#4: Tensor in TensorFlow

The video shows how to create a tensor and perform basic operations in TensorFlow. Timeline (Python 3.7.12; TensorFlow 2.8) 00:00 - Begin 00:09 - Outline of video 00:22 - Open notebook in Google Colaboratory 01:28 - Create a Rank 0 tensor: tf.constant() 02:42 - Create a Numpy array 03:20 - Create a Rank 1 tensor 04:40 - Create a Rank 2 tensor 05:39 - Create a Rank 3 tensor 07:57 - Convert a tensor to Numpy array 09:40 - Basic math operations 10:00 - Basic math operations: Create two tensors: x, y 10:38 - Basic math operations: add: tf.add(x,y) 11:05 - Basic math operations: subtract: tf.subtract(x,y) 11:53 - Basic math operations: multiply: tf.multiply(x,y) 12:47 - Basic math operations: matmul: tf.matmul(x,y) 14:01 - Basic math operations: divide: divide(x,y) 14:35 - Basic math operations: add: x + y 14:42 - Basic math operations: subtract: x - y 14:55 - Basic math operations: divide: x / y 15:03 - Basic math operations: floor division: x // y 15:16 - Basic math operations: matmul: x @ y 15:19 - * * * Correction: Misspoke 'ampersand' for '@' instead of 'at sign' 15:55 - Operations: create a tensor 16:56 - Operations: max value: tf.reduce_max(x) 18:20 - Operations: max value by axis: tf.reduce_max(x, axis=0) 20:30 - Operations: get index for largest value: tf.argmax(x) 23:11 - Operations: softmax: tf.nn.softmax(x) 25:52 - Shape and rank: create a rank 4 tensor: tf.zeros() 29:32 - Shape and rank: x.dtype 29:57 - Shape and rank: x.ndim 30:21 - Shape and rank: x.shape 30:42 - Shape and rank: number of elements along axis=0: x.shape[0] 31:33 - Shape and rank: number of elements along last axis, axis=-1: x.shape[-1] 31:52 - Shape and rank: total number elements: tf.size(x) 32:33 - Ending notes