What is LSTM? You might have heard this term in the last interview you gave for a Machine Learning Engineer position, or some of your friends might have mentioned using LSTM in their predictive modelling projects. So the big question that may arise here is what is LSTM, the purpose…

Last year in November of 2020 I wrote an article on the Top five Generative Adversarial Networks projects for computer science students and aspirants. Thankfully you peeps loved it a lot and showed huge respect. Here is the link to that article in case you missed it: Top 5 GAN(Generative Adversarial Networks) Projects for Final Year Computer Science Students. — In today's article, we are going to review some really good Generative Adveresial Projects who are still in deployment in 2021.

A recommender system, or a recommendation system, is a subclass of information filtering systems that seeks to predict the “rating” or “preference” a user would give to an item. They are primarily used in commercial applications. Wikipedia — There are majorly six types of recommender systems that work primarily in the Media and Entertainment industry: Collaborative Recommender system, Content-based recommender system,

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, and big data. Wikipedia — There are a variety of tools for visualizations, languages, frameworks, platforms, and technologies that form the skeletal structure of Data Science, we will not explain to them as it is not in the scope of this article but I will link each of the sub-topics with the links to their…

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Wikipedia — In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification, and regression.

Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that the machine is forced to build a compact internal representation of its world through mimicry. Wikipedia — Unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of the area required, but you must be able to identify and label classes after the classification.

Automated machine learning is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. Wikipedia — What Does AutoML do? Automated machine learning, or AutoML, aims to reduce or eliminate the need for skilled data scientists to build machine learning and deep learning models. Instead, an AutoML system allows you to provide the labelled training data as input and receive an optimized model as output.

TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but focuses on the training and inference of deep neural networks. Tensorflow is a symbolic math library based on dataflow and differentiable programming. Wikipedia — In machine learning, a model is a function with learnable parameters that maps an input to an output. The optimal parameters are obtained by training the model on data. A well-trained model will provide an accurate mapping from the input to the desired output.tensorflow.org

Sentiment analysis refers to natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and personal information. Wikipedia — Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative, or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback and understand customer needs.

Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Wikipedia — Natural Language Processing explains that in NLP, machines are taught to read and interpret the text as humans do. NLP is recognized as the “enabler of text analysis and speech-recognition applications.” This human capability for interpreting text comes in handy for analyzing large volumes of text data. …

Object detection is a computer technology related to computer vision and image processing that detects instances of semantic objects of a certain class in digital images and videos. — In today’s article, we will talk about Six of the open-source custom Object Detection projects to enhance your skills in the field of computer vision and image processing. Note: In this article, we are going to talk about some of the not-so-famous but really good open-source custom object detection projects…

Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Wikipedia — Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. — Keras is a high-level interface and uses Theano or Tensorflow for its backend. It runs smoothly on both CPU and GPU. Keras supports almost all the models of a neural network — fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.

Human 3D pose estimation is one of the most talked-about or the hottest in recent years and even the future. Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. — In this post, you will discover 5 of the best open-source Computer Vision Human-Pose Estimation projects. We will review five of the best open-source Computer Vision Human-Pose Estimation projects available on Github for anyone interested to use it.

Machine Learning can be an astonishingly valuable tool to reveal unknown insights and predict forthcoming trends. In today’s post, you will discover machine learning, why Python is popularly accepted for machine learning, several machine learning methods & all the tools you require to get started with machine learning with Python. — Outline Intro To Machine Learning Why Python For Machine Learning

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process single data points (such as images) and entire sequences of data (such as speech or video). For example, LSTM applies to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems).Wikipedia

The CycleGAN is a technique that involves the automatic training of image-to-image translation models without paired examples. The models are trained in an unsupervised manner using a collection of images from the source and target domain that do not need to be related in any way. This simple technique is powerful, achieving visually impressive results on various application domains, most notably translating photographs of horses to zebra and the reverse.

Python is an interpreted, high-level and general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach help programmers write clear, logical code for small and large-scale projects. Wikipedia — Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (mainly procedural), object-oriented, and functional programming. Python is often described as a “batteries included” language due to its comprehensive standard library.

YOLO (“You Only Look Once”) is an effective real-time object recognition algorithm, first described in the seminal 2015 paper by Joseph Redmon et al. — In this article, we introduce the concept of object detection, the YOLO algorithm itself, and one of the algorithm’s open-source implementations. We will review five of the best open-source YOLO Machine Learning projects available. Having good theoretical knowledge is amazing but implementing them in code in a real-time machine learning…

This article will demonstrate the practical application of correlation by comparing two separate correlation coefficients, i.e. Pearson correlation coefficient and Spearman correlation coefficient, and discern whether they will proffer the equivalent strength or deviation. — Index · What is Correlation · Pearson vs Spearman correlation · Functional application of correlation applying R · Inference