Book Image

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone
Book Image

The TensorFlow Workshop

By: Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
Preface

2. Loading and Processing Data

Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler

Solution:

  1. Open a new Jupyter notebook to implement this activity. Save the file as Activity2-01.ipnyb.
  2. In a new Jupyter Notebook cell, import the pandas library, as follows:
    import pandas as pd
  3. Create a new pandas DataFrame named df and read the Bias_correction_ucl.csv file into it. Examine whether your data is properly loaded by printing the resultant DataFrame:
    df = pd.read_csv('Bias_correction_ucl.csv')

    Note

    Make sure you change the path (highlighted) to the CSV file based on its location on your system. If you're running the Jupyter notebook from the same directory where the CSV file is stored, you can run the preceding code without any modification.

  4. Drop the date column using the drop method. Since you're dropping the columns, pass 1 to the axis argument and True to the inplace argument:
    df.drop(&apos...