Mar 3, Do not mark non-universal wheels as universal. Oct 19, View code. Migration from django-storages Build status Support and announcements More information. Features Django file storage for Amazon S3. Django static file storage for Amazon S3. Works in Python 3! Installation Install using pip install django-s3-storage. Configure your Amazon S3 settings see Available settings, below.
The AWS region to connect to. The name of the bucket to store files in. Leave blank to use the default region URL. If False, then generated URLs will not include an authentication token, and their permissions will be set to "public-read". It also affects the "Cache-Control" header of the files. Important: Changing this setting will not affect existing files. Uninstalled Anaconda in order to run Docker.
It'd be my pleasure if it solves anyone's problem. I had a custom path set as the base path for my jupyter notebook start-up folder.
While taking some backup I had removed that folder. This was leading to Jupyter notebook and JupyterLab failing on start up. It worked fine after I fixed that path. You just need to find the right version of anaconda I think.
For me it works well when I downloaded a previous version, I was also facing the same issue. I could then launch jupyter from navigator itself. Stack Overflow for Teams — Collaborate and share knowledge with a private group.
Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Jupyter notebook not launching from Anaconda Navigator Ask Question. Asked 3 years, 2 months ago. Active 2 months ago. Viewed 33k times. Improve this question.
Omar Zayed 79 8 8 bronze badges. Use shutil. Show 5 more comments. Jim Calfas Jim Calfas 2, 1 1 gold badge 10 10 silver badges 2 2 bronze badges. Otherwise, shutil. Take care to realize that shutil. Be aware: shutil. After Python 3. NOhs 2, 3 3 gold badges 21 21 silver badges 51 51 bronze badges. MoonFruit MoonFruit 8 8 silver badges 9 9 bronze badges. Worked like a charm! This works perfectly, but it will fail if you want move the file from one device to another Invalid cross-device link — capooti.
Amar maybe this is better. It is there : Now we have moved a folder and its files from a source to a destination and back again. I used the destination and source reversed to see that the files moved from the source and then back to it I could see how that is unclear. Hope this helps you. Edit: I've turned this into a function, that accepts a source and destination directory, making the destination folder if it doesn't exist, and moves the files. Path dst. Peter Vlaar Peter Vlaar 2 2 silver badges 6 6 bronze badges.
You can easily turn this into a filtered move by using fnmatch. Also, its best to use os. For a more in-depth explanation of each, check out the linked articles. Naive Bayes is a family of simple algorithms that usually give great results from small amounts of training data and limited computational resources. Similar to LSA, MNB correlates the probability of words appearing in a text with the probability of that text being about a certain topic.
The main difference between the two is what is done with the data afterwards: LSA looks for patterns in the existing dataset, while MNB uses the existing dataset to make predictions for new texts. Although based on a simple idea, Support Vector Machines SVM is more complex than Naive Bayes, so it requires more computational power, but it usually gives better.
However, it's possible to get training times similar to those of an MNB classifier with optimization by feature selection, in addition to running an optimized linear kernel such as scikit-learn's LinearSVC. The basic idea for SVM is, once all the texts are vectorized so they are points in mathematical space , to find the best line in higher dimensional space called a hyperplane that separates these vectors into the desired topics.
Then, when a new text comes in, vectorize it and take a look at which side of the line it ends up: that's the output topic. Deep learning is actually a catch-all term for a family of algorithms loosely inspired by the way human neurons work.
Although the ideas behind artificial neural networks originate in the s, these algorithms have seen a great resurgence in recent years thanks to the decline of computing costs, the increase of computing power, and the availability of huge amounts of data.
Text classification , in general, and topic classification in particular, have greatly benefited from this resurgence and usually offer great results in exchange for some draconian computational requirements. It's not unusual for deep learning models to train for days, weeks, or even months. The differences are outside the scope of this article, but here's a good comparison with some real-world benchmarks.
Although deep learning algorithms require much more training data than traditional machine learning algorithms, deep learning classifiers continue to get better the more data they have. On the other hand, traditional machine learning algorithms, such as SVM and MNB, reach a limit, after which they can't improve even with more training data:.
This doesn't mean that the other algorithms are strictly worse; it depends on the task at hand. For instance, spam detection was declared "solved" a couple of decades ago using just Naive Bayes and n-grams. Other deep learning algorithms like Word2Vec or GloVe are also used; these are great for getting better vector representations for words when training with other, traditional machine learning algorithms. The idea behind hybrid systems is to combine a base machine learning classifier with a rule-based system that improves the results with fine-tuned rules.
These rules can be used to correct topics that haven't been correctly modeled by the base classifier. Training models is great and all, but unless you have a regular, consistent way to measure your results, you won't be able to judge the operability or improvability of your model. In order to measure the performance of a model, you'll need to let it categorize texts that you already know which topic category they fall under, and see how it performed. The ideal way to measure would be to take part of the manually tagged data, don't use it to train, and when the model is trained then use it to test.
This dataset is called a golden standard. However, leaving part of your hard-earned data just lying around unused instead of powering the model isn't very optimal. You could add this testing data to the final model, but then you have the same problem: you don't know if it was actually better off without it. Something that can be done instead is called cross-validation.
Then, you build the final model by training with all the data, but the performance metrics you use for it are the average of the partial evaluations. Topic analysis helps businesses become more efficient by saving time on repetitive manual tasks and gathers insights from the text data they manage on a daily basis. From sales and marketing to customer support and product teams, topic analysis offers endless possibilities across different industries and areas within a company.
Topic analysis enables you to do all this and more in a simple, fast, and cost-effective way. Every day, people send million tweets. Impressive, right? Within these immense volumes of social media data, there are mentions of products and services, stories of customer experiences , and interactions between users and brands.
Following these conversations is vital to get real-time actionable insights from customers, address potential issues, and anticipate crises. But getting a handle on all this data can be daunting. Topic analysis allows you to automatically add context to social media data, to understand what people are actually saying about your brand. Imagine you work at United Airlines. You could use topic detection to analyze what users are saying about your brand on Twitter , Facebook, and Instagram, and easily identify the most common topics in the conversation.
Your customers may be alluding to functionalities, ease of use, or maybe referring to customer support issues. You can also use topic detection to keep an eye on your competition and track trends in your field over time.
Add an extra dimension to your data analysis, by combining topic detection with sentiment analysis , so you can get a sense of the feelings and emotions behind social media comments. Aspect-based sentiment analysis is a machine learning technique that allows you to associate specific sentiments positive, negative, neutral to different aspects topics of a product or service.
In the case of United Airlines, not only would you know that most of your users are talking about your in-flight menu on Twitter, but you could also find out if they are referring to it in a negative or positive way, as well as the main keywords they are using for this topic. Follow reactions to marketing campaigns or product releases in real time and get an accurate map of perceptions and follow them as they change over time.
At MonkeyLearn, we used machine learning to analyze millions of tweets posted by users during the US elections. First, we classified tweets by topic, whether they were talking about Donald Trump or Hillary Clinton.
Then, we used sentiment analysis to classify tweets as positive , negative or neutral. This allowed us to do all sorts of analysis, like extracting the most relevant keywords for the negative tweets about Trump on a particular day. This graph shows the progression of positive, neutral and negative tweets referring to Trump and Clinton over time. The majority of tweets about both candidates are negative. Real-time topic analysis allows you to keep track of your brand image take action in case of a crisis, or make improvements based on customer feedback , but also to monitor your competitors and detect the latest trends in your industry.
Use topic identification and analysis to get insights about your brand by detecting and tracking the different areas of your business people are discussing the most. You could even combine this with keyword extraction to reveal the most relevant terms used about each of the topics. Combining a number of text analysis techniques allows you to get truly fine-grained results from your data analysis, to understand why something is happening and even make predictions for the future.
Example: Analyzing Slack reviews on Capterra. With MonkeyLearn, you can create personalized, custom-built models with machine learning and train them to do the work automatically. To show you exactly how it works, we used MonkeyLearn R package to analyze thousands of Slack reviews from the product review site Capterra.
After scraping the data, we defined a series of topic classifiers that helped us detect what the text in the reviews was about pricing , UX , customer support , performance , etc. Then, we used an opinion unit extractor to divide each review into individual opinions as some sentences may contain more than one opinion.
Finally, we created a sentiment analysis model to classify them as positive, negative or neutral:. The graphic above shows the sentiment for each of the aspects analyzed, weighted by the number of mentions. We see that the aspects users love most about Slack are ease of use , integrations , and purpose , while most of the complaints refer to performance-quality-reliability , pricing , and notifications.
Being able to deliver a great customer experience can make all the difference and help you stand out from your competitors. With, perhaps hundreds or thousands of support tickets arriving at your helpdesk every day, a big part of the job in customer service consists of processing large amounts of text.
First, you need to find out the subject of each ticket and tag them accordingly, and then triage tickets to the corresponding area that handles each type of issue. Machine learning opens the door to automating this repetitive and time-consuming not to mention horribly boring task to save valuable time for your customer support team, allowing them to focus on what they can do best: helping customers and keeping them happy.
Machine learning algorithms can be trained to sort customer support tickets in large volumes or instantaneously, tag each ticket with the relevant topic or department, and automatically route them to the proper employee, all with no need for human interaction. Thanks to a combination of machine learning models not only topic labeling, but also intent classification , urgency detection, sentiment analysis, and language detection your customer support team can:.
Customer feedback is a valuable source of information that provides insights into the experiences, level of satisfaction, and expectations of your customers, so you can take direct action, identify promoters and detractors, and make improvements based on feedback. If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems.
Search PyPI Search. Latest version Released: Aug 6, Navigation Project description Release history Download files. Project links Homepage Download. Maintainers dmmiller Project description Project details Release history Download files Project description Bert Extractive Summarizer This repo is the generalization of the lecture-summarizer repo. Examples are provided below. Simple Example with coreference from summarizer import Summarizer from summarizer. She loves him. Officials with Tishman and RFR did not immediately respond to a request for comments.
It's unclear when the deal will close. The building sold fairly quickly after being publicly placed on the market only two months ago.
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