Google’s TensorFlow is currently the most famous deep-learning library in the world. TensorFlow offers a comprehensive, flexible ecosystem of tools, libraries, and community resources.
Table of Contents
What is TensorFlow?
TensorFlow is an industry-grade machine-learning library developed by Google DeepMind in 2015. It’s written in C++ and Python, with APIs available for Java, Go, Rust, and many more languages.
Powered by the underlying mechanics of neural networks, TensorFlow makes it possible to run general-purpose deep learning algorithms across a wide variety of problem types.
It allows developers to push the state-of-the-art in machine learning (ML), and build and deploy ML-powered applications.
TensorFlow architecture works in three parts:
- Preprocessing the data
- Build the model
- Train and estimate the model
Why You Should Use TensorFlow?
Google is pushing us towards AI/ML as much as it can — they use it a lot themselves — and it’s going to be the skill that separates tech pros from amateurs; TensorFlow is the tool for building deep networks, thus, any company that wants to keep up with Google should learn how this library works!
Think Google doesn’t use Tensorflow? Think again, they obviously do as they’ve open-sourced it. I’d wager you’re going to see more cool stuff from them soon. So if you want a job there or anywhere else that’s betting big on ML, learn about this tool and make sure you know what it does well and where the limitations are.
Tutorials and Courses
This section provides tutorials for both novice and experienced users. The tutorials are written in Jupyter notebooks and can be run directly in Google Colab, a hosted notebook environment that requires no setup. To get started, the Keras sequential API is a great option for those who are new to this field. It allows users to construct models by combining different components.
In this video, you will learn the platform’s concepts like what are tensors, what are the program elements in TensorFlow, what are constants & placeholders in TensorFlow for Python, how variable works in a placeholder, and a demo on MNIST.
This course will guide you through how to use Google’s TensorFlow framework to build your own Neural Network from scratch with Python. Neural Networks are changing how we tackle artificial intelligence and create advanced data-driven systems that make decisions as humans do – it’s a whole other world! This course explains just what neural networks are and how they work.
You’ll learn all about the most popular types of neural models used in computer vision, natural language processing, speech recognition, text generation using RNNs(Recurrent Neural Networks), reinforcement learning problems solved through GAN networks(“Generative Adversarial Networks”), sequential data prediction using LSTM(Long Short Term Memory) sequential recurrent networks and much more.
This new TensorFlow Specialization teaches you how to use the framework so that you can start building and applying scalable models to real-world problems.
Packed with content to help you build basic neural networks in TensorFlow and write convolutions, this Udemy course is perfect for beginners or anyone looking for a fun project. You will find insightful explanations of TensorFlow best practices like how to set up your workspace and library creation.
There are plenty of tips on setting gradients and scale parameters while training your network that will teach you the tricks of the trade when it comes to deep-learning machine vision. Loads of code snippets make it easy to follow along as well as adding some lighthearted humor that makes these complicated concepts more approachable—even if they can be difficult! Knowledge is power so read up and get ahead today!
- TensorBoard – TensorBoard provides the visualization and tooling needed for machine learning experimentation.
- Colaboratory – This tool allows you to write and execute Python in your browser, with zero configuration required.
- What-If Tool – Using WIT, you can test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data, and for different ML fairness metrics.
- Tensor Playground – A playground with a neural network on your browser.
- MLPerf – Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.