The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. You can find a great explanation of what these are right here on wikipedia.
But we are not going to fully train one ourselves: that would take way more time than I would be willing to spend. Instead, we are going to do transfer learning, where we take a pre-trained CNN and replace only the last layer by a layer of our own. Then we only need to train that single layer, as all the other layers already have weights that are quite sensible. Here we exploit the fact that the images we are interested in have a lot of the same properties as those images that the original network was trained on. You can find a great explanation of transfer learning here.
Read on for a detailed example.
Keras is a high-level neural networks API, written in Python and capable of running on top of Tensorflow, CNTK or Theano. It was developed with a focus on enabling fast experimentation. In this blog, we are going to cover one small case study for fashion mnist.
Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
The end result wasn’t that great, but Shubham was using a sequential model rather than a convolutional neural network, so you can probably take this as a starting point and improve upon it.
The sharp eyed will see that the data set is defined by SQL. So, does that suffer from injection attacks? Short answer is no. If there was more than one result set within the Python code, it’s going to error out. So you’re protected there.
This is important, because the data set query can be defined with parameters. You can pass values to those parameters, heck, you’re likely to pass values to those parameters, from the external query or procedure. So, is that an attack vector?
Another factor is that you need explicitly to grant
EXECUTE ANY EXTERNAL SCRIPT rights to non-sysadmin, non-db_owner users, meaning a non-privileged user can’t execute external scripts at all. You can also limit the executing service account
1. SQL Server and Python Pandas Indexes are two different worlds and should not be mixed.
2. SQL Server uses Index primarily for DML operations and to keep data ACID.
3. Python Pandas uses Index and MultiIndex for keeping data dimensionality when performing data wrangling and statistical analysis.
4. SQL Server Index and Python Pandas Index don’t know about each other’s existence, meaning if user want to propagate the T-SQL index to Python Pandas (in order to minimize the impact of duplicates, missing values or to impose the relational model), it needs to be introduced and created, once data enters “in the python world”.
Read on for additional conclusions and the demos which bring us here.
Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Therefore, when you conduct a regression model it can be helpful to do a lasso regression in order to predict how many variables your model should contain. This secures that your model is not overly complex and prevents the model from over-fitting which can result in a biased and inefficient model.
Read on for demonstrations.
Pandas is an open-source Python package that provides users with high-performing and flexible data structures. These structures are designed to make analyzing relational or labeled data both easy and intuitive. Pandas is one of the most popular and quintessential tools leveraged by data scientists when developing a machine learning model. The most crucial step in the machine learning process is not simply fitting a model to a given data set. Most of the model development process takes place in the pre-processing and data exploration phase. An accurate model requires good predictors and, in order to acquire them, the user must understand the raw data. Through Pandas’ numerous data wrangling and analysis tools, this important step can easily be achieved. The goal of this blog is to highlight some of the central and most commonly used tools in Pandas while illustrating their significance in model development. The data set used for this demo consists of a supermarket chain’s sales across multiple stores in a variety of cities. The sales data is broken down by items within the stores. The goal is to predict a certain item’s sale.
Click through for an example of the process, including data cleansing and feature extraction, data analysis, and modeling.
Kristian Larsen has a couple of posts on Monte Carlo style simulation in Python. First up is a post which covers how to generate data from different distributions:
One method that is very useful for data scientist/data analysts in order to validate methods or data is Monte Carlo simulation. In this article, you learn how to do a Monte Carlo simulation in Python. Furthermore, you learn how to make different Statistical probability distributions in Python.
A useful method for data scientists/data analysts in order to validate methods or data is Bootstrap with Monte Carlo simulation In this article, you learn how to do a Bootstrap with Monte Carlo simulation in Python.
Both posts are worth the read.
Apache Spark MLlib users often tune hyperparameters using MLlib’s built-in tools
TrainValidationSplit. These use grid search to try out a user-specified set of hyperparameter values; see the Spark docs on tuning for more info.
Databricks Runtime 5.3 and 5.3 ML and above support automatic MLflow tracking for MLlib tuning in Python.
With this feature, PySpark
TrainValidationSplitwill automatically log to MLflow, organizing runs in a hierarchy and logging hyperparameters and the evaluation metric. For example, calling
CrossValidator.fit()will log one parent run. Under this run,
CrossValidatorwill log one child run for each hyperparameter setting, and each of those child runs will include the hyperparameter setting and the evaluation metric. Comparing these runs in the MLflow UI helps with visualizing the effect of tuning each hyperparameter.
Hyperparameter tuning is critical for some of the more complex algorithms like random forests, gradient boosting, and neural networks.
As a prerequisite, of course, you’ll need to have python installed in your machine, I recommend having an external IDE like Visual Studio Code to write your Python code as the PowerBI window offers zero assistance to coding.
You can follow this article in order to configure Python Correctly for PowerBI.
Step 2 is to add a Python Visual to the page, and let the magic happen.
Click through for the step-by-step instructions, including quite a bit of Python code and a few warnings and limitations.
Yesterday I ran a simple Twitter poll about the relative ease of learning R vs. Python. Although a correct answer to this query will ALWAYS have to be based on nuances like pre-existing skills and the scope of need, this originates from people telling me they encounter job or career profiles that list a need for R and/or Python. If they don’t have either, if they prioritised the pursuit of just one, which would be possible to develop a degree of competency more easily, more quickly and more efficiently?
Andy has also created a Twitter moment from the responses.
My thought, based only on the question itself, is that R would be better than Python because the hypothetical person has no additional programming skills. For someone with additional programming skills, the breakdown for me starts with, if your background is statistics, database development, or functional programming, you probably want R; if your background is object-oriented development or imperative programming, you probably want Python. And then it gets nuanced.