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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Dr. Amita Kapoor, Sujit Pal
4.3 (26)
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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

4.3 (26)
By: Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

Prediction using linear regression

Linear regression is one of the most widely known modeling techniques. Existing for more than 200 years, it has been explored from almost all possible angles. Linear regression assumes a linear relationship between the input variable (X) and the output variable (Y). It involves finding a linear equation for predicted value Y of the form:

Yhat = WTX + b

Where X = {x1, x2, ..., xn} are the n input variables, and W = { w1, w2, ...wn} are the linear coefficients, with b as the bias term. The bias term allows our regression model to provide an output even in the absence of any input; it provides us with an option to shift our data left or right to better fit the data. The error between the observed values (Y) and predicted values (Yhat) for an input sample i is:

ei = Yi - Yhati

The goal is to find the best estimates for the coefficients W and bias b, such that the error between the observed values Y and the predicted values Yhat is minimized...

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Deep Learning with TensorFlow 2 and Keras
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