Q1. What is Tensorflow?
TensorFlow is a machine learning library created by the Brain Team of Google and made open source in 2015.
Basically, Tensorflow is a low-level toolkit for doing complicated math and it offers the users customizability to build experimental learning architectures, to work with them and to turn them into running software.
Q2. What does the latest release of TensorFlow have to offer?
The latest release of TensorFlow is 1.7.0 and is available on www.tensorflow.org. It has been designed with deep learning in mind but applicable to a much wider range of problems.
Q3. What are Tensors?
Tensors are higher dimensional arrays which are used in computer programming to represent a multitude of data in the form of numbers.
There are other n-d array libraries available on the internet like Numpy but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives.
Q4. What is a TensorBoard?
TensorBoard, a suit of visualizing tools, is an easy solution to Tensorflow offered by the creators that lets you visualize the graphs, plot quantitative metrics about the graph with additional data like images to pass through it.
Q5. What are the features of TensorFlow?
Tensorflow has APIs for Matlab, and C++ and has a wide language support.
With each day passing by, researchers working on making it more better and recently in the latest Tensorflow Summit, tensorflow.js, a javascript library for training and deploying machine learning models has been introduced.
Q6. List a few advantages of TensorFlow?
It has platform flexibility
It is easily trainable on CPU as well as GPU for distributed computing.
TensorFlow has auto differentiation capabilities
It has advanced support for threads, asynchronous computation, and queue es.
It is a customizable and open source.
Q7. List a few limitations of Tensorflow.
Has GPU memory conflicts with Theano if imported in the same scope.
No support for OpenCL
Requires prior knowledge of advanced calculus and linear algebra along with a pretty good understanding of machine learning.
Q8. What are TensorFlow servables?
These are the central rudimentary units in TensorFlow Serving. Objects that clients use to perform the computation are called Servables.
The size of a servable is flexible. A single servable might include anything from a lookup table to a single model to a tuple of inference models.
Q9. What do the TensorFlow managers do?
Tensorflow Managers handle the full lifecycle of Servables, including:
Loading Servables
Serving Servables
Unloading Servables
Q10. What are TensorFlow loaders?
Tensorflow Loaders are used for adding algorithms and data backends one of which is tensorflow itself. For example, a loader can be implemented to load, access and unload a new type of servable machine learning model.
TensorFlow Interview Questions and Answers for Freshers. Q- 1,2,3,4,5,6,7,8
TensorFlow Interview Questions and Answers for Experience. Q- 9, 10
Q11. What is deep speech?
Deep Speech developed by Mozilla is a TesnsorFlow implementation motivated by Baidu’s Deep Speech architecture.
Q12.What do you mean by sources in TensorFlow?
Sources are in simple terms, modules that find and provide servables. Each Source provides zero or more servable streams. One Loader is supplied for each servable version it makes available to be loaded.
Q13. How does TensorFlow make use of the python API?
Python is the most recognisable and “the main” language when it comes to TensorFlow and its development. The first language supported by TensorFlow and still supports most of the features. It seems as TensorFlow’s functionality first define in Python and then moved to C++.
Q14. What are the APIs inside the TensorFlow project?
The API’s inside TensorFlow are still Python-based and they have low-level options for its users such as tf.manual or tf.nnrelu which use to build neural network architecture. These APIs also use to aid designing deep neural network having higher levels of abstraction.
Q15. What are the APIs outside TensorFlow project?
TFLearn: This API shouldn’t be seen as TF Learn, which is TensorFlow’s tf.contrib.learn. It is a separate Python package.
TensorLayer: It comes as a separate package and is different from what TensorFlow’s layers API has in its bag.
Pretty Tensor: It is actually a Google project which offers a fluent interface with chaining.
Sonnet: It is a project of Google’s DeepMind which features a modular approach.
Q16. How does TensorFlow use the C++ API?.
The runtime of TensorFlow is written in C++ and mostly C++ is connected to TensorFlow through header files in tensorflow/cc. C++ API still is in experimental stages of development but Google is committed to working with C++.
Q17. In TensorFlow, what exactly Bias and Variance are? Do you find any similarity between them?
In the learning algorithms, Biases can consider as errors which can result in failure of entire model and can alter the accuracy. Some experts believe these errors are essential to enable leaner’s gain knowledge from a training point of view.
Q18. Can TensorFlow be deployed in container software?
Tensorflow can also use with containerization tools such as docker, for instance, it could use to deploy a sentiment analysis model which uses character level ConvNet networks for text classification.
Q19. What exactly Neural Networks are? What are the types of same you are familiar with?
Neural networks as the name suggests are a network of elemental processing entities that together make a complex form. There can be Artificial Neural Networks and Biological Neural Networks. The use of artificial neural networks is more common as they try to imitate the mechanics of the human brain.
Q20. What are the general advantages of using the Artifical Neural Networks?
They use to find answers to complex problems in a stepwise manner. All the information that a network can receive can easily be in any format. They also make use of real-time operations along with a good tolerance capability.
TensorFlow Interview Questions and Answers for Freshers. Q- 11,12,14,15,16,19
TensorFlow Interview Questions and Answers for Experience. Q- 13,17,18,20
Q21. What exactly do you know about Recall and Precision?
The other name of Recall is the true positive rate. It is the overall figure of positiveness a model can generally claim. The predictive value which is generally positive in nature is Precision.
The difference between the true positive rate and claimed positive rate can be defined with the help of both these options.
Q22. Name some products built using TensorFlow?
TensorFlow built the following products:
TensorFlow Interview Questions
TensorFlow Interview Questions and Answers- Products Built Using TensorFlow
Teachable Machine
Nsynth
Handwriting
Giorgio Camthat
Q23. What are some advantages of TensorFlow over other libraries?
Debugging facility, scalability, visualization of data, pipelining and many more.
Q24. How can you make sure that overfitting situation is not arriving with a model you are using?
Users need to make sure that their model is simple and not have any complex statement. Variance takes into the account and the noise eliminates from the model data. Techniques like k-fold and LASSO can also help.
Q25. What exactly do you know about a ROC curve and its working?
ROC or region of convergence used to reflect data rates which classify as true positive and false positive. Represented in the form of graphs, it can use as a proximity to swap operations related to different algorithms.
Q26. In the machine learning context, how useful and reliable Bayes’ theorem is?
Baye’s theorem is useful for determining the probability of an event, obtained by dividing the actual positive rate by the false positive rate. Some of the very complex questions and challenges can solve using a simple approach and eliminated with the help of this theorem.
Q27. What difference do you find in Type I and Type II errors?
Type I error means a false positive value while Type II error means a false negative.
Q28. What would be your strategy to handle a situation indicating an imbalanced dataset?
This usually occurs when a vast set of data keep in a single class. Sampling the dataset again is one of the possible solutions and the other one being the migration of data to parallel classes. The dataset should not be damaged.
Q29. What do you know about supervised and unsupervised machine learning?
Supervised learning consists of labelled data which is not necessarily present in unsupervised learning.
Q30. In machine learning based on TensorFlow, what is more important among the performance or the accuracy of a model?
It depends on the overall experience. Both of them have an equal weightage although accuracy is most important in most of the models.
