TensorFlow Tutorial for Beginners: Learn Basics with Example

TensorFlow Tutorial Summary

This TensorFlow tutorial for beginners covers TensorFlow basics to advance topics like linear regression, classifier, create, train and evaluate a neural network like CNN, RNN, auto encoders etc with TensorFlow examples. Refer this Machine Learning TensorFlow tutorial, sequentially, one after the other, for maximum efficacy to learn TensorFlow. Learn Tensorflow basic concepts with this TensorFlow Deep Learning tutorial.

What is TensorFlow?

Google’s TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks.

TensorFlow Course Syllabus


👉 Lesson 1 What is TensorFlow? How it Works? — Introduction & Architecture
👉 Lesson 2 How to Download & Install TensorFLow — Jupyter | Windows/Mac
👉 Lesson 3 Jupyter Notebook Tutorial — How to Install & use Jupyter?
👉 Lesson 4 TensorFlow Basics — Tensor, Shape, Type, Sessions & Operators

Advanced Stuff

👉 Lesson 1 TensorBoard Tutorial — TensorFlow Graph Visualization [Example]
👉 Lesson 2 Python Pandas Tutorial — DataFrame, Date Range, Use of Pandas
👉 Lesson 3 Pandas Cheat Sheet — Pandas Cheat Sheet for Data Science in Python
👉 Lesson 4 Import CSV Data — Import CSV Data using Pandas.read_csv()
👉 Lesson 5 Linear Regression with TensorFlow — Learn with Example
👉 Lesson 6 Linear Regression with Facet & Interaction Term — Learn with Example
👉 Lesson 7 Binary Classification in TensorFlow — Linear Classifier Example
👉 Lesson 8 Gaussian Kernel in Machine Learning — Kernel Methods Examples
👉 Lesson 9 Artificial Neural Network (ANN) — TensorFlow Example Tutorial
👉 Lesson 10 TensorFlow CNN Image Classification — Learn with Steps & Examples
👉 Lesson 11 TensorFlow Autoencoder — Dataset with Deep Learning Example
👉 Lesson 12 RNN (Recurrent Neural Network) Tutorial — TensorFlow Example
👉 Lesson 13 PySpark Tutorial for Beginners — Learn with EXAMPLES
👉 Lesson 14 Scikit-Learn Tutorial — How to Install, Python Scikit-Learn Example
👉 Lesson 15 Python NumPy Tutorial — np.zeros, np.arange, vstack and hstack
👉 Lesson 16 PyTorch Tutorial — Regression, Image Classification Example
👉 Lesson 17 PyTorch Transfer — PyTorch Transfer Learning Tutorial with Examples
👉 Lesson 18 Keras Tutorial — What is Keras? How to Install in Python [Example]
👉 Lesson 19 TensorFlow Vs Keras — TensorFlow Vs Keras

Must Know!

👉 Lesson 1 TensorFlow Books — 10 BEST TensorFlow Books
👉 Lesson 2 Tensorflow Tutorial PDF — Download Tensorflow Tutorial PDF for Beginners

What will I learn in this TensorFlow Tutorial?

In this TensorFlow 2.0 tutorial, you will learn basic and advanced concepts of TensorFlow like TensorFlow introduction, architecture, how to download and install TensorFlow, TensorBoard, Python Pandas, Linear regression, Kernel Methods, Neural Networks, Autoencoder, RNN, etc.

Are there any prerequisites for this TensorFlow Tutorial?

This online Tensorflow Python Tutorial is designed for beginners with little or no TensorFlow Experience. Though basic understanding of Python is required.

Who is this TensorFlow Tutorial for?

This TensorFlow Deep Learning Tutorial is for beginners who want to gain knowledge about TensorFlow, Machine Learning, Deep Learning, and more advanced concepts. This tutorial also helps Python developers for research and development purposes in Machine Learning and Deep Learning with TensorFlow using Python.

Why should you learn TensorFlow?

TensorFlow is a widely preferred framework for Machine Learning and Deep Learning applications, and it also allows building a strong foundation for Deep learning. Moreover, it is widely used by many big companies worldwide, so there is a vast number of job opportunities available for candidates with better salary prospects. Therefore, learning TensorFlow to either get a job or gain additional knowledge is beneficial for a candidate.