Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (12 chapters)

Single hidden layer model

Here, we'll put the basics of neural network into practice. We'll adapt the logistic regression TenserFlow code into a single hidden layer of neurons. Then, you'll learn the idea behind backpropagation to compute the weights, that is, train the net. Finally, you'll train your first true neural network in TensorFlow.

The TensorFlow code for this section should look familiar. It's just a slightly evolved version of the logistic regression code. Let's look at how to add a hidden layer of neurons that will compute nonlinear combinations of our input pixels.

You should start with a fresh Python session, execute the code to read in, and set up the data as in the logistic model. It's the same code, just copied to the new file:

import tensorflow as tf
import numpy as np
import math
from tqdm import tqdm
    from tqdm import tqdm
except ImportError:
    def tqdm(x, *args, **kwargs):
        return x

You can always go back to the previous sections and remind...