##### Convolutional Neural Networks for Image Classification

In the ’90s to perform complex operations the most powerful algorithm available is Support Vector Machines ( SVM ). Back then there was no sufficient computational power. Since 2006 several

In the ’90s to perform complex operations the most powerful algorithm available is Support Vector Machines ( SVM ). Back then there was no sufficient computational power. Since 2006 several

Broadcasting in Numpy refers to the functionality provided by NumPy to carry out arithmetic operations on ndarrays having different dimensions. The arithmetic operations on arrays are normally done on corresponding

A Set is a collection of well-defined and distinct elements. Sets are one of the fundamental concepts in mathematics. The NumPy package provides the following functions to perform set operations on the array.

The NumPy package provides functions for reading and writing the arrays as text files as well as in a simple numpy binary format(.npy). loadtxt() and savetxt(): The loadtxt() function of

A random number is a number from a sequence or a distribution, whose future occurrence cannot be predicted based on any past or present data over a defined interval or

As we had discussed in the introductory article, Machine Learning, Artificial Intelligence, and NLP are interlinked together. We need to know Machine Learning if we efficiently want to solve NLP

A dictionary is a collection of key-value pairs that are ordered, mutable, and indexed by keys. The key-value pairs in a dictionary are enclosed within the curly braces {} and are separated by commas. A colon(:) separates the key from its corresponding value

In the previous article on fastText, we had seen how to build a fastText model. In this article, we will use the same concept of the fastText model and build

Neural Networks is a component of Artificial intelligence and is composed of artificial neuron and nodes that are meant to stimulate the functioning of a human brain. Using algorithms they

Table of Contents Show / Hide 1. Sorting functions1.1. sort():1.2. argsort():1.3. lexsort():1.4. sort_complex():2. Searching functions2.5. argmax():2.6. argmin():2.7. where():2.8. extract(): Sorting functions The NumPy package provides various functions to perform sorting

The NumPy package provides several functionalities to perform statistical operations on the array elements. amin() amax() nanmin() nanmax() ptp() percentile() nanpercentile() quantile() nanquantile() median() average() mean() std() var() nanmean() nanmedian()

Table of Contents Show / Hide 1. String Operations1.1. add():1.2. multiply():1.3. center():1.4. CaSe Handling:1.5. join():1.6. partition():1.7. replace():1.8. split():1.9. strip(), rstrip() and lstrip():1.10. zfill():1.11. encode() and decode():2. String Comparisons2.12. compare_chararrays():3. String

Table of Contents Show / Hide 1. Linear Algebra functions1.1. dot():1.2. multi_dot():1.3. vdot():1.4. inner():1.5. outer():1.6. matmul():1.7. matrix_power():1.8. eig():1.9. eigh():1.10. det():1.11. matrix_rank():1.12. trace():1.13. solve():1.14. inv():2. Matrix functions2.15. mat():2.16. eye():2.17. identity():2.18. repmat():2.19.

A list is an inbuilt data structure in python, that stores a sequence of data items. A list can hold elements with different data types.

Table of Contents Show / Hide 1. Rounding functions:1.1. around():1.2. rint():1.3. fix():1.4. floor():1.5. ceil():1.6. trunc():2. Some commonly used functions:2.7. sum():2.8. prod():2.9. cumsum():2.10. cumprod():2.11. fmax():2.12. fmin():2.13. lcm():2.14. gcd():2.15. square():2.16. sqrt():2.17. cbrt():2.18.

Table of Contents Show / Hide 1. Trigonometric Functions:1.1. sin():1.2. cos():1.3. tan():1.4. arcsin():1.5. arccos():1.6. arctan():1.7. hypot():1.8. degrees():1.9. radians():2. Hyperbolic Functions:2.10. sinh():2.11. cosh():2.12. tanh():2.13. arcsinh():2.14. arccosh():2.15. arctanh(): Trigonometric Functions: The trigonometric

In previous articles, we have discussed and built models for word embeddings and for document representations. The models we had trained were Word2Vec models and Doc2Vec models. In this article,

In the previous articles, we have seen how to generate vectors for words in the form of word embeddings. For that task, we had used the Word2Vec model. But what

Object Tracking allows us to identify the objects and locate objects in the image or a video. Object tracking can detect multiple objects in an image or video. In object

In a previous article, we had discussed and implemented the cosine similarity. With the help of cosine similarity, we were able to know if the two documents were similar or

An array is a collection of objects of the same data type stored at the contiguous memory location. An array helps us to store multiple items of the same type together.

Recursion breaks the problem into smaller ones and is easier to use. In recursion, the same operation is performed multiple times with the smaller inputs to make the problem smaller

In the previous article, we learned about word embeddings and saw a glimpse of the Word2Vec model. If you recall, we had used an already trained model by Google which

The NumPy package has several dedicated classes and modules to provide mathematical functions and operations for numpy arrays. Table of Contents Show / Hide 1. Arithmetic operations:1.1. add(), subtract(), multiply()

Face recognition is a machine learning technology where a human face in a digital image or a frame from a video will be matched against a database of images and

Before diving into word embeddings we see the difference between syntax and semantics in NLP. Table of Contents Show / Hide 1. Syntax vs Semantics in NLP2. Word embeddings3. How

NumPy package provides several functions for the manipulation of the array and its elements. These functions can be classified broadly as: Changing Array Shapes Tranpose Operations Changing Array Dimensions Joining

A chatbot is one of the most important applications of Natural Language Processing. In the introductory article, we had discussed chatbots in brief. Chatbots are growing immensely in popularity so

The elements of a NumPy ndarray can be accessed and modified by indexing and slicing similar to Python’s in-built data structures. Table of Contents Show / Hide 1. Indexing1.0.1. Accessing

In the previous two articles, we discussed two algorithms by which we convert text into mathematical representations. After converting the text into a suitable mathematical form, how can we know