For deep learning and artificial intelligence, Tensorflow is the most popular library built by Google. Many AI and Machine Learning companies choose it over other libraries to achieve their goals. To put it simply, if you want to do Deep Learning, you'll need Tensorflow. Therefore, I have created this list of the best TensorFlow courses for developers who want to learn this machine learning library and deep learning framework. I have also created a detailed comparison between TensorFlow and Keras, if you want to check it out, you can check it out here.

DeepLearning.Ai is founded by the AI pioneer -- Andrew Ng, who is also the Co-Founder of Coursera, DeepLearning AI and Adjunct Professor at Stanford University. Through the guided series of lectures and exercises, you will build the cognitive skills you need to deeply understand the concepts and applications of AI.

Complete Guide to TensorFlow for Deep Learning with Python by Jose Portilla will help you learn how to use Google's Deep Learning Framework, TensorFlow with Python. This Deep Learning TensorFlow course is for Python developers who want to learn the latest Deep Learning techniques with TensorFlow. You will understand how Neural Networks work. Then you will build your own Neural Network from scratch with Python. This Deep Learning TensorFlow tutorial will teach you to use TensorFlow for Classification and Regression Tasks.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs.