turicreate | Turi Create simplifies the development of custom machine | Machine Learning library

thanks for the direct question. I am here after I received your email. I think the two questions that you raised are somewhat similar and can be answered through each other. Basically, your question is why do we need word count vector while conducting sentiment analysis.

In all honesty, this is actually a long answer but I will try to make it as concise as possible. I am not aware of your level of NLP understanding at the moment but all machine learning models are only built for numerical values which means when you are working with text data, you first need to convert the text into a numerical format. This process is known as vectorization. That is essentially what we are doing here but there are many ways of achieving that. The vectorizer that is being used here is a CountVectorizer where each word in the counts dictionary is treated as a separate feature for that particular sentence. This leads to the creation of a sparse matrix which can represent m sentences with n unique words as a m x n matrix.

The way we're going about it is that we count the number of times a word occurs a particular type of sentence (either positive or negative). It is understandable that words like terrible might have a very high count in negative sentences and almost 0 counts in positive sentences. Similarly, there will be a reverse effect for words like 'great' and 'amazing'. This is what is used in classifiers to allot weights to each word. Negative weights to words occurring popularly in negative classes and positive weights to words occurring in positive classes. This is what sentiment analysis classification is based on.

This might be a really helpful resource. You can also read through this.

PS: I wouldn't recommend using TuriCreate before you have either coded this from scratch to understand how it works or used scikit-learn because TuriCreate abstracts a lot of the usage and you might not understand what is happening in the background.

The issue you're running into is with tokenization pattern, specified in token_pattern='(?u)\\b\\w\\w+\\b'. You may adapt it to your task:

Creating new environment with a version of python (3.7) compatible with Turicreate solved this problem..

First of all - I have the experience limited to ubuntu 18.04 and 16.xx and python DL frameworks. But I hope some sugestions will be helpfull.

• If I were familiar with docker I would rather consider to use docker instead of setting-up everything from scratch. This approach is described in section about tensorflow container
• If you decided to setup all components yourself please see this guideline I used some contents from it for 18.04, succesfully.
• be carefull with automatic updates. After the configuration is finished and tested protect it from being overwritten with newest version of CUDAor TensorRT.

Answering one of your sub-questions - How does this integrate with jupyter notebook? - it does not, becuase it is unneccesary. CUDA library cooperates with a framework such as Tensorflow, not with the Jupyter. Jupyter is just an editor and execution controller on the server side.

Instead of downgrading python for the whole OS, may be you should try using a downgraded python version for just your project.

Try using a virtual environment and use it to create a dev environment with python 3.7.

Not possible. You need to write a python routine to print the decision tree in an awkward manner.

I found the problem, and it basically stemmed from my unfamiliarity with Python.

In one part of the Data Preparation section, after creating bounding boxes out of the mask images, each annotation is assigned a 'label' indicating the type of object the annotation is meant to be. My data had a different name format than the tutorial's data, so rather than each annotation having 'label': 'bike', my annotations had 'label': 'vid4frame25`, 'label': 'vid4frame26', etc.

Correcting this such that each annotation has 'label': 'hand' seems to have corrected this (or at least it's creating a legitimate-seeming model so far).

Error shows in which line it has problem