Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks / by Umberto Michelucci.

Format:

Book

Author/Creator:

Michelucci, Umberto., Author.

Language:

English

Subjects (All):

Artificial intelligence.

Python (Computer program language).

Open source software.

Computer programming.

Big data.

Artificial Intelligence.

Python.

Open Source.

Big Data.

Local Subjects:

Artificial Intelligence.

Python.

Open Source.

Big Data.

Physical Description:

1 online resource (425 pages)

Edition:

1st ed. 2018.

Other Title:

Case-based approach to understanding neural networks

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2018.

System Details:

text file

Summary:

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). You will: Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset.

Contents:

Chapter 1: Introduction

Chapter 2: Single Neurons

Chapter 3: Fully connected Neural Network with more neurons

Chapter 4: Neural networks error analysis

Chapter 5: Dropout technique

Chapter 6: Hyper parameters tuning

Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.)

Chapter 8: Convolutional Networks and image recognition

Chapter 9: Recurrent Neural Networks

Chapter 10: A practical COMPLETE example from scratch (put everything together)

Chapter 11: Logistic regression implement from scratch in Python without libraries. .

ISBN:

9781484237908

1484237900

OCLC:

1056157446