TensorFlow Test 2
What is the purpose of TPU?
TPU stands for Tensor Processing Unit, which is an application-specific integrated circuit used to speed up AI calculations and algorithms. For the TensorFlow program, Google developed it specifically for neural network machine learning.
Which of the following is not a TensorFlow-based product?
Pandas is a Python module for data science, data analysis, and machine learning. It's based on Numpy, a multi-dimensional array support package.
In TensorFlow, what is the full form of XLA?
Accelerated Linear Algebra (XLA compiler) is another aspect that makes JAX so quick. Tensorflow has extensively employed XLA, a domain-specific linear algebra compiler.
What does TPU stand for?
TPU stands for Tensor Processing Unit, which is an application-specific integrated circuit used to speed up AI calculations and algorithms. For the TensorFlow program, Google developed it specifically for neural network machine learning. TensorFlow is a piece of software owned by Google. TPU was first used by Google in 2015, and it was made public in 2018.
What are the different types of Tensors?
Tensors are higher-dimensional versions of vectors and matrices. They represent base data-type n-dimensional arrays. A tensor's elements all have the same data type, which is always known. Tensors come in a variety of shapes and sizes, some of which are listed here.
tf.Variable <br? Except for tf.Variable, they're all immutable.
Tensors were discovered by who?
Gregorio Ricci-Curbastro was a mathematician from Italy. He is best known for inventing the tensor calculus.
In TensorFlow, which of the following dashboards?
TensorFlow has a large number of dashboards. These are necessary for a variety of tasks, including measurement and visualization as well as hyperparameter tweaking. TensorFlow graphs are visualized using them. Some of them include:
Which of the following statements about the backpropagation algorithm is correct?
Using a concept known as the delta rule or gradient descent, the Backpropagation algorithm hunts for the least value of the error function in weight space. The weights that minimize the error function are therefore regarded a learning problem solution.
What are the restrictions of the back propagation rule in general?
All of these are limitations of the backpropagation algorithm in general.