Deep Learning With TensorFlow

Deep Learning With TensorFlow. “What is Deep Learning” video will help you to understand the relationship between Deep Learning, Machine Learning, and Artificial Intelligence and how Deep Learning came into the picture.

This Deep Learning With TensorFlow tutorial will be discussing Artificial Intelligence, Machine Learning, and its limitations, how Deep Learning overcame Machine Learning limitations, and different real-life applications of Deep Learning.

Why Learn Deep Learning With TensorFlow?

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning a vast range of model architectures. Traditional neural networks relied 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 layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

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