Neural Network Programming with Tensorflow

OverviewIn this On Demand course, we’ll start by building a simple flower recognition program, making you feel comfortable with TensorFlow, and it will teach you several important concepts in Neural Networks. Next, you’ll start working with high-dimensional uses to predict one output, create a handwritten number recognition system trained on the famous MNIST dataset, work with simple multilayer perceptron to a state of the art Deep Convolutional Neural Network. In the final program, estimate what a celebrity looks like. By the end of this course, you’ll not only be able to build a Neural Network for your own dataset, you’ll also be able to reason which techniques will improve your Neural Network using the power of TensorFlow.Objectives

Upon completion of this event you will:

• Develop a strong background in neural network programming from scratch, using the popular Tensorflow library.
• Use TensorFlow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more.
• Real-world case studies to illustrate the power of neural network models
• Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points
• Learn Linear Algebra and mathematics behind neural network.

Content

Learn the foundational points of neural networks

Understand the various types of networks like RNNs, CNNs, DBNs, GANs, etc

Learn how to optimize your neural networks

Apply your Learning with one of the most popular DL frameworks, TensorFlow

FurtherinfoThis course is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, the course can be used as a generic resource to bridge the gap between the math and the implementation of deep Learning.