Answer / Gulshan Jahan

Gradient Descent is an optimization algorithm used in machine learning to find the minimum value of a function by iteratively moving in the direction of steepest descent. It works by adjusting the parameters of a model such that the cost function or error is minimized. The update rule for Gradient Descent is: w = w - α * gradient(w). Here, w represents the weights or parameters of the model, α is the learning rate which determines the step size, and gradient(w) is the derivative of the cost function with respect to the weights.

Is 8gb ram enough?

1 Answers  

---

Is 16gb of ram a lot?

1 Answers  

---

Explain the different layers of cnn.

1 Answers  

---

What is deep learning and how does it relate to ai?

1 Answers  

---

What is meant by deep learning?

1 Answers  

---

What is Gradient Descent?

1 Answers  

---

What is Deep Learning?

1 Answers  

---

What is matrix element-wise multiplication?

1 Answers  

---

What is the difference between Epoch, Batch and Iteration in Deep Learning?

1 Answers  

---

What is model capacity?

1 Answers  

---

What do you understand by perceptron? Also, explain its type.

1 Answers  

---

Please explain what is deep learning, and how does it contrast with other machine learning algorithms?

1 Answers  

---