Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Discover a revolutionary approach to training machine learning models directly on your Mac with Create ML, which simplifies the process while delivering robust Core ML models. You can train several models with various datasets all within one cohesive project. Utilize Continuity to preview your model's performance by connecting your iPhone's camera and microphone to your Mac, or simply input sample data for evaluation. The training process allows you to pause, save, resume, and even extend as needed. Gain insights into how your model performs against test data from your evaluation set and delve into essential metrics, exploring their relationships to specific examples, which can highlight difficult use cases, guide further data collection efforts, and uncover opportunities to enhance model quality. Additionally, if you want to elevate your training performance, you can integrate an external graphics processing unit with your Mac. Experience the lightning-fast training capabilities available on your Mac that leverage both CPU and GPU resources, and take your pick from a diverse selection of model types offered by Create ML. This tool not only streamlines the training process but also empowers users to maximize the effectiveness of their machine learning endeavors.
Description
Gensim is an open-source Python library that specializes in unsupervised topic modeling and natural language processing, with an emphasis on extensive semantic modeling. It supports the development of various models, including Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), which aids in converting documents into semantic vectors and in identifying documents that are semantically linked. With a strong focus on performance, Gensim features highly efficient implementations crafted in both Python and Cython, enabling it to handle extremely large corpora through the use of data streaming and incremental algorithms, which allows for processing without the need to load the entire dataset into memory. This library operates independently of the platform, functioning seamlessly on Linux, Windows, and macOS, and is distributed under the GNU LGPL license, making it accessible for both personal and commercial applications. Its popularity is evident, as it is employed by thousands of organizations on a daily basis, has received over 2,600 citations in academic works, and boasts more than 1 million downloads each week, showcasing its widespread impact and utility in the field. Researchers and developers alike have come to rely on Gensim for its robust features and ease of use.
API Access
Has API
No
API Access
Has API
Yes
Integrations
C
No
Cython
No
NumPy
No
Python
No
fastText
No
word2vec
No
Integrations
C
Yes
Cython
Yes
NumPy
Yes
Python
Yes
fastText
Yes
word2vec
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Apple
Country
United States
Website
developer.apple.com/machine-learning/create-ml/
Vendor Details
Company Name
Radim Řehůřek
Founded
2009
Country
Czech Republic
Website
radimrehurek.com/gensim/
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Product Features
Natural Language Processing
Co-Reference Resolution
No
In-Database Text Analytics
No
Named Entity Recognition
No
Natural Language Generation (NLG)
No
Open Source Integrations
No
Parsing
No
Part-of-Speech Tagging
No
Sentence Segmentation
No
Stemming/Lemmatization
No
Tokenization
No