Frameworks and Libs

Question 1

Caffe2

Answer 1

Open source deep learning framework

Question 2

MLib (Spark)

Answer 2

MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as:

ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
Featurization: feature extraction, transformation, dimensionality reduction, and selection
Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
Persistence: saving and load algorithms, models, and Pipelines
Utilities: linear algebra, statistics, data handling, etc.

Question 3

Weka

Answer 3

Weka is used for visual data mining and machine learning software in Java.

Question 4

Rattle

Answer 4

Rattle is the R analytical tool that gets you started with data analytics and machine learning.

Question 5

TensorFlow

Answer 5

TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.

Question 6

Scikit-learn / sklearn

Answer 6

Scikit-learn is a collection of advanced machine-learning algorithms for Python. It also is built upon Numpy and SciPy.

Scikit-learn is one of the most useful library for machine learning in Python. It builds is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Question 7

Numpy

Answer 7

NumPy is a library for efficient array computations, modeled after Matlab

Question 8

Microsoft Cognitive Toolkit (CNTK)

Answer 8

The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

Question 9

PyTorch

Answer 9

PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.

Question 10

Anaconda

Answer 10

Python Distribution

Question 11

SciPy

Answer 11

SciPy provides a large menu of libraries for scientific computation, such as integration, interpolation, signal processing, linear algebra, statistics, etc. It is built upon the infrastructure of Numpy.

Question 12

Pandas

Answer 12

Pandas library is good for analyzing tabular data. You can use it for exploratory data analysis, statistics, visualization.