Amir Ali | Warsaw University of Technology (original) (raw)

Teaching Documents by Amir Ali

Research paper thumbnail of A Deep Level Understanding of Auto Encoders with Practical Implementation in Pytorch

Auto Encoders, 2019

One of the main issues with machine learning is too many input variables when going from raw data... more One of the main issues with machine learning is too many input variables when going from raw data. Too many numbers of inputs make it hard to train the models and often can lead to overfitting. Autoencoders were originally proposed as a method of reducing dimensions and extracting higher-level features that we know for sure contain most if not all of the information. It can be thought of as a method for storing the information in a compressed, more efficient and meaningful way.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Restricted Boltzmann Machine with Practical Implementation in Pytorch

Restricted Boltzmann Machine, 2019

A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that ca... more A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986 and rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000. RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning, topic modeling, and even many-body quantum mechanics. They can be trained in either supervised or unsupervised ways, depending on the task.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Self Organizing Maps with Practical Implementation in Minisom

Self Organizing Maps, 2019

The Self Organizing Map is one of the most popular neural models. It belongs to the category of t... more The Self Organizing Map is one of the most popular neural models. It belongs to the category of the competitive learning network. The SOM is based on unsupervised learning, which means that is no human intervention is needed during the training and those little needs to be known about characterized by the input data. We could, for example, use the SOM for clustering membership of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM the Self Origination Feature Map.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Recurrent Neural Network & LSTM with Practical Implementation in Keras & Tensorflow

Recurrent Neural Network & LSTM, 2019

A recurrent neural network is a class of artificial neural networks where connections between nod... more A recurrent neural network is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state to process sequences of inputs.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Convolutional Neural Network with Practical Implementation in Keras & Tensorflow

Convolutional Neural Network, 2019

Convolutional neural networks. Sounds like a weird combination of biology and math with a little ... more Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year’s ImageNet competition (basically, the annual Olympics of computer vision), dropping the classification error record from 26% to 15%, an astounding improvement at the time. Ever since then, a host of companies have been using deep learning at the core of their services. Facebook uses neural nets for their automatic tagging algorithms, Google for their photo search, Amazon for their product recommendations, Pinterest for their home feed personalization, and Instagram for their search infrastructure.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Artificial Neural Network with Practical Implementation in Keras & Tensorflow

Artificial Neural Network, 2019

Artificial neural networks are one of the main tools used in machine learning. As the “neural” pa... more Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems that are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Linear Discriminant Analysis (LDA) with Practical Implementation in Scikit Learn

Linear Discriminant Analysis (LDA), 2019

LDA is a type of Linear combination, a mathematical process using various data items and applyin... more LDA is a type of Linear combination, a mathematical process using various data items and applying a function to that site to separately analyze multiple classes of objects or items.
Following Fisher's Linear discriminant, linear discriminant analysis can be useful in areas like image recognition and predictive analysis in marketing. The fundamental idea of linear combinations goes back as far as the 1960s with the Altman Z-scores for bankruptcy and other predictive constructs. Now LDA helps in preventative data for more than two classes, when Logistics Regression is not sufficient. The linear Discriminant analysis takes the mean value for each class and considers variants in order to make predictions assuming a Gaussian distribution. Maximizing the component axes for class-separation.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Principal Component Analysis (PCA) with Practical Implementation in Scikit Learn

Principal Component Analysis (PCA), 2019

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformat... more Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Hierarchical Cluster with Practical Implementation in Scikit Learn

Hierarchical cluster, 2019

Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups... more Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of K-Mean Cluster with Practical Implementation in Scikit Learn

K-Mean Cluster, 2019

K-Means clustering aims to partition n observation into k clusters in which each observation belo... more K-Means clustering aims to partition n observation into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Eclat Algorithm with Practical Implementation

Eclat Algorithm, 2019

The ECLAT algorithm stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It i... more The ECLAT algorithm stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It is one of the popular methods of Association Rule mining.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Apriori Algorithm with Practical Implementation

Apriori Algorithm, 2019

Apriori is an algorithm for frequent itemset mining and association rule learning over relational... more Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Support Vector Machine with Practical Implementation in Scikit Learn

Support Vector Machine, 2019

A Support Vector Machine (SVM) is a discriminative classifier that can be used for both classific... more A Support Vector Machine (SVM) is a discriminative classifier that can be used for both classification and regression problems. The goal of SVM is to identify an optimal separating hyperplane which maximizes the margin between different classes of the training data. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples to create the largest possible distance to reduce an upper bound. Supports Vectors are simply the coordinates of data points that are nearest to the optimal separating hyperplane provide the most useful information for SVM classification. In addition, an appropriate kernel function is used to transform the data into a high-dimension to use linear discriminate functions.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Logistic Regression with Practical Implementation in Scikit Learn

Logistic Regression, 2019

Logistic Regression is a Statistical method for analyzing a dataset in which there are one or mor... more Logistic Regression is a Statistical method for analyzing a dataset in which there are one or more independent variables that determine the outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of K-Nearest Neighbor with Practical Implementation in Scikit Learn

K-Nearest Neighbor, 2019

kNN is one of the simplest classification algorithms and it is one of the most used learning algo... more kNN is one of the simplest classification algorithms and it is one of the most used learning algorithms. kNN falls in the supervised learning family of an algorithm. Informally this means that we are given a labelled dataset consisting of training observation (x,y) & would like to capture the relationship between x and y more informally our goal is to learn a function h: X implies to Y so that given an unseen observation x, h(x) can confidently predict the corresponding output y.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Random Forest with Practical Implementation in Scikit Learn

Random Forest, 2019

Random Forrest is a supervised algorithm used for both classification and regression problems too... more Random Forrest is a supervised algorithm used for both classification and regression problems too. We can see it from a supervised algorithm to create a forest in some way & make it random. The larger the number of trees the more accurate results.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Decision Tree with Practical Implementation in Scikit Learn

Decision Tree, 2019

A decision tree is a largely used non-parametric effective machine learning modeling technique fo... more A decision tree is a largely used non-parametric effective machine learning modeling technique for regression and classification problems. To find solutions a decision tree makes a sequential, hierarchical decision about the outcomes variable based on the predictor data.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Naïve Bayes Classifier with Practical Implementation in Scikit Learn

Naive Bayes, 2019

Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), a... more Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assumes that the effect of the value of a predictor (x) on a given class © is independent of the values of other predictors. This assumption is called class conditional independence.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Linear Regression with Practical Implementation in Scikit Learn

Linear Regression, 2019

Simple linear regression is a type of regression analysis where the number of independent variabl... more Simple linear regression is a type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variable.

Bookmarks Related papers MentionsView impact

Papers by Amir Ali

Research paper thumbnail of HAN TO PREDICT CUISINE FROM RECIPES INGREDIENTS

International Research Journal of Modernization in Engineering Technology and Science, 2022

Cuisine is an important aspect of a recipe, as it influences the flavors and techniques used in t... more Cuisine is an important aspect of a recipe, as it influences the flavors and techniques used in the preparation of a dish. Predicting cuisine from recipe ingredients is a problem that involves classifying a recipe into one of many possible cuisines based on the ingredients it contains. This is a challenging task because there are many different cuisines in the world, each with its own unique set of ingredients and flavors. To accurately predict the cuisine of a recipe, a model must be able to learn the characteristics of each cuisine and use them to identify the correct cuisine based on the ingredients of the recipe. In this study, we implement different deep learning techniques, such as fastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, and HAN, to the task of accurately predicting cuisine from ingredients. Our results showed that all of the techniques gave a good performance, but HAN achieved the highest accuracy, at 87%.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Auto Encoders with Practical Implementation in Pytorch

Auto Encoders, 2019

One of the main issues with machine learning is too many input variables when going from raw data... more One of the main issues with machine learning is too many input variables when going from raw data. Too many numbers of inputs make it hard to train the models and often can lead to overfitting. Autoencoders were originally proposed as a method of reducing dimensions and extracting higher-level features that we know for sure contain most if not all of the information. It can be thought of as a method for storing the information in a compressed, more efficient and meaningful way.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Restricted Boltzmann Machine with Practical Implementation in Pytorch

Restricted Boltzmann Machine, 2019

A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that ca... more A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986 and rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000. RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning, topic modeling, and even many-body quantum mechanics. They can be trained in either supervised or unsupervised ways, depending on the task.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Self Organizing Maps with Practical Implementation in Minisom

Self Organizing Maps, 2019

The Self Organizing Map is one of the most popular neural models. It belongs to the category of t... more The Self Organizing Map is one of the most popular neural models. It belongs to the category of the competitive learning network. The SOM is based on unsupervised learning, which means that is no human intervention is needed during the training and those little needs to be known about characterized by the input data. We could, for example, use the SOM for clustering membership of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM the Self Origination Feature Map.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Recurrent Neural Network & LSTM with Practical Implementation in Keras & Tensorflow

Recurrent Neural Network & LSTM, 2019

A recurrent neural network is a class of artificial neural networks where connections between nod... more A recurrent neural network is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state to process sequences of inputs.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Convolutional Neural Network with Practical Implementation in Keras & Tensorflow

Convolutional Neural Network, 2019

Convolutional neural networks. Sounds like a weird combination of biology and math with a little ... more Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year’s ImageNet competition (basically, the annual Olympics of computer vision), dropping the classification error record from 26% to 15%, an astounding improvement at the time. Ever since then, a host of companies have been using deep learning at the core of their services. Facebook uses neural nets for their automatic tagging algorithms, Google for their photo search, Amazon for their product recommendations, Pinterest for their home feed personalization, and Instagram for their search infrastructure.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Artificial Neural Network with Practical Implementation in Keras & Tensorflow

Artificial Neural Network, 2019

Artificial neural networks are one of the main tools used in machine learning. As the “neural” pa... more Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems that are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Linear Discriminant Analysis (LDA) with Practical Implementation in Scikit Learn

Linear Discriminant Analysis (LDA), 2019

LDA is a type of Linear combination, a mathematical process using various data items and applyin... more LDA is a type of Linear combination, a mathematical process using various data items and applying a function to that site to separately analyze multiple classes of objects or items.
Following Fisher's Linear discriminant, linear discriminant analysis can be useful in areas like image recognition and predictive analysis in marketing. The fundamental idea of linear combinations goes back as far as the 1960s with the Altman Z-scores for bankruptcy and other predictive constructs. Now LDA helps in preventative data for more than two classes, when Logistics Regression is not sufficient. The linear Discriminant analysis takes the mean value for each class and considers variants in order to make predictions assuming a Gaussian distribution. Maximizing the component axes for class-separation.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Principal Component Analysis (PCA) with Practical Implementation in Scikit Learn

Principal Component Analysis (PCA), 2019

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformat... more Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Hierarchical Cluster with Practical Implementation in Scikit Learn

Hierarchical cluster, 2019

Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups... more Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of K-Mean Cluster with Practical Implementation in Scikit Learn

K-Mean Cluster, 2019

K-Means clustering aims to partition n observation into k clusters in which each observation belo... more K-Means clustering aims to partition n observation into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Eclat Algorithm with Practical Implementation

Eclat Algorithm, 2019

The ECLAT algorithm stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It i... more The ECLAT algorithm stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It is one of the popular methods of Association Rule mining.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Apriori Algorithm with Practical Implementation

Apriori Algorithm, 2019

Apriori is an algorithm for frequent itemset mining and association rule learning over relational... more Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Support Vector Machine with Practical Implementation in Scikit Learn

Support Vector Machine, 2019

A Support Vector Machine (SVM) is a discriminative classifier that can be used for both classific... more A Support Vector Machine (SVM) is a discriminative classifier that can be used for both classification and regression problems. The goal of SVM is to identify an optimal separating hyperplane which maximizes the margin between different classes of the training data. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples to create the largest possible distance to reduce an upper bound. Supports Vectors are simply the coordinates of data points that are nearest to the optimal separating hyperplane provide the most useful information for SVM classification. In addition, an appropriate kernel function is used to transform the data into a high-dimension to use linear discriminate functions.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Logistic Regression with Practical Implementation in Scikit Learn

Logistic Regression, 2019

Logistic Regression is a Statistical method for analyzing a dataset in which there are one or mor... more Logistic Regression is a Statistical method for analyzing a dataset in which there are one or more independent variables that determine the outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of K-Nearest Neighbor with Practical Implementation in Scikit Learn

K-Nearest Neighbor, 2019

kNN is one of the simplest classification algorithms and it is one of the most used learning algo... more kNN is one of the simplest classification algorithms and it is one of the most used learning algorithms. kNN falls in the supervised learning family of an algorithm. Informally this means that we are given a labelled dataset consisting of training observation (x,y) & would like to capture the relationship between x and y more informally our goal is to learn a function h: X implies to Y so that given an unseen observation x, h(x) can confidently predict the corresponding output y.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Random Forest with Practical Implementation in Scikit Learn

Random Forest, 2019

Random Forrest is a supervised algorithm used for both classification and regression problems too... more Random Forrest is a supervised algorithm used for both classification and regression problems too. We can see it from a supervised algorithm to create a forest in some way & make it random. The larger the number of trees the more accurate results.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Briefly Explanation of Decision Tree with Practical Implementation in Scikit Learn

Decision Tree, 2019

A decision tree is a largely used non-parametric effective machine learning modeling technique fo... more A decision tree is a largely used non-parametric effective machine learning modeling technique for regression and classification problems. To find solutions a decision tree makes a sequential, hierarchical decision about the outcomes variable based on the predictor data.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Intuitive Guide of Naïve Bayes Classifier with Practical Implementation in Scikit Learn

Naive Bayes, 2019

Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), a... more Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assumes that the effect of the value of a predictor (x) on a given class © is independent of the values of other predictors. This assumption is called class conditional independence.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Deep Level Understanding of Linear Regression with Practical Implementation in Scikit Learn

Linear Regression, 2019

Simple linear regression is a type of regression analysis where the number of independent variabl... more Simple linear regression is a type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variable.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of HAN TO PREDICT CUISINE FROM RECIPES INGREDIENTS

International Research Journal of Modernization in Engineering Technology and Science, 2022

Cuisine is an important aspect of a recipe, as it influences the flavors and techniques used in t... more Cuisine is an important aspect of a recipe, as it influences the flavors and techniques used in the preparation of a dish. Predicting cuisine from recipe ingredients is a problem that involves classifying a recipe into one of many possible cuisines based on the ingredients it contains. This is a challenging task because there are many different cuisines in the world, each with its own unique set of ingredients and flavors. To accurately predict the cuisine of a recipe, a model must be able to learn the characteristics of each cuisine and use them to identify the correct cuisine based on the ingredients of the recipe. In this study, we implement different deep learning techniques, such as fastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, and HAN, to the task of accurately predicting cuisine from ingredients. Our results showed that all of the techniques gave a good performance, but HAN achieved the highest accuracy, at 87%.

Bookmarks Related papers MentionsView impact