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**Q. In TensorFlow, what exactly Bias and Variance are? Do you find any similarity of difference between them?**

In the learning algorithms, Bias are generally considered as errors that declare their presence due to overly assumptions. These can sometimes result in failure of entire model and can largely affect the accuracy also in several cases. Some experts believe these errors are essential to enable leaner’s gain knowledge from a training point of view. On the other side, Variance is another problem that comes when the learning algorithm is quite complex. Therefore a limit is to be imposed on this.

**Q. When will you find overfit condition of your model in TensorFlow?**

There are variations in the training data or data that needs to be verified through TensorFlow. If the variations are very large in the data, probably it could lead to this problem. The best possible solution is to remove the noise from the available data upto the possible extent.

**Q. What exactly do you know about Bias-Variance decomposition?**

It is generally used to decompose problems such as errors that occur during learning in different algorithms. Bias keeps reducing if the data is to be made complex. Trading off the Variance and Bias are very essential to get results that are totally free from errors.

**Q. Are you familiar with a Fourier Transform?**

Well, it is a mathematical concept. Basically, it is a generic method that is used for decomposing the generic functions into a superposition of other functions that are generally symmetric in nature. When it comes to finding the speeds of cycles and amplitudes, they are widely adopted in the machine learning. Fourier Transformation is also used for solving some of the very complex problems of mathematics.

**Q. How k-means clustering is different from KNN?**

K-means clustering is basically an unsupervised clustering algorithm. It is capable to tolerate some minor errors. On the other side, the KNN is structured clustering algorithm. For reliable operations, it should be accurate and reliable. The mechanism for both seems very similar at the first glance but users need to label the data in the KNN which is not required in the k-means clustering.

**Q. What exactly do you know about a ROC curve and its working?**

IT is basically used to reflect something very important regarding the rates which are classified as true positive rates and false positive rates. It represents all the information in the form of graphs. Basically, it can be used as a proxy for the trade-off operations related to different algorithms.

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**Q. What exactly Neural Networks are? What are the types of same you are familiar with?**

It is basically a connection of processing elements which can be very large or very small depending on the application it is deployed for. These elements are called as neurons and generally two types of networks can be seen in this category. They are Artificial Neural Networks and Biological Neural Networks. The use of artificial neural networks is more common and generally they are considered for creating machines which are equally powerful to human brains.

**Q. What are the general advantages of using the Artifical Neural Networks?**

They provide complete information on how to find solutions to complex problems in a stepwise manner. All the information that a network receive can easily be represented in any format. Artificial neural networks also make sure of real-time operations. In addition to this, they have excellent fault tolerance capability.

**Q. Compare general computers with artificial neural networks?**

The artificial neural network’s information source is examples that are common in general computers. It is very necessary to choose the examples carefully as they need to be given as input to the artificial neural network. Predicting the artificial neural network outcome is not an easy job but it can be trusted for its accuracy. However, the outcomes of general computers are already well-defined and can easily be predicted.

**Q. What exactly do you know about Recall and Precision**

The other name of Recall is true positive rate. Actually, it is the overall figure of positiveness a model generally claim. Precision is generally regarded as the predictive value which is positive in nature. The difference between the true positive rate an claimed positive rate can be defined with the help of both these options.

**Q. In the machine learning context, how useful and reliable Bayes’ theorem is according to you?**

This theorem defines the probability of any event in machine learning. It represents a fixed value which is actually a mathematical calculation. This value is generally obtained by dividing the true positive rate divided by the false positive rate. In machine learning, some of the very complex problems and challenges can easily be solved and eliminated with the help of this theorem. Most of the time results provided by it are highly accurate and can easily be trusted.

**Q. What difference do you find in Type I and Type II errors?**

Type I error is a false positive value. On the other side, Type II error is a false negative value. Type I error generally represent that something has happened when actually it doesn’t while Type II error is to representing the machine that nothing is wrong when actually something is not good.

**Q. In TensorFlow, what exactly discriminative and generative method?**

All the distinction among the different types of data can simply be learned with the help of discriminative method. On the other side, generative model is used for understanding a specific format of same. The tasks that can also be handled with both these approaches need to be classified in a well-defined order first.

**Q. What exactly do you mean by Pruning in a decision tree?**

Many times it has been seen that in a cluster the predictive powers are very weak and they need to be removed. This is to cut down the overall complexity of the model or to increase the accuracy. This condition is generally considered as Purning. There is a strict limit on purning otherwise it makes the model totally useless. The latest version available in the machine learning algorithms is Reduced error pruning.

**Q. What would be your strategy to handle a situation indicating an imbalanced dataset?**

This situation occurs when a majority of data that is under a specific use is kept in one class only. Resampling the dataset is the best possible solution for the users. Migration of data to the parallel classes can also overcome the problem to a great extent. Users also need to make sure that a dataset is not damaged.

**Q. What is the application of Naïve Bayes Naïve in machine learning?**

It is basically a sub-algorithm of a sub-module that defines the conditional probabilities of different components. The final results can be integrated with other possible outcomes to predict the final outcomes. It can also overcome a lot of problems which are related to the unstructured data.

**Q. How is it possible to evaluate a logistic regression model for a user?**

It is necessary for the users to fully understand the typical goals related to the concepts are. Some use cases are also to be considered for this approach.

**Q. What do you know about supervised and unsupervised machine learning?**

One of the prime needs in the supervised learning is the labeled data which is not always necessary to be present in unsupervised learning. Data labeling is important to enable the groups handle all the things in the right manner. On the other side, it is possible for the users to use data in unsupervised learning but labeling the same is not always necessary.

**Q. Name the two common regularizations in a machine learning approach and explain the significance of them?**

These are L1 and L2 regularization. Both these have their own well-defined functions. L1 contains multiple variables that are in binary values. L2 regularization are meant for error handling and both of them largely depends on the Gaussian concept.

**Q. In machine learning based on TensorFlow, what is more important you among the performance or the accuracy of a model?**

The answer to this question depends on your overall experience. Although both are important accuracy is more important in the majority of tasks. It would be good for you to boost your knowledge on nuances of machine learning to have a better reply to this question if it is asked to you in an interview.

**Q. What exactly F1 score is in machine learning? **

It is basically a score that gives clear information regarding the overall performance of a model that is utilized for any task. The score generally varies between the two fixed values that are generally 0 and 1. The latter is regarded as the best score while 0 represents the worst performance.

**Q. Name the algorithm you are properly familiar with and explain everything you know about it in one minute?**

Such a question basically text your information representing skills for the tasks that are technical and complex. Make sure you summarize the text properly and give the answer in a defined format. You can go with any algorithm that you have studied or practiced properly.

**Q. What exactly do you know about Deep learning?**

It is basically related to neural networks and is generally considered as a subset of machine learning. When it comes to implementing some important principles that are related to the back propagation, this concept is applied. It is an unsupervised learning algorithm that is used for data understanding and to use neural nets properly.

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**Q. On a time series dataset, what type of validation technique would you prefer?**

This dataset is not a randomly distributed data and thus the standard techniques such as k-folds cannot be applied. Therefore a pattern based technique would be useful here and this is because it makes sure that all the sub-tasks flow in a well-defined sequence. There are no chances of any errors that can be considered as a chronological order that creates issues related to the functionality of the model.

**Q. In which situation the ensemble approach is useful?**

In a model, there is a need to use multiple learning algorithms. This situation needs ensemble approach. In addition to this, there can also be a need to combine an applied part of learning algorithms for the optimization or the predictive performance. One of the primary aim to use this approach is to impose a limit on the overfitting.

**Q. 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 is not having any complex statement. All the variance should be taken into the account and the noise should be eliminated from the model data. Cross-validation technique like k-fold is another useful method that is helpful in this matter. LASSO technique is another possible solution to this issue.

**Q. What exactly do you know about Kernel Trick?**

All the kernel functions are involved in this trick basically. These tricks are useful to perform some advanced calculations. It is possible for the users to express these functions in terms of products. Also, different algorithms can be made run effectively. the good thing is this can be done even if the dimensional data is low.

**Q. What if a file is corrupted or missing in a dataset?**

It is possible to replace them with other values that run parallel to them. Dropna and isnull are the two methods that are useful in this matter. In some special cases, it is even possible to replace them with desired values and have an error-free outcome.

**Q. What differences will you find in an array and a linked list?**

Collection of objects in a well-defined order is generally considered as an array. On the other side, a linked list is also a set of objects but they are not always necessary to be well-defined or remain in a sequence. Also, they have a pointer which is missing in case of an array.

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