Developed by Google Brain, TensorFlow is one of the most popular open-source libraries for numerical computation. This library helps in building and training deep neural network applications and offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud.
In this article, we list down 9 free tutorials to become a pro in the open-source machine learning framework, TensorFlow.
1| Official Documentation
What’s better than understanding the library from the maker itself. In this official documentation, you will learn how to use machine learning techniques, utilise machine learning at production scale, creating and deploying TensorFlow models on the web and mobile, understanding TensorFlow's High-Level APIs and much more.
2| TensorFlow Tutorial: Deep Learning for Beginners
In this tutorial, you will learn the basics and advance machine learning topics like Linear Regression, Classifiers, create, train and evaluate a neural network like CNN, RNN, autoencoders, etc. You will also learn the architecture of TensorFlow, how Jupyter Notebook works, graph visualisation, basics of TensorFlow, Linear Regression and Linear Classifier with TensorFlow, kernel methods in Machine Learning, RNN with TensorFlow example and much more.
3| Natural Language Processing in TensorFlow
In this tutorial, you will learn how to build natural language processing (NLP) systems using TensorFlow. You will also learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. Furthermore, you’ll also be able to apply RNNs, GRUs, and LSTMs in TensorFlow.
4| TensorFlow Tutorial For Beginners
TensorFlow Tutorial for beginners will help you in understanding the performance of Deep Learning. You will learn about the performance of multidimensional data arrays, how to install TensorFlow and get started, understand the basics of TensorFlow, load in data on Belgian traffic signs and exploring it with simple statistics and plotting, how to perform data manipulation and other such.
5| Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
In this tutorial, you will learn the best practices for using TensorFlow framework, how to build a basic neural network in TensorFlow, train a neural network for a computer vision application and understand how to use convolutions to improve a neural network. This is an intermediate level course and will need basic knowledge and experience in Python coding.
6| TensorFlow Tutorial
This TensorFlow Tutorial is mainly for Python developers who focus on research and development with various machine learning and deep learning algorithms. The aim of this tutorial is to describe all TensorFlow objects and methods. In this tutorial, you will learn about the basics of TensorFlow, CNN, RNN, Tensorboard visualisation, Single layer perceptron, Linear Regression Tensorflow, gradient descent optimisation, image recognition using TensorFlow, recommendations using neural network training, understand TFlearn and optimisers and much more.
7| Introduction to Deep Learning with TensorFlow
This tutorial is at an advanced level and talks about concepts such as how to create a neural net model, understand how the network will run, simple preprocessing language data for deep learning, train and test data, how to use CUDA and GPU version of TensorFlow for deep learning, basics of RNN, CNN and LSTM in TensorFlow, TFlearn in high-level abstraction layer, and other such topics.
8| TensorFlow Tutorial – Deep Learning Using TensorFlow
This TensorFlow tutorial is mainly designed for professionals and enthusiasts who are interested in applying Deep Learning Algorithm using TensorFlow to solve various problems. In this blog, you will learn the basics of TensorFlow, how to build and run computational graph, understand constants, placeholder and variables, how to perform Linear Regression Model using TensorFlow, and much more.
9| Python TensorFlow Tutorial – Build a Neural Network
This is an introductory tutorial to TensorFlow which will give an overview of some of the basic concepts of TensorFlow in Python. It will help in building more complex deep learning networks, such as Convolution Neural Networks, natural language models and Recurrent Neural Networks. You will learn to create and build a simple three-layer neural network to classify the MNIST dataset.
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