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12 Dec
0

Auto Encoders

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Auto Encoders are used for reconstruction of input data that can be image, vectors etc. It is a very trending topic and has many applications like In Removing Watermarks, Constructing blurred Image into Clear Image etc.  

12 Dec
0

Tutorial on GenSim, a tool for Topic Modelling (Part 1)

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Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.  

12 Dec
0

Tutorial on GenSim, a tool for Topic Modelling (Part 2)

In Artificial Intelligence | No comment

Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.  

12 Dec
0

Tutorial on GenSim, a tool for Topic Modelling (Part 3)

In Artificial Intelligence | No comment

Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.  

12 Dec
0

Tutorial on GenSim, a tool for Topic Modelling (Part 4)

In Artificial Intelligence | No comment

Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.  

12 Dec
0

Deepy Installation

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Deepy is a deep learning framework for designing models with complex architectures. Many important components such as LSTM and Batch Normalization are implemented inside. Although highly flexible, deepy maintains a clean high-level interface.  

12 Dec
0

Keras based upon the tensor flow

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Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer.  

12 Dec
0

PyML

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PyML is an interactive object oriented framework for machine learning written in Python. PyML is focused on kernel-methods for classification and regression, including Support Vector Machines (SVM). It provides tools for feature selection, model selection, syntax for combining classifiers and methods for assessing classifier performance.  

12 Dec
0

Pylearn 2 part4

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Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).  

12 Dec
0

Pylearn 2 part3

In Artificial Intelligence | No comment

Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).  

12 Dec
0

pylearn2 part2

In Artificial Intelligence | No comment

Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).  

12 Dec
0

Pylearn2 part1

In Artificial Intelligence | No comment

Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).  

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