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Written by:
Martin Barrett
Last Updated:
DataCamp TensorFlow Beginners Guide

DataCamp TensorFlow: Beginners Guide

If you’re interested in data science, you’ve probably heard of TensorFlow. TensorFlow is a platform designed to help with processing data, problem-solving, and model-building. 

But what exactly does this platform do, and how can you learn more about it through DataCamp? What is TensorFlow DataCamp?

These are the questions we will be answering in our beginner’s guide to DataCamp TensorFlow.

We’ll be going over what TensorFlow is, what DataCamp has to offer in terms of TensorFlow course content, and how you can get started using this platform for yourself. 

What Is TensorFlow? 

TensorFlow is a beginner-friendly, end-to-end platform for machine learning. Using TensorFlow, you can essentially manage every single aspect of machine learning systems. 

You might think of TensorFlow as a kind of machine learning library. It’s owned by Google and has been available to the public since 2015.

You can work with either JavaScript or Python when using TensorFlow, collecting together different neural networks and using programmatic metaphors to make them work more efficiently. 

Using TensorFlow as a developer, you can create structures called dataflow graphs.

These graphs help to visualize the movement of data through processing nodes, with every node representing a mathematical operation, connected by tensors (multidimensional data arrays). 

Through TensorFlow’s incredible library, you can access pre-trained models to incorporate into your own project, or you can borrow TensorFlow code in order to train your own models. 

Learning About TensorFlow On DataCamp

As a beginner, one of the best places to learn how to use TensorFlow in Python, specifically, is DataCamp. 

DataCamp has a course that you can start for free, called Introduction to TensorFlow in Python

The first module of this course is free, and once you have completed it, you can choose to pay for the rest of the course, which we highly recommend doing because the subsequent modules are invaluable for learning the basics of TensorFlow. (Check out ‘DataCamp Review: Is It Right For You?‘)

Introduction To TensorFlow In Python Course 

Chapter 1: Introduction 

The first chapter in the Introduction to TensorFlow in Python course is called ‘Introduction to TensorFlow’, and it basically outlines all the basics you will need to use as building blocks for using TensorFlow. Before you can build advanced models, you will first need to understand the basics. (See also ‘How Good Is Datacamp For Python Courses?’)

After you learn the best ways to define constants and variables, you’ll move on to basic operations such as to perform element-wise multiplication and use matrix multiplication to make predictions. 

Advanced operations are also included in the introduction, such as how to reshape tensors, use gradients for optimization, and navigate image classification data. 

Chapter 2: Linear Models 

Chapter 2 is called ‘Linear Models’, and it builds on the information and skills you learned in the first chapter to teach you building and solving skills, as well as how to use models to make predictions. 

Specifically, this chapter will teach you about the class of models known as linear regression models. 

You’ll start this chapter by learning how to input data, which includes loading the data through the use of pandas and learning how to set a data type. 

You’ll also learn how to use TensorFlow for loss functions and modifications of said loss functions before moving on to the linear regression model.

This chapter teaches you how to set linear regressions up, how to train the model, and all about multiple linear regression. 

The final part of the chapter focuses on batch training, starting with how to prepare for batch training and how to batch train a linear model.

Chapter 3: Neural Networks 

Neural networks are a core component of TensorFlow, so DataCamp’s course dedicates a whole chapter to this aspect of using the platform. 

What is TensorFlow DataCamp?

Starting with dense layers, the neural networks chapter covers dense layers through the lens of linear algebra and includes a lesson on the low-level approach. 

Moving on to activation functions (both binary and multiclass classification problems), this chapter takes you all the way through to the topic of optimizers, including how and why to avoid local minima. 

You’ll then learn how to train a network in TensorFlow, covering initialization, definition, and the step-by-step process of training neural networks with TensorFlow. 

Chapter 4: APIs 

The final chapter of the course is titled ‘High-Level APIs’. In this chapter, you will learn how to use Keras to define a neural network, and for training and validation purposes.

You’ll then learn how to use Estimators API for training models. 

The goal of this course model is to teach you the best methods for streamlining model definition and training, without making errors along the way. You will be able to design and train deep-learning models in 15 lines of code.

Setting Up TensorFlow For DataCamp 

In order to follow the Introduction to TensorFlow in Python course offered by DataCamp, you will need to use TensorFlow 2.6. Here’s how to set everything up and get started: 

First and foremost, you will need to import the TensorFlow library.

You will need to do this under the ‘tf’ alias, initializing two constant variables and passing one array, composed of four numbers, into the function ‘constant()’. 

Now, you could technically also do this in an integer, but arrays are more common, and they are what you’ll be focusing on when working with tensors.

You can multiply both variables using ‘multiply ()’ and use the ‘result’ variable for result storage. Use ‘print ()’ to print your result. 

If you would also like to pass in options, you can use ‘ConfigProto’ (a protocol buffer’ to specify configuration options, logging the CPU or GPU for each operation. 

Final Thoughts 

DataCamp’s Introduction to TensorFlow in Python course is one of the best ways to learn how to use TensorFlow. 

This course will teach you the fundamentals of how to work with neural networks and build deep learning models on TensorFlow using Python. Today, a skilled data scientist equipped with nothing more than a laptop can classify tens of thousands of objects with greater accuracy than the human eye.

The course consists of four chapters, one of which is available for free. 

When you’re ready to get started with DataCamp’s TensorFlow course, follow the instructions in this guide to install the correct version of TensorFlow and start learning!

If you want to brush up on your basics first, DataCamp also has courses such as ‘Understanding Data Science’ and ‘Introduction to Python’.

Frequently Asked Questions 

Does Anyone Still Use TensorFlow?

Although a lot of people are now choosing to use PyTorch as their machine learning framework, many are still using TensorFlow, including researchers and industry professionals.

That’s because the visualization in TensorFlow is still superior compared to PyTorch, so the training process is easier to track. 

Is It Difficult To Learn TensorFlow?

One of the advantages of TensorFlow is that it’s beginner-friendly. Although experts also use it regularly, beginners will be able to pick up the basics quite easily and be able to start creating machine learning models in no time. 

Plus, TensorFlow is very user-friendly in that it can be used on both mobile and desktop, as well as cloud and web, so it’s easy to use in that sense as well. 

Are Mathematics And Coding Required For TensorFlow?

Yes, some mathematical concepts as well as a basic knowledge of coding will be required in order to use TensorFlow.

Any kind of machine learning algorithm is based on mathematics, after all, so you’ll need to be familiar with the core concepts in order to find solutions for said algorithms. 

You will also need to know coding for data management, tuning parameters, and analyzing results. Without these skills, it will not be possible to test your model and optimize it.