1⭐ Difference Between Supervised and Unsupervised Learning Machine Learning interviews often start with the question:“What is the...
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✅ 1. Print “Hello, World!” Output ✅ 2. Take input and display it Sample Output ✅ 3....
🧠 1. What is Deep Learning, and How Does It Differ from Traditional Machine Learning? Deep Learning...
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11 नवंबर 2025, नई दिल्ली – शाम 7 बजे का वक्त, रेड फोर्ट मेट्रो स्टेशन गेट नंबर...
Hello readers! In this blog post, I’ll walk you through completing and running a basic project using...
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1. Lack of Data Availability One of the most common challenges in data science and machine learning...
![Explain the Concept of Backpropagation and Its Significance Backpropagation (short for Backward Propagation of Error) is the key algorithm used to train neural networks. It calculates how much each weight in the network contributed to the error and then updates the weights to reduce that error. ✅ How Backpropagation Works (Step-by-Step) 1. Forward Pass The input data is passed through the network layer by layer to generate an output (prediction). 2. Loss Calculation The predicted output is compared with the actual label using a loss function (example: Cross-Entropy or MSE). This loss value tells how wrong the prediction was. 3. Backward Pass Using the Chain Rule of Calculus, backpropagation computes gradients (how sensitive the loss is to each weight). 4. Weight Update The weights are adjusted (usually using gradient descent or Adam optimizer) to minimize the loss. The network improves a little after each update. ⭐ Why Backpropagation is Important (Significance) Significance Explanation Optimizes network weights Helps the model learn better representations Enables end-to-end learning The model learns directly from raw inputs to outputs Foundation of Deep Learning Used in all modern frameworks (TensorFlow, PyTorch, Keras) Without backpropagation, training deep neural networks would not be possible. 🧑💻 Code Example (Keras automatically performs Backpropagation) model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) model.fit(x_train, y_train, epochs=5) # Backpropagation happens internally Here: optimizer handles weight updates loss is used to compute error model.fit() automatically performs forward pass + loss + backward pass + weight updates 🟢 In Short Backpropagation is the process of computing gradients and updating weights so that a neural network learns from its mistakes.](https://aryugyan.com/wp-content/uploads/2025/11/fdb2b915-c018-40ae-833d-96c09b243cdf-768x1089.jpg)
Explain the Concept of Backpropagation and Its Significance Backpropagation, often referred to as backward error propagation, is...
Statistics is broadly divided into two main categories: Descriptive Statistics and Inferential Statistics. Both are used to...
