Alan Turing, in 1947, said that “what we want is a machine that can learn from experience.” His words can be marked true today as we have Deep Learning — a new machine learning technique that imitates how we human beings gain knowledge and learn through examples.

Deep learning includes statistics and predictive modeling, and hence it is an essential element of data science. Deep learning makes the process faster and easier, especially when it comes to tasks related to data science like collecting, analyzing, interpreting, and everything that deals with working on a large amount of data.

Disbelief, a proprietary system built by Google in 2011 based on deep learning and neural networks, was further updated and modified in 2015, making it a better application-based library and was named as TensorFlow. Tensorflow, now open source, is a popularly used framework for deep learning as it provides developers with the easiest way to build and deploy applications.

Let us discuss the most frequently asked ten important Tensorflow interview questions along with the solutions. Let us first start right from scratch, the questions asked to the freshers, and then move towards the interview questions asked to an experienced or professional individual. Any individual with TensorFlow Certification will have an edge as they will be in a position to answer any tricky question asked by the interviewer.

**What is TensorFlow?**

The Brain team of Google created an open-source machine learning library in 2015 called TensorFlow. The word TensorFlow is the combination of two words, Tensor — representation of data for multi-dimensional array and Flow — the series of operations performed on the Tensor. It is a low-level toolkit used for performing complicated and complex mathematics. It also helps to make a running software out of experiential learning architectures made by the user.

**What is Tensor?**

Tensor is nothing but a mathematical object that generalizes matrices, scalar, and vectors as a multi-dimensional matrix ranging from *zero *to *N *dimensions and used in computer programming. A tensor represents a multitude of data in the form of numbers. It automatically computes the derivatives by providing methods to create tensor functions.

The graph can conduct all the operations in a tensor. The edge of the node is called a tensor. If you want to implement a tensor, an initial input of a feature vector is needed. In machine learning, objects are fed with the list of objects where these objects are called feature vectors.

**What are the types of Tensors?**

If you are planning to create a neural network model, there are three types of Tensors: Constant Tensor, Variable Tensor, and the PlaceHolder Tensor.

As the name suggests, a constant tensor is used as a continuous. It creates a node that then doesn’t change its value after taking one. The variable Tensor is nothing but a node that gives its value as an output.

The placeholder tensor is an essential one. It is used to assign the data at a later time. The value is to be fed at the node during the time of execution. They need data type and tensor shape, and hence it does not require any initial value.

**TensorFlow supports which client languages?**

TensorFlow supports many client languages, among which Python is the best. For C++, Java, and Go, few experimental interfaces are available. The open-source community supports the language bindings for multiple other languages such as C#, Scala, Julia, and Ruby.

**What are a few options to load the data in the TensorFlow?**

Before training a machine learning algorithm, the data is to be loaded in TensorFlow, i.e., loading the data in TensorFlow is an initial step. The data can be loaded in TensorFlow in two ways; the easiest method is to load data in memory, the data gets loaded in the memory as a single array. The second method is TensorFlow Data Pipeline. It is usually used to deal with a large dataset. The data is loaded, the operation is performed, and the machine learning algorithm is fed easily as the TensorFlow has built-in APIs.

**What is TensorFlow Servables and TensorFlow Serving?**

To perform the computation, the clients use some objects. These objects are the serves. The serves are flexible in size. A servable model may contain anything right from a single model, to a lookup table, and a tuple of inference models.

The TensorFlow Serving is made up of the production environment. It is used for machine learning models; it is also a flexible and high performing serving system. TensorFlow servings provide mind-blowing integration with TensorFlow models. Also, it can be easily extended to serve the other models and data whenever required. It can be learned in detail with the **TensorFlow Certification** courses.

**Mention some products built using TensorFlow**

Few products which are build using TensorFlow are Giorgio Cam, Teachable Machine, Nsynth, Hand Writing Recognition.

**What are Loaders in TensorFlow?**

Loaders are implemented for unloading, loading, and accessing a new type of servable machine learning model. The loaders are used for loading the algorithms and data to the backend.

**How does TensorFlow use Python API?**

Regarding TensorFlow and its development, Python is a primary language. It is the most original and recognizable language supported by TensorFlow. The functionalities of TensorFlow were earlier written in Python, and now it has been moved to C++.

**Can you mention a few differences between tf.variable and tf.placeholder?**

tf.variable and tf.placeholder are almost alike; however, few of their differences can be stated as below:

tf.variable defines variables modified with time, whereas tf.placeholder defines specific input data that does not change with duration.

At the time of definition, Tf.variable requires an initial value, whereas tf.placeholder at the time of definition, does not require an initial value.

These are the ten questions we discussed along with their solution. However, there are more than 270 probable questions. It is not that simple to mug up all the answers for the interview. What is essential is the strong fundamentals of deep learning using TensorFlow and even necessary is practicing it. To make it simple, you can join the **TensorFlow Certification** course since you will be guided by the industry leaders, get hands-on practice, and at the end of the course, you also get to work on industry level projects.