Module Two - Languages of Data Science

Question 1

Name some key features of Caffe framework

Answer 1

High Performance: It can process over 60 million images per day on a single GPU.

Focus on Convolutional Neural Networks (CNNs) which are commonly used in image-related tasks like classification, detection, and segmentation.

Layer-Based Architecture: Models in Caffe are built as a series of layers, with each layer representing a specific operation (e.g., convolution, pooling, activation).

Cross-Platform Compatibility

Question 2

Python libraries: Pandas

Answer 2

Pandas is an open-source Python library that provides data structures and data analysis tools, primarily for manipulating and analyzing structured data in the form of Data Frames and Series.

Question 3

TensorFlow

Answer 3

TensorFlow is an open-source machine learning framework developed by Google that facilitates the creation, training, and deployment of deep learning models through a flexible and comprehensive ecosystem of tools and libraries.

Question 4

Why is SQL an American National Standards Institute (or AN-see) standard?

Answer 4

SQL is an American National Standards Institute (or AN-see) standard, which means if you learn SQL and use it with one database, you can apply your SQL knowledge to many other databases.

Question 5

What is the WEKA software suite intended for?

Answer 5

WEKA (Waikato Environment for Knowledge Analysis) is a popular open-source software suite for data mining and machine learning. Developed at the University of Waikato in New Zealand, WEKA provides a collection of algorithms and tools for various data analysis tasks, making it a widely used tool in both academic and industrial settings.

Question 6

Python libraries: SciPy

Answer 6

SciPy is an open-source Python library used for scientific and technical computing, offering a wide range of functionalities for optimization, integration, interpolation, eigenvalue problems, and other advanced mathematical operations built on the NumPy library.

Question 7

Python libraries: NumPy

Answer 7

NumPy is a powerful open-source Python library for numerical computing that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Question 8

Python libraries: PyTorch

Answer 8

PyTorch is an open-source machine learning library for Python that provides a flexible framework for building and training deep learning models using dynamic computation graphs and tensor operations.

Question 9

Can Python be used for NLP?

Answer 9

Yes. Python can also be used for Natural Language Processing (NLP) using the Natural Language Toolkit (NLTK).

Question 10

What are the main characteristics of the Julia language?

Answer 10

Julia is a compiled language designed in MIT for high-performance numerical analysis and computational science.

Julia provides speedy development like Python or R, while producing programs that run as fast as C or Fortran programs.

It’s compiled which means that Julia code is executed directly on the processor as executable code.

It calls C, Go, Java, MATLAB, R, Fortran, and Python libraries, and has refined parallelism.

Question 11

What is JuliaDB?

Answer 11

Developed with Julia, JuliaDB is a Data Science package for working with large persistent data sets.

Question 12

Who are the typical users of R Lang?

Answer 12

Statisticians, mathematicians, and data miners use R to develop statistical software, graphing, and data analysis.

R Language’s array-oriented syntax makes it easier to translate from math to code for learners with no or minimal programming background.

Question 13

Name some frequently used Python libraries for Data Science

Answer 13

For data science, you can use Python’s scientific computing libraries like Pandas, NumPy, SciPy, and Matplotlib.

Question 14

Why is SQL different from other software development languages?

Answer 14

SQL is different from other software development languages because it is a non-procedural language.
SQL stands for Structured Query Language.
It was designed for managing data in relational databases.
SQL is an ANSI standardized language.
If you learn SQL and use it with one database, you can apply your SQL knowledge with many other databases easily.

Question 15

As of 2024, which other deep learning framework have superseded Caffe?

Answer 15

TensorFlow and PyTorch have largely superseded it for broader machine learning applications.

While Caffe is great for CNNs and computer vision, it lacks the flexibility and ease of use for other types of deep learning models like RNNs, which are more easily handled by frameworks like TensorFlow and PyTorch.

Question 16

What is Caffe framework and what is intended for?

Answer 16

Caffe (short for Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC). It is primarily designed for speed and efficiency in training and deploying deep learning models, especially in computer vision tasks like image classification and object detection.

Question 17

What are some typical applications for Caffe?

Answer 17

Image Classification: Training models to classify images into categories.

Object Detection: Identifying and localizing objects within images.

Segmentation: Dividing an image into segments or regions of interest.

Feature Extraction: Using pre-trained networks to extract meaningful features from images for downstream tasks.

Question 18

Difference between Open-Source and Free Software Foundation

Answer 18

The Open-Source Initiative (OSI) champions open source, while the Free Software Foundation (FSF) defines free software. Open source is more business focused, while free software is more focused on a set of values.

Question 19

Python libraries: Matplotlib

Answer 19

Matplotlib is a widely used open-source plotting library for Python that provides a flexible interface for creating static, animated, and interactive visualizations in a variety of formats and styles.

Question 20

Scala strengths for Data Science

Answer 20

Scala offers strong support for functional programming, seamless integration with big data frameworks like Apache Spark, and concise syntax, making it a powerful choice for building scalable and efficient data science applications.
It is also inter-operable with Java as it runs on the JVM.

Question 21

Advantages of using Java for Data Science

Answer 21

Java provides strong performance, portability, and a rich ecosystem of libraries and frameworks, making it suitable for building scalable, robust data science applications, particularly in enterprise environments.

Question 22

Examples of notable data science tools built with Java

Answer 22

Weka for data mining, Java-ML for machine learning, Apache
MLlib makes machine learning scalable, and Deeplearning4 for deep learning.

Hadoop is another application of Java which manages data processing and storage for big data applications running in clustered systems.

Question 23

R programming language

Answer 23

R is a programming language and software environment primarily used for statistical computing and data analysis, featuring a wide array of packages for data visualization, statistical modeling, and data manipulation.

Question 24

Python programming language

Answer 24

Python is a high-level, interpreted programming language known for its readability, versatility, and extensive libraries, making it popular for web development, data analysis, artificial intelligence, scientific computing, and automation.