Python Tutorial : Basic operations

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-tensorflow-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video, we'll talk about basic operations in TensorFlow. TensorFlow has a model of computation that revolves around the use of graphs. A TensorFlow graph contains edges and nodes, where the edges are tensors and the nodes are operations. In the graph shown, which was drawn using TensorFlow, the const operations define 2 by 2 constant tensors. Two tensors are summed using the add operation. Another two tensors are then summed using the add operation. Finally, the resulting matrices are multiplied together with the matmul operation. Let's start with the addition operator. We will first import the constant and add operations. We may now use constant to define 0-dimensional, 1-dimensional, and 2-dimensional tensors. Finally, let's add them together using the operation for tensor addition. Note that we can perform scalar addition with A0 and B0, vector addition with A1 and B1, and matrix addition with A2 and B2. The add operation performs element-wise addition with two tensors. Each pair of tensors added must have the same shape. Element-wise addition of the scalars 1 and 2 yields the scalar 3. Element-wise addition of the vectors 1,2 and 3,4 yields the vector 4,6. Element-wise addition of the matrices 1,2,3,4 and 5,6,7,8 yields the matrix 6,8,10,12. Furthermore, the add operator is overloaded, which means that we can also perform addition using the plus symbol. We will consider both element-wise and matrix multiplication. For element-wise multiplication, which is performed with the multiply operation, the tensors involved must have the same shape. For instance, you may want to multiply the vector 1,2,3 by 3,4,5 or 1,2 by 3,4. For matrix multiplication, you use the matmul operator. Note that performing matmul(A,B) requires t