TensorFlow is an open-source library that is commonly used for data flow programming. However, it also includes a symbolic math library that can be used for machine learning applications and neural networking.

Developed by the Google Brain team, TensorFlow is already playing a huge role in helping machines advance. This is why it is one of the most important technologies that people should definitely learn to advance their career. It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

1.Complete Guide to TensorFlow for Deep Learning with Python

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!

With this course, you will:

  • Understand how neural networks work
  • Build your own neural network from scratch with Python
  • Use TensorFlow for classification and regression tasks
  • Use TensorFlow for image classification with convolutional neural networks
  • Use TensorFlow for Time Series Analysis with recurrent neural networks
  • Use TensorFlow for solving unsupervised learning problems with auto encoders
  • Learn how to conduct reinforcement learning with openAI gym
  • Create generative adversarial networks with TensorFlow

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning. This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand.

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. You will also have plenty of exercises to test your new skills along the way.

2. Master Deep Learning with TensorFlow in Python

Build Deep Learning Algorithms with TensorFlow, Dive into Neural Networks and Master the #1 Skill of the Data Scientist

With this course, you will:

  • Gain a strong understanding of TensorFlow
  • Build deep learning algorithms from scratch in Python using NumPy and TensorFlow
  • Set yourself apart with hands-on deep and machine learning experience
  • Grasp the mathematics behind deep learning algorithms
  • Understand back propagation, stochastic gradient descent, batching, momentum, and learning rate schedules
  • Know the ins and outs of underfitting, overfitting, training, validation, testing, early stopping, and initialization
  • Competently carry out pre-processing, standardization, normalization, and one-hot encoding

This course will start with the basics and take you step by step toward building your very first (or second, or third etc.) Deep Learning algorithm. Moreover, it programs everything in Python and explain each line of code.

Each lecture is built upon the last and practical exercises, mean that you can practice what you’ve learned before moving on to the next step.

3. Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

In this course, you will learn how to:

  • Apply momentum to backpropagation to train neural networks
  • Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
  • Understand the basic building blocks of Theano
  • Build a neural network in Theano
  • Understand the basic building blocks of TensorFlow
  • Build a neural network in TensorFlow
  • Build a neural network that performs well on the MNIST dataset
  • Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
  • Understand and implement dropout regularization in Theano and TensorFlow
  • Understand and implement batch normalization in Theano and Tensorflow
  • Write a neural network using Keras
  • Write a neural network using PyTorch
  • Write a neural network using CNTK
  • Write a neural network using MXNet

In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

4. Deep Learning with TensorFlow

Channel the power of deep learning with Google's TensorFlow!

With this course, you will learn how to:

  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Understand intuitively convolutional neural networks for image recognition
  • Bootstrap a neural network from simple to more accurate models
  • See how to use TensorFlow with other types of networks
  • Program networks with SciKit-Flow, a high-level interface to TensorFlow

This course will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.

During the video course, you will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more.

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5. Tensorflow Bootcamp For Data Science in Python

Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python

With this course, you will be able to:

  • Harness the power of anaconda/iPython for practical data science
  • Learn how to install & use Tensorflow within anaconda
  • Implement statistical & machine learning with Tensorflow
  • Implement neural network modelling with Tensorflow
  • Implement deep learning based unsupervised learning with Tensorflow
  • Implement deep learning based supervised learning with Tensorflow

This course covers all the aspects of practical data science with Tensorflow. You’ll start by absorbing the most valuable Python Tensorflow data science basics and techniques.

You will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

6. A beginners guide for building neural networks in tensorflow

How to learn deep learning and neural networks in tensorflow from scratch. Tensorflow training for beginners.

In this course, you will:

  • Build step by step our neural network in python code
  • Have each coding step explained
  • Have the option to customize your own neural network
  • Get to train and test your neural network

You will learn step by step how to code a neural network in tensorflow. Each step will be explained. With this course you will have created, trained and tested a complete neural network.

7. Tensorflow for Beginners

A complete guide for building machine learning and deep learning solutions using Tensorflow

With this course, you will:

  • Learn Tensorflow from groundup
  • Learn to build real world AI and ML apps using tensorflow
  • Learn ML lifecycle and Tensorboard
  • Learn to implement neural networks using Tensorflow

The course combines theory and real-world applications to offer the most practical course that can help you learn TensorFlow in a systematic manner. It will show you how you can get started on machine learning, deep learning and building your own neural networks from scratch.

The course starts with a detailed introduction into TensorFlow and its basics, including delving into the TensorFlow Foundation. It also covers the machine learning lifecycle, TensorBoard, logical regression, neural network basics, single & multiple hidden layer neural networks, convolutional neural networks, and deep learning. In the last section of the course, you’ll use everything you’ve learned throughout the course to build an actual project from scratch.

8. TensorFlow: Getting Started

This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You'll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.

In this course, you will:

  • See how TensorFlow easily addresses these concerns by learning TensorFlow from the bottom up.
  • Be introduced to the installation process, building simple and advanced models, and utilizing additional libraries that make development even easier.
  • Learn how the unique architecture in TensorFlow lets you perform your computing on systems as small as a Raspberry Pi, and as large as a data farm.
  • Explore using TensorFlow with neural networks in general, and specifically with powerful deep neural networks.

9. Understanding the Foundations of TensorFlow

This course introduces TensorFlow, an open source data flow library for numerical computations using data flow graphs.

In this course, you will:

  • Learn the TensorFlow library from very first principles
  • Start with the basics of machine learning using linear regression as an example and focuses on understanding fundamental concepts in TensorFlow.
  • Discover how to apply them to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions, and the computation graph.
  • Be introduced to TensorBoard, the visualization tool used to view and debug the data flow graphs.
  • Work with basic math operations and image transformations to see how common computations are performed.
  • Solve a real world machine learning problem using the MNIST handwritten dataset and the k-nearest-neighbours algorithm.

10. Machine Learning With TensorFlow The Practical Guide

A comprehensive source to help you learn Machine learning with TensorFlow

This course includes the following topics:

  • Fundamentals of Tensorflow and its installation on Windows, Mac and Linux
  • Basics of Tensorflow including tensors, operators, variables and others
  • Basics of Machine learning and its types
  • Main algorithms and its implementation - Linear regression, logistic regression, KNN regression and others
  • Clustering and its approaches
  • Advanced machine learning- Neural networks, convolution neural network, recurrent neural networks
  • Project on deep neural networks

This course comprises numerous topics with the sole aim to understand Tensorflow and machine learning. This course gives an insight into the basics of Tensorflow covering topics like tensors, operators and variables. Furthermore, this course also covers advanced machine learning like a neural network, convolution neural network and others. Here, you’ll also gain the practice by implementing it in a project on Deep Neural Network.

11. Tensorflow for Beginners

A complete guide for building machine learning and deep learning solutions using Tensorflow

In this course, you will learn about:

  • A detailed introduction into TensorFlow
  • Familiarity with TensorFlow foundation
  • Machine learning lifecycle and TensorBoard
  • Logistic regression & neural networks basics
  • Single & multiple hidden layer neural networks
  • Convolutional neural networks
  • Deep learning

Starting at the very beginning, this TensorFlow tutorial will focus on the basics of TensorFlow and from there progress on to difficult concepts. There are also entire sections that are dedicated to Deep Learning and also using everything you learn in this course to build a complete project from scratch.

The course combines the perfect blend of theory and practical applications to provide you with the best method of learning TensorFlow and how you can get started on machine learning, deep learning and building your own neural networks from scratch.

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