Optimizers in Neural Networks | Adagrad | RMSprop | ADAM | Deep Learning basics

In deep learning, choosing the right learning rate is crucial. If it's too high, we might overshoot the optimal solution. If it's too low, training becomes painfully slow. An adaptive learning rate can help tackle these issues effectively. Previous tutorial - https://youtu.be/V39sJEANQDo Deep Learning Playlist - https://tinyurl.com/4auxcm66 🛠 Dense vs Sparse Features: Dense Features: Get updated frequently. 🔄 Sparse Features: Get updated rarely. ⏳ This imbalance between dense vs sparse features can lead to inefficient training with traditional Gradient Descent (GD), GD with Momentum, and even Nesterov Accelerated Gradient (NAG). ✨ AdaGrad: Benefit: Adjusts the learning rate for each parameter individually based on the frequency of updates. Great start! 🌱 Drawback: For dense features, the gradient can become too small, causing the learning rate to shrink excessively before reaching the optimal solution. 😔 How it works: AdaGrad adapts the learning rate for each parameter by considering all past gradients. This means that frequently updated parameters get a smaller learning rate, while rarely updated parameters get a larger learning rate. This helps to balance the updates across all parameters, making it particularly effective for sparse features. 🔄 RMSprop: Solution: Uses an exponentially weighted moving average of squared gradients to adapt the learning rate. Advantage: Prevents the learning rate from decaying too quickly, keeping the training process smooth and efficient. 👍 How it works: RMSprop improves on AdaGrad by using a moving average of the squared gradients. This means it gives more weight to recent gradients, which prevents the learning rate from shrinking too much over time. This makes RMSprop more robust, ensuring that the learning process remains stable and efficient, even for dense features. 🔥 ADAM (Adaptive Moment Estimation): Combination: Merges the benefits of both Momentum and RMSprop. Momentum: Helps accelerate updates for relevant features by considering the past gradients. 🚀 RMSprop: Adapts the learning rate based on the squared gradients. 📈 How it works: ADAM combines the best aspects of Momentum and RMSprop. It maintains an exponentially decaying average of past gradients (like Momentum) and the squared gradients (like RMSprop). This dual approach allows ADAM to adapt the learning rates for each parameter more effectively, providing a balance between stability and performance. ADAM is particularly good at handling noisy data and sparse gradients, making it a popular choice for a wide range of deep learning tasks.