TensorFlow is all about building neural networks that can “learn” functions, and linear regression can be learnt by the simplest possible neural network – of just 1 neuron! In contrast, the XOR function requires 3 neurons arranged in 2 layers, and smart image recognition can require thousands of neurons. In this course, Building Regression Models using TensorFlow, you’ll learn how the neurons in neural networks learn non-linear functions. First, you’ll begin by learning functions such as XOR, and how to train different gradient descent optimizers. Next, you’ll dive into the implications of choosing activation functions, such as softmax and ReLU. Finally, you’ll explore the use of built-in estimators in Tensorflow. By the end of this course, you’ll have a better understanding of how neurons “learn”, and how neural networks in TensorFlow are set up and trained to execute operations such as regression and classification.
Author Name: Vitthal Srinivasan
Author Description:
Vitthal has spent a lot of his life studying – he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working – as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b… more