handson-ml | ⛔️ DEPRECATED – See https | Machine Learning library

From the python document, it is mentioned that urllib.request.urlretrieve returns a tuple and will be used to open file as shown in Code-A below.

However in the example Code-B. The urllib.request.urlretrieve does not return but the code will fail without it. Please help clarify what does urllib.request.urlretrieve doing in Code B. THanks

Code A

I am learning python programming and I am stuck in something that is probably very basic but I cannot fix. My problem is: I have a .py file called "Downloading_The_Data" in a directory. This file contains some variables like "HOUSING_PATH" and others. Now, I have another .py file in the same directory and I want to import the other file into it. I simply write "import DOWNLOADING_THE_DATA" and everything works fine except when I try to use the variable "HOUSING_PATH" which seems not imported. The code:

The "DOWNLOADING_THE_DATA" file:

I have been trying to implement the Reinforcement learning algorithm on Python using different variants like Q-learning, Deep Q-Network, Double DQN and Dueling Double DQN. Consider a cart-pole example and to evaluate the performance of each of these variants, I can think of plotting sum of rewards to number of episodes (attaching a picture of the plot) and the actual graphical output where how well the pole is stable while the cart is moving.

But these two evaluations are not really of interest in terms to explain the better variants quantitatively. I am new to the Reinforcement learning and trying to understand if any other ways to compare different variants of RL models on the same problem.

I am referring to the colab link https://colab.research.google.com/github/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb#scrollTo=MR0z7tfo3k9C for the code on all the variants of cart pole example.

I am reading the book "Hands-On Machine Learning" and I have a problem with exercise 9 of chapter 13, the exercise is as follows:

a. Load the Fashion MNIST dataset (introduced in Chapter 10); split it into a training set, a validation set, and a test set; shuffle the training set; and save each dataset to multiple TFRecord files. Each record should be a serialized Example protobuf with two features: the serialized image (use tf.io.serialize_tensor() to serialize each image), and the label.

b. Then use tf.data to create an efficient dataset for each set. Finally, use a Keras model to train these datasets, including a preprocessing layer to standardize each input feature.

You can find the exercise with the solution at the end of this notebook: https://github.com/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb

I loaded the data like this:

• I have trained a ML model, and stored it into a Pickle file.
• In my new script, I am reading new 'real world data', on which I want to do a prediction.

However, I am struggling. I have a column (containing string values), like:

I am currently studying the book "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow". I tried running the following example, without success however. The link is working, pandas is installed correctly, os, tarfile and urllib are system packages. Still, I get the error message below (tried Jupyter & Spyder):

For housing data set, I am trying to use DataFrameMapper() from sklearn_pandas to apply polynomial features on selected columns.

My code:

Getting this memory error. But the book/link I am following doesn't get this error.

A part of Code: