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GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON### MCCORMICKML.COM

K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOR Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTS 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON### MCCORMICKML.COM

GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON### MCCORMICKML.COM

K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOR Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

TUTORIALS · CHRIS MCCORMICK Radial Basis Function Networks. I’ve written a number of posts related to Radial Basis Function Networks. Together, they can be taken as a multi-part tutorial to RBFNs. Part 1 - RBFN Basics, RBFNs for Classification. Part 2 - RBFN Example Code in Matlab. Part 3 - RBFN for function approximation. MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection MinHash Tutorial with Python Code 12 Jun 2015. In this post, I’m providing a brief tutorial, along with some example Python code, for applying the MinHash algorithm to compare a large number of documents to one another efficiently. THE GAUSSIAN KERNEL · CHRIS MCCORMICK The Gaussian function is based on the squared Euclidean distance. Note that squaring the Euclidean distance is the same as just removing the square root term. This leads to the (x - mu)^2 term in the equation for the one dimensional Gaussian. For a one-dimensional input, the squared Euclidean distance is just the parabola y = x^2. DEEP LEARNING TUTORIAL Autoencoder - By training a neural network to produce an output that’s identical to the input, but having fewer nodes in the hidden layer than in the input, you’ve built a tool for compressing the data. Going from the input to the hidden layer is the compression step. You take, e.g., a 100 element vector and compress it to a 50### element vector.

GAUSSIAN MIXTURE MODELS TUTORIAL AND MATLAB CODE · CHRIS Gaussian Mixture Models Tutorial and MATLAB Code. 04 Aug 2014. You can think of building a Gaussian Mixture Model as a type of clustering algorithm. Using an iterative technique called Expectation Maximization, the process and result is very similar to k-means clustering. The difference is that the clusters are assumed to each### have an

### SVM TUTORIAL

SVM Tutorial - Part I. 16 Apr 2013. I found it really hard to get a basic understanding of Support Vector Machines. To learn how SVMs work, I ultimately went through Andrew Ng’s Machine Learning course (available freely from Stanford). I think the reason SVM tutorials are so challenging is that training an SVM is a complex optimization WORD2VEC TUTORIAL PART 2 Sampling rate. The word2vec C code implements an equation for calculating a probability with which to keep a given word in the vocabulary. w i is the word, z ( w i) is the fraction of the total words in the corpus that are that word. For example, if the word “peanut” occurs 1,000 times in a 1 billion word corpus, then z (‘peanut’) = 1E-6. DEEP LEARNING TUTORIAL Whitening has two simple steps: Project the dataset onto the eigenvectors. This rotates the dataset so that there is no correlation between the components. Normalize the the dataset to have a variance of 1 for all components. This is done by simply dividing each WHAT IS AN L2-SVM? · CHRIS MCCORMICK Support vector machines with linear sum of slack variables, which are commonly used, are called L1-SVMs, and SVMs with the square sum of slack variables are called L2-SVMs. It’s really just a slight difference in the objective function used to optimize the SVM. The objective for an L1-SVM is: The difference is in the regularization### term

RADIAL BASIS FUNCTION NETWORK (RBFN) TUTORIAL · CHRIS 15 Aug 2013. A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier. Generally, when people talk about neural networks or “Artificial Neural Networks” they are referring to the Multilayer Perceptron (MLP). Each neuron in an MLP takes the CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

TUTORIALS · CHRIS MCCORMICK Radial Basis Function Networks. I’ve written a number of posts related to Radial Basis Function Networks. Together, they can be taken as a multi-part tutorial to RBFNs. Part 1 - RBFN Basics, RBFNs for Classification. Part 2 - RBFN Example Code in Matlab. Part 3 - RBFN for function approximation. MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection MinHash Tutorial with Python Code 12 Jun 2015. In this post, I’m providing a brief tutorial, along with some example Python code, for applying the MinHash algorithm to compare a large number of documents to one another efficiently. THE GAUSSIAN KERNEL · CHRIS MCCORMICK The Gaussian function is based on the squared Euclidean distance. Note that squaring the Euclidean distance is the same as just removing the square root term. This leads to the (x - mu)^2 term in the equation for the one dimensional Gaussian. For a one-dimensional input, the squared Euclidean distance is just the parabola y = x^2. DEEP LEARNING TUTORIAL Autoencoder - By training a neural network to produce an output that’s identical to the input, but having fewer nodes in the hidden layer than in the input, you’ve built a tool for compressing the data. Going from the input to the hidden layer is the compression step. You take, e.g., a 100 element vector and compress it to a 50### element vector.

GAUSSIAN MIXTURE MODELS TUTORIAL AND MATLAB CODE · CHRIS Gaussian Mixture Models Tutorial and MATLAB Code. 04 Aug 2014. You can think of building a Gaussian Mixture Model as a type of clustering algorithm. Using an iterative technique called Expectation Maximization, the process and result is very similar to k-means clustering. The difference is that the clusters are assumed to each### have an

### SVM TUTORIAL

SVM Tutorial - Part I. 16 Apr 2013. I found it really hard to get a basic understanding of Support Vector Machines. To learn how SVMs work, I ultimately went through Andrew Ng’s Machine Learning course (available freely from Stanford). I think the reason SVM tutorials are so challenging is that training an SVM is a complex optimization WORD2VEC TUTORIAL PART 2 Sampling rate. The word2vec C code implements an equation for calculating a probability with which to keep a given word in the vocabulary. w i is the word, z ( w i) is the fraction of the total words in the corpus that are that word. For example, if the word “peanut” occurs 1,000 times in a 1 billion word corpus, then z (‘peanut’) = 1E-6. DEEP LEARNING TUTORIAL Whitening has two simple steps: Project the dataset onto the eigenvectors. This rotates the dataset so that there is no correlation between the components. Normalize the the dataset to have a variance of 1 for all components. This is done by simply dividing each WHAT IS AN L2-SVM? · CHRIS MCCORMICK Support vector machines with linear sum of slack variables, which are commonly used, are called L1-SVMs, and SVMs with the square sum of slack variables are called L2-SVMs. It’s really just a slight difference in the objective function used to optimize the SVM. The objective for an L1-SVM is: The difference is in the regularization### term

RADIAL BASIS FUNCTION NETWORK (RBFN) TUTORIAL · CHRIS 15 Aug 2013. A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier. Generally, when people talk about neural networks or “Artificial Neural Networks” they are referring to the Multilayer Perceptron (MLP). Each neuron in an MLP takes the CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

TUTORIALS · CHRIS MCCORMICK Radial Basis Function Networks. I’ve written a number of posts related to Radial Basis Function Networks. Together, they can be taken as a multi-part tutorial to RBFNs. Part 1 - RBFN Basics, RBFNs for Classification. Part 2 - RBFN Example Code in Matlab. Part 3 - RBFN for function approximation. MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection MinHash Tutorial with Python Code 12 Jun 2015. In this post, I’m providing a brief tutorial, along with some example Python code, for applying the MinHash algorithm to compare a large number of documents to one another efficiently. THE GAUSSIAN KERNEL · CHRIS MCCORMICK The Gaussian function is based on the squared Euclidean distance. Note that squaring the Euclidean distance is the same as just removing the square root term. This leads to the (x - mu)^2 term in the equation for the one dimensional Gaussian. For a one-dimensional input, the squared Euclidean distance is just the parabola y = x^2. DEEP LEARNING TUTORIAL Autoencoder - By training a neural network to produce an output that’s identical to the input, but having fewer nodes in the hidden layer than in the input, you’ve built a tool for compressing the data. Going from the input to the hidden layer is the compression step. You take, e.g., a 100 element vector and compress it to a 50### element vector.

GAUSSIAN MIXTURE MODELS TUTORIAL AND MATLAB CODE · CHRIS Gaussian Mixture Models Tutorial and MATLAB Code. 04 Aug 2014. You can think of building a Gaussian Mixture Model as a type of clustering algorithm. Using an iterative technique called Expectation Maximization, the process and result is very similar to k-means clustering. The difference is that the clusters are assumed to each### have an

### SVM TUTORIAL

SVM Tutorial - Part I. 16 Apr 2013. I found it really hard to get a basic understanding of Support Vector Machines. To learn how SVMs work, I ultimately went through Andrew Ng’s Machine Learning course (available freely from Stanford). I think the reason SVM tutorials are so challenging is that training an SVM is a complex optimization WORD2VEC TUTORIAL PART 2 Sampling rate. The word2vec C code implements an equation for calculating a probability with which to keep a given word in the vocabulary. w i is the word, z ( w i) is the fraction of the total words in the corpus that are that word. For example, if the word “peanut” occurs 1,000 times in a 1 billion word corpus, then z (‘peanut’) = 1E-6. DEEP LEARNING TUTORIAL Whitening has two simple steps: Project the dataset onto the eigenvectors. This rotates the dataset so that there is no correlation between the components. Normalize the the dataset to have a variance of 1 for all components. This is done by simply dividing each WHAT IS AN L2-SVM? · CHRIS MCCORMICK Support vector machines with linear sum of slack variables, which are commonly used, are called L1-SVMs, and SVMs with the square sum of slack variables are called L2-SVMs. It’s really just a slight difference in the objective function used to optimize the SVM. The objective for an L1-SVM is: The difference is in the regularization### term

RADIAL BASIS FUNCTION NETWORK (RBFN) TUTORIAL · CHRIS 15 Aug 2013. A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier. Generally, when people talk about neural networks or “Artificial Neural Networks” they are referring to the Multilayer Perceptron (MLP). Each neuron in an MLP takes the CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK 05 Oct 2020. Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK For Question Answering, they have a version of BERT-large that has already been fine-tuned for the SQuAD benchmark. BERT-large is really big it has 24-layers and an embedding size of 1,024, for a total of 340M parameters! Altogether it is 1.34GB, so expect it to take a couple minutes to download to your Colab instance. DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another, with the goal of driving “research in the development of general and robust natural language understanding systems.”. The collection consists of nine### “difficult and

GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Understanding the DeepLearnToolbox CNN Example. 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for MATLAB. His example code applies a relatively simple CNN with 2 hidden layers and only 18 neurons to the MNIST### dataset.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

CHRIS MCCORMICK · MACHINE LEARNING TUTORIALS AND INSIGHTSCHRIS MCCORMICK AUTHORCHRIS MCCORMICK BERTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITERCHRISTOPHER MCCORMICK How To Build Your Own Question Answering System 27 May 2021. In this post, we’ll create a very simple question answering system that, given a natural language question, returns the most likely answers from a corpus of documents. QUESTION ANSWERING WITH A FINE-TUNED BERT · CHRIS MCCORMICK 2. Load Fine-Tuned BERT-large. For Question Answering we use the BertForQuestionAnswering class from the transformers library.. This class supports fine-tuning, but for this example we will keep things simpler and load a BERT model that has already been fine-tuned for the### SQuAD benchmark.

DOMAIN-SPECIFIC BERT MODELS · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMBERT VOCABULARYC# BERTDOWNLOAD BERT MODEL GLUE EXPLAINED: UNDERSTANDING BERT THROUGH BENCHMARKS # Outputs of BERT, corresponding to one output vector of size 768 for each input token outputs = model (input_ids, attention_mask = attention_mask, token_type_ids = token_type_ids, position_ids = position_ids, head_mask = head_mask) # Grab the token, used as an aggregate output representation for classification tasks pooled_output = outputs # Create dropout (for GRADIENT DESCENT DERIVATION · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gradient Descent Derivation 04 Mar 2014. Andrew Ng’s course on Machine Learning at Coursera provides an excellent explanation of gradient descent for linear regression. To really get a strong grasp on it, I decided to work through some of the derivations and some simple MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCHRIS MCCORMICK AICHRIS MCCORMICK BERTCHRIS MCCORMICK CTCHRIS MCCORMICK FACEBOOKCHRIS MCCORMICK WRITER LAPLACIAN OF GAUSSIAN (MARR-HILDRETH) EDGE DETECTOREDGE DETECTIONEDGE DETECTION IN IMAGE PROCESSINGEDGE DETECTION ONLINEIMAGEJ EDGE DETECTIONROBERTS EDGE DETECTIONSIMPLE EDGE DETECTION ALGORITHM Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Laplacian Of Gaussian (Marr-Hildreth) Edge Detector 27 Feb 2013. The following are my notes on part of the Edge Detection lecture by Dr. Shah: Lecture 03 – Edge Detection Noise can really affect edge detection, because noise can cause one pixel to look very K-FOLD CROSS-VALIDATION, WITH MATLAB CODE · CHRIS MCCORMICKSEE MORE ON MCCORMICKML.COMCROSS VALIDATION MATLABMATLAB CODE EXAMPLEMATLAB CODE DOWNLOADMATLAB CODE ONLINEMATLAB SOURCE CODE DOWNLOADSIMPLE### MATLAB CODE

UNDERSTANDING THE DEEPLEARNTOOLBOX CNN EXAMPLE · CHRIS Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Understanding the DeepLearnToolbox CNN Example 10 Jan 2015. In this post, I provide a detailed description and explanation of the Convolutional Neural Network example provided in Rasmus Berg Palm’s DeepLearnToolbox for### MATLAB.

PRODUCT QUANTIZERS FOR K-NN TUTORIAL PART 1 · CHRIS MCCORMICKSEE MORE### ON MCCORMICKML.COM

TUTORIALS · CHRIS MCCORMICK Radial Basis Function Networks. I’ve written a number of posts related to Radial Basis Function Networks. Together, they can be taken as a multi-part tutorial to RBFNs. MINHASH TUTORIAL WITH PYTHON CODE · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection MinHash Tutorial with Python Code 12 Jun 2015. In this post, I’m providing a brief tutorial, along with some example Python code, for applying the MinHash algorithm to compare a large number of documents to one another efficiently.### SVM TUTORIAL

Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection SVM Tutorial - Part I 16 Apr 2013. I found it really hard to get a basic understanding of Support Vector Machines. GAUSSIAN MIXTURE MODELS TUTORIAL AND MATLAB CODE · CHRIS Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Gaussian Mixture Models Tutorial and MATLAB Code 04 Aug 2014. You can think of building a Gaussian Mixture Model as a type of clustering algorithm. THE GAUSSIAN KERNEL · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection The Gaussian Kernel 15 Aug 2013. Each RBF neuron computes a measure of the similarity between the input and its prototype vector (taken from the training### set).

DEEP LEARNING TUTORIAL Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Deep Learning Tutorial - Sparse Autoencoder 30 May 2014. This post contains my notes on the Autoencoder section of Stanford’s deep learning tutorial /### CS294A.

DEEP LEARNING TUTORIAL Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Deep Learning Tutorial - PCA and Whitening 03 Jun 2014 Principal Component Analysis WORD2VEC TUTORIAL PART 2 Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Word2Vec Tutorial Part 2 - Negative Sampling 11 Jan 2017. In part 2 of the word2vec tutorial (here’s part 1), I’ll cover a few additional modifications to the basic skip-gram model which are important for actually making it feasible to train. MAHALANOBIS DISTANCE · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection Mahalanobis Distance 22 Jul 2014. Many machine learning techniques make use of distance calculations as a measure of similarity between two points. WHAT IS AN L2-SVM? · CHRIS MCCORMICK Chris McCormick About Tutorials Store Forum Archive New BERT eBook + 11 Application Notebooks! → The BERT Collection What is an L2-SVM? 06 Jan 2015. While reading through various deep learning research papers, I’ve come across the term “L2-SVM” a couple times. CHRIS MCCORMICK ABOUT TUTORIALS### STORE

### ARCHIVE

New BERT eBook + 11 Application Notebooks! → The BERT Collection HOW TO APPLY BERT TO ARABIC AND OTHER LANGUAGES### 05 Oct 2020

Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly powerful tool, but model releases are almost always in English, perhaps followed by Chinese, Russian, or Western European language variants.### 2 Comments

SMART BATCHING TUTORIAL - SPEED UP BERT TRAINING### 29 Jul 2020

In this blog post / Notebook, I’ll demonstrate how to dramatically increase BERT’s training time by creating batches of samples with different sequence lengths.### 1 Comment

GPU BENCHMARKS FOR FINE-TUNING BERT### 21 Jul 2020

While working on my recent Multi-Class Classification Example### , I was having

trouble with running out of memory on the GPU in Colab–a pretty### frustrating issue!

### 0 Comments

DOMAIN-SPECIFIC BERT MODELS### 22 Jun 2020

If your text data is domain specific (e.g. legal, financial, academic, industry-specific) or otherwise different from the “standard” text corpus used to train BERT and other langauge models you might want to consider either continuing to train BERT with some of your text data or looking for a domain-specific language model.### 2 Comments

EXISTING TOOLS FOR NAMED ENTITY RECOGNITION### 19 May 2020

In conjunction with our tutorial for fine-tuning BERT on Named Entity Recognition (NER) tasks here , we wanted to provide some practical guidance and resources for building your own NER application since fine-tuning BERT may not be the best solution for every NER application.### 1 Comment

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