalization ✅ How to choose the right optimizer for your dataset and resources 💡 Key Insight: While Adam is often the default choice for speed and ease, SGD with momentum can achieve better generalization, especially in fine-tuning scenarios. The best optimizer depends on your problem, dataset size, and compute limits. 👉 If you find this helpful, don’t forget to 👍 like, 🔔 subscribe, and 💬 share your thoughts in the comments — I’d love to hear which optimizer you prefer! 🔖 Hashtags #optimizers #deeplearning #machinelearning #mlops #datascience #sgd #momentum #adam #neuralnetworks #mlworkflow #trainingtips