📊 TensorFlow Computational Graph 🔹 What is a Computational Graph? A computational graph is a directed graph used to represent mathematical computations. Nodes (vertices) → Operations (like addition, multiplication, activation) Edges → Tensors (data flowing between operations) 👉 Simple idea: Graph = Operations + Data flow 🔹 Components of the Graph 1. Nodes (Operations / Ops) Represent computations Examples: Matrix multiplication ( matmul ) Addition Activation functions (ReLU, Sigmoid) 👉 They take tensors as input and produce tensors as output 2. Edges (Tensors) Represent data flowing between nodes Carry: Inputs Intermediate results Outputs 👉 Think: Edges = Data pipeline 🔹 How to Build a Computational Graph Step 1: Define Operations Decide what computations you need (e.g., multiplication, loss calculation) Step 2: Create Tensors Inputs Model parameters Intermediate values Step 3: Connect Operations Link outputs of one operation to inputs of another 👉 This creates a graph structure 🔹 Example…