TensorFlow 2 tutorials

In artificial neural networks (ANNs), the activation function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer [1].

The activation functions are at the very core of Deep Learning. They determine the output of a model, its accuracy, and computational efficiency. In some cases, activation functions have a major effect on the model’s ability to converge and the convergence speed.

In this article, you’ll learn the following most popular activation functions in Deep Learning and how to use them with Keras and TensorFlow 2.

1. Sigmoid (Logistic)
2. Hyperbolic Tangent (Tanh)
3. Rectified Linear Unit…

Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.

Keras has provided a number of built-in callbacks, for example, EarlyStopping, CSVLogger, ModelCheckpoint, LearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called Callback which allows us to create our own callbacks and perform some custom actions. …

Reading data is the first step in any data science project. Often, you’ll work with data in JSON format and run into problems at the very beginning. In this article, you’ll learn how to use the Pandas built-in functions read_json() and json_normalize() to deal with the following common problems:

1. Reading simple JSON from a local file
2. Reading simple JSON from a URL
3. Flattening nested list from JSON object
4. Flattening nested list and dict from JSON object
5. Extracting a value from deeply nested JSON

Please check out Notebook for the source code.

1. Reading simple JSON from a local file

Let’s begin with a simple example.

[
  {
    "id": "A001",
    "name": "Tom",
    "math": 60,
    "physics": 66,
    "chemistry": 61
  },
  {
    "id": "A002",
    "name": "James",
    "math": 89,
    "physics": 76,
    "chemistry": 51
  },
  {
    "id": "A003",
    "name": "Jenny",
    "math": 79,
    "physics": 90,
    "chemistry": 78
  }…

Web scraping is the process of collecting and parsing data from the web. The Python community has come up with some pretty powerful web scrapping tools. However, many modern websites are dynamic, in which the content is loaded and populated using client JavaScript. Therefore, some extra setups are required in order to scrape data from JavaScript webpages.

In this article, you’ll learn how to scrape tables from a JavaScript webpage using Selenium, BeautifulSoup, and Pandas.

1. Challenges of a JavaScript webpage
2. Install libraries and Selenium web driver
3. Scrap tables using Selenium, BeautifulSoup, and Pandas

Please check out the source code from…

Web scraping is the process of collecting and parsing data from the web. The Python community has come up with some pretty powerful web scrapping tools. Among them, Pandas read_html() is a quick and convenient way for scraping data from HTML tables.

In this article, you’ll learn Pandas read_html() to deal with the following common problems and should help you get started with web scraping.

1. Reading tables from a string
2. Reading tables from a URL
3. Reading tables from a file
4. Parsing date columns with parse_dates
5. Explicitly typecast with converters
6. MultiIndex, header, and index column
7. Matching a table with match
8. Filtering tables with…

Suppose you encountered a situation where you need to push all rows in a DataFrame or require to use the previous row in a DataFrame. Maybe you want to calculate the difference in consecutive rows, Pandas shift() would be an ideal way to achieve these objectives.

In this article, we’ll be going through some examples of manipulating data using Pandas shift() function. We will focus on practical problems and should help you get started with data analysis.

1. Shifting values with periods
2. Shifting time-series data with freq
3. A practical example: calculating the difference in consecutive rows
4. A practical example: calculating the 7 days difference for time-series…

Pandas provides various built-in functions for easily combining datasets. Among them, merge() is a high-performance in-memory operation very similar to relational databases like SQL. You can use merge() any time when you want to do database-like join operations.

In this article, we’ll be going through some examples of combining datasets using Pandas merge() function. We will cover the following common usages and should help you get started with data combinations.

1. The simplest call without any key column
2. Specifying key columns using on
3. Merging using left_on and right_on
4. Various forms of joins: inner, left, right and outer
5. Using validate to avoid invalid…

Time-series data is common in data science projects. Often, you may be interested in resampling your time-series data into the frequency that you want to analyze data or draw additional insights from data [1].

In this article, we’ll be going through some examples of resampling time-series data using Pandas resample() function. We will cover the following common problems and should help you get started with time-series data manipulation.

1. Downsampling and performing aggregation
2. Downsampling with a custom base
3. Upsampling and filling values
4. A practical example

Please check out the notebook for the source code.

1. Downsampling and performing aggregation

Downsampling is to resample a time-series dataset to a wider time frame. For example, from minutes to hours, from days to years. The result will have a reduced number of rows and values can be aggregated with mean(), min(), max(), sum() etc. …