The global job market is increasingly becoming AI-driven. Consequently, mere theoretical knowledge is not enough, and professionals have to showcase their practical machine learning skills to be recognized as deserving candidates for recruiters.

If you’re wondering how to make yourself credible in the AI job market with a resume that outshines others, start with the coveted TensorFlow certification.

Specifically speaking, the TensorFlow Developer Certificate Program is a foundational course for professionals, students, and data scientists who want to hone their practical machine learning skills by building and training models with TensorFlow.

So, here’s a guide with a brief introduction to TensorFlow and the top 10 TensorFlow questions and answers that you must know to ace the interviews!

A Brief Introduction To TensorFlow

If you are new to TensorFlow, here’s a brief introduction to help you get the hang of it before you sign up for a full-fledged TensorFlow training.

TensorFlow is an open-source platform for machine learning. The platform is end-to-end, with a flexible and comprehensive array of libraries, tools, and community resources to ease the process of building and deploying machine learning-powered applications.

Developed by Google, TensorFlow has a host of helpful machine learning and deep learning algorithms and models. It provides a Python-based frontend API for building applications and executes the same in high-performance C++. 

Besides the C++ and Python frontends, the Layers API offers a simple interface for the most frequently used layers in deep learning models. In addition, there are the higher-level APIs (Keras and Estimator) for training and evaluating models. Canned Estimators come with several ready-to-use models in a box. Being a cross-platform, TensorFlow runs on CPUs, GPUs, TPUs (Tensor Processing Units), and mobile and embedded platforms.

Top 10 TensorFlow Interview Questions

Once you’ve obtained your TensorFlow certification, what’s next?

Understanding the concept of TensorFlow is one thing and proving your knowledge is another. No doubt, a TensorFlow course includes live projects on TensorFlow that will equip you with all the learning you need to build and deploy machine learning models. Still, the real test of your knowledge is when an interviewer will assess your skills and validate your performance. 

So, if you want to brush up on your TensorFlow concepts and prepare for the interview process, here is a set of ten questions and their answers for TensorFlow interviews.

1. What are tensors in TensorFlow?

Tensors represent vectors or multi-dimensional arrays. It constitutes a combination of digits to portray data in the coded form. A tensor has three properties: name, shape, and dtype. All values in a tensor have a known shape and hold identical data types. The shape of the data defines the dimensionality of the array. All the operations in TensorFlow are conducted inside graphs, the operations are interconnected, and each operation is called an op node. The edge of the nodes represents the tensor, and while the graph does not display any values, tensors are a means to populate operations with data.

2. What are the different types of tensors used for creating neural network models?

There are three types of tensors: constant tensors, variable tensors, and placeholder tensors.

• Constant tensors represent nodes that store one value and which do not change with time. 
• Variable tensors represent the different values at each run and give their current value as output when executed. 
• Placeholder tensors are used for assigning data at a later time. They do not require any initial value, except a data type and a tensor shape.

3. What is a TensorBoard?

TensorBoard is a visualization toolkit for understanding and inspecting TensorFlow runs and graphs. It is a tool that helps in debugging, dynamic runtime, and eager execution. It supports five visualization techniques: graphs, histograms, audio, images, and scalars.

4. Which languages are supported in TensorFlow?

While the runtime is written in C++, the frontend can be implemented using Python, C, C++, Go, R, Java, etc.

5. What are the components of the TensorFlow architecture?

The TensorFlow architecture has three working components:

• Data preprocessing
• Model building
• Training and estimating the model

6. What are the ways to load data into TensorFlow?

The first step of training a machine learning algorithm is to load the data into TensorFlow. There are two ways to do it:

• Loading data into the memory as a single array
• Using TensorFlow’s built-in APIs, which simplifies the tasks of data loading, performing operations, and feeding the machine learning algorithm

7. What are estimators?

Estimators are high-level APIs that provide methods for training the model, judging the model’s accuracy, and generating predictions. They are of two types – pre-made and custom. Estimator-based models can run on local hosts and distributed multi-server environments without causing any change to the model. 

8. What are the most common steps of TensorFlow algorithms?

The most common steps to TensorFlow algorithms are:

• Importing and generating data
• Feeding the data through the computational graph
• Evaluating output on the loss function
• Using backpropagation to modify variables
• Repeating until a stopping condition

9. What is the need for normalization in TensorFlow, and how is it done?

Since the TensorFlow system requires all inputs in a dimension and the dataset we work with does not have significant values, data normalization is needed. The syntax for batch normalization of data is:

data =  tf.nn.batch_norm_with_global_normalization()

10. What are some limitations of TensorFlow?

Some of the limitations of TensorFlow are as follows: 

• Using TensorFlow requires a sound understanding of the fundamentals of machine learning as well as the knowledge of linear algebra and advanced calculus.
• It does not support Open Computing Language.
• GPU memory clashes with Theano are common when imported in the same scope

Conclusion

While a TensorFlow certification validates your practical machine learning skills, mastering the interviews requires an in-depth study of the theoretical aspects of machine learning and TensorFlow. 

TensorFlow has a slew of libraries and tools to facilitate the building and training of models. As an interview, you will be expected to have thorough knowledge about TensorFlow and its various features and components. So, boost your confidence for the D-day and include these questions in your preparation plan for the interviews.