Your AI algorithms represent a unique competitive advantage for your company and come at a considerable expense. A successful adversarial attack against them could cost you that advantage and you would never know it happened. HiddenLayer is the first productized solution for the next security ...

Prevent attacks against your AI with HiddenLayer

HiddenLayer provides a software-based platform that helps enterprises monitor the inputs and outputs of machine-learning algorithms.

HiddenLayer - Crunchbase Company Profile & Funding

HiddenLayer | 5,128 followers on LinkedIn. #Security4AI | HiddenLayer, a Gartner recognized AI Application Security company, is a provider of security solutions for artificial intelligence algorithms, models &amp; the data that power them. With a first-of-its-kind, non-invasive software approach ...

HiddenLayer | LinkedIn

HiddenLayer today emerged from stealth with $6 million in seed funding from Ten Eleven Ventures, Secure Octane and other investors. Sestito, the former director of threat research at Cylance and VP of engineering at Qualys, co-founded the company several months ago with Tanner Burns and Jim Ballard.

HiddenLayer emerges from stealth to protect AI models from attacks

Bringing the company’s total raised to $56 million, the new funds will be put toward supporting HiddenLayer’s go-to-market efforts, expanding its headcount from 50 employees to 90 by the end of the year and further investing in R&amp;D, co-founder and CEO Chris Sestito told TechCrunch via email.

HiddenLayer raises $50M for its AI-defending cybersecurity tools ...

Cybersecurity for Machine Learning Models · © SOSSEC INC. | Powered By AVATAR Computing

HiddenLayer – SOSSEC, Inc.

Today we proudly announce our seed investment in ML security company HiddenLayer. As soon as we met the incredible team behind this innovative company, we knew we wanted to be a part of their pioneering effort to develop a brand new market protecting a cornerstone of the next economy, ML ...

Why We Invested in HiddenLayer - Ten Eleven

For more information, please contact us at info@hiddenlayerllc.com.

Hidden Layer LLC

/PRNewswire/ -- HiddenLayer, a Gartner recognized AI Application Security company, is proud to announce a strategic collaboration with Intel Corporation to...

HiddenLayer Announces Machine Learning Security Collaboration with ...

Cyber security product specifically designed for machine learning models. Products include detection &amp; response, proactive vulnerability scanning, and attack...

Pitch | Hidden Layer

HiddenLayer helps enterprises safeguard the machine learning models behind their most important products with a comprehensive security platform. Only HiddenLayer offers turnkey AI/ML security that does not add unnecessary complexity to models and does not require access to raw data and algorithms.

HiddenLayer - Ten Eleven

HiddenLayer, the leading security provider for artificial intelligence (AI) models and assets, has raised $50 million in Series A funding to expand its talent base, increase go-to-market efforts, and further invest in its award-winning Machine Learning Security (MLSec) Platform. The investment marks the largest Series A funding raised by a cybersecurity company ...

HiddenLayer Raises $50M in Series A Funding to Safeguard AI

The latest tweets from HiddenLayer (@hiddenlayersec)

HiddenLayer (@hiddenlayersec) · X

Additionally, HiddenLayer recently announced that CRN®, a brand of The Channel Company, named HiddenLayer to its 2023 Stellar Startups list. This annual list, previously known as CRN Emerging Vendors, recognizes fast-rising technology manufacturers committed to delivering leading-edge solutions ...

HiddenLayer Launches Channel Partner Program to Secure AI and MLOps ...

In neural networks, a Hidden Layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered ...

Hidden Layer Definition | DeepAI

HiddenLayer, which emerged from stealth in July 2022 with $6 million in funding, said the latest financing was led by M12, Microsoft’s Venture Fund, and Moore Strategic Ventures. The Austin company also took on equity investments from Booz Allen Ventures, IBM Ventures, Capital One Ventures, ...

HiddenLayer Raises Hefty $50M Round for AI Security Tech - ...

WASHINGTON — Booz Allen Hamilton, one of the largest U.S. defense contractors, said it invested in HiddenLayer, a smaller company specializing in digital defenses of artificial intelligence and sensitive networks.

Booz Allen Hamilton invests in AI security startup HiddenLayer

Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. - GitHub - waleedka/hiddenlayer: Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

GitHub - waleedka/hiddenlayer: Neural network graphs and training ...

Neural network graphs and training metrics for PyTorch and TensorFlow

hiddenlayer · PyPI

Chris Sestito, Co-founder and CEO, HiddenLayer, pitches at the RSAC 2023 Innovation Sandbox Contest.For 17 years, cybersecurity&#x27;s boldest new innovators have...

HiddenLayer — RSA Conference 2023 Innovation Sandbox - YouTube

HiddenLayer, a Gartner recognized AI Application Security company, is a provider of security solutions for artificial intelligence algorithms, models & the data that power them. With a first-of-its-kind, non-invasive software approach to observing & securing AI, HiddenLayer is helping to protect the world’s most valuable technologies. Founded in March of 2022 by experienced security and AI professionals, HiddenLayer is based in Austin, Texas.

HiddenLayer

In our fast-paced world, machine learning and artificial intelligence are infiltrating every corner of our daily lives. But what is AI really all about? What is behind this magic? How can such a complex phenomenon be explained so simply? Join us on an amazing journey where we will immerse you in the world of artificial intelligence with clear and accessible examples! A neuron is the basic element of any neural network. To understand how a neuron works, let's take a closer look at it. are the values that the neuron takes in. This data can be anything, such as image pixels or feature values. Input data is the number by which the input data is multiplied. The weights determine how strongly the neuron responds to the input data. The weighting factor is the result of multiplying the input data by the weighting factor. Output value Each layer of an artificial neural network contains neurons, but they perform different functions. In the input layer, neurons simply transmit input data without additional processing. In the hidden layer, neurons actively process information by applying weighting coefficients and activation functions. And in the output layer, the neuron generates a final result based on the processed information from the previous layer. How Does a Neuron Make Predictions? To make a prediction, the neuron simply multiplies the input data by its weighting factor: prediction=input×weight
prediction=input×weight Sample code: def neural_network(input, weight):
    prediction = input * weight
    return prediction In the code above, I've given you a peek behind the scenes of a simple neural network. This is a basic model with a single input. Here is our tuning tool. Multiplying it by the input, we get the prediction. But it's actually much more interesting than that! weight In the future, we will extend this model with more inputs and complexity. And yes, you will learn more about this mysterious `weight' a little later. Ready to dive deeper? The World Is Multifaceted: When One Characteristic Isn't Enough Imagine determining whether you're looking at a cat or a dog based on more than just their weight. After all, each animal has its own unique coat color, height and many other characteristics. What if your neural network could analyze all this mosaic of information at once and make accurate predictions? Each input neuron is like a detective examining different clues, and together they come to the right conclusion! Let's use the example of defining an animal. Imagine we have an animal shelter, and we often have to determine whether an arriving pet is a cat or a dog based on various characteristics. We will use the following data for each animal: - the length of the animal's paw. paw_length - the frequency of sounds produced (meowing or barking). sound_freq - the number of people (volunteers) who want to care for this animal. fan_count Based on the experience of previous shelter professionals, we have initial coefficients to help us make predictions: weights = [0.4, -0.2, 0.1] These coefficients were obtained by analyzing previous data: for paw length: Paw length was found to have little effect on animal identification. 0.1 for frequency of sounds: The frequency of sounds produced appeared to be a more important characteristic. 0.2 for number of volunteers: This parameter has no effect on determining who a pet is, but it may be useful for other tasks, such as predicting how long an animal will stay at the shelter. 0 We can now use these weights to determine who is in front of us: paw_lengths = [1.5, 7.5, 9.0, 7.0]
sound_freqs = [1.3, 0.85, 0.5, 0.9]
fan_counts = [1.1, 1.2, 1.4, 1.3]
 
input_data = [paw_lengths[0], sound_freqs[0], fan_counts[0]] Based on the provided data, our neural network will try to make a prediction. If we need to improve the accuracy of the prediction, we can adjust the weights based on additional data and feedback. This graph shows the distribution of the characteristics of each animal. You can easily compare how much each characteristic makes up for each animal. Let's write our code! def w_sum(a, b):
    assert (len(a) == len(b))
    output = 0
    for i in range(len(a)):
        output += (a[i] * b[i])
    return output This is our data blending cookbook! Think of it as a chef carefully combining ingredients to create the perfect dish. We take two lists, multiply their values element by element, and then add them up. This process is like creating art from simple colors! w_sum: def neural_network(input, weights):
    prediction = w_sum(input, weights)
    return prediction This is our cheat sheet for the data world! All you need to do is give it the information and weights and it will "decipher" the answer for you. Is it a cat or a dog? Like a true detective, this feature will solve this mystery. neural_network: input_data = [paw_length[0], sound_freq[0], enthusiasm[0]]
prediction = neural_network(input_data, weights)

if prediction > 0.5:
    print("It's probably a dog!")
else:
    print("It's probably a cat!") We chose a threshold value of 0.5 (or any other number) because this value optimally separated cats and dogs based on our test data. If the prediction is above this threshold, we consider that we have a dog in front of us, otherwise, we consider it a cat. Final Python Code import numpy as np


# Function for calculating the weighted sum of inputs
def w_sum(a, b):
    assert len(a) == len(b)
    output = 0
    for i in range(len(a)):
        output += (a[i] * b[i])
    return output


# Activation function (here we use the identity function for simplicity)
def activation(x):
    return x


# Neural network function
def neural_network(input, weights):
    pred = w_sum(input, weights)
    return activation(pred)


# Input data (foot length, sound frequency, number of fans)
input_data = [1.5, 0.85, 1.2]

# Starting weights
weights = [0.1, 0.2, 0]

# Making a prediction
prediction = neural_network(input_data, weights)
print("Initial prediction:", prediction)

# Target (let 0 be a cat, 1 be a dog)
target = 1

# Learning curve
learning_rate = 0.01

# The learning process
for iteration in range(100):
    prediction = neural_network(input_data, weights)
    error = (prediction - target) ** 2
    print("Error:", error, "Prediction:", prediction)

    # Adjusting the weights
    for i in range(len(weights)):
        direction_and_amount = (prediction - target) * input_data[i]
        weights[i] -= direction_and_amount * learning_rate

# Final prediction after training
prediction = neural_network(input_data, weights)
print("Final prediction after training:", prediction) In this code, we start with an initial prediction based on the input data and initial weights. We then proceed to a learning process where we gradually adjust the weights based on the error between the current prediction and the target value. The goal of training is to minimize the error. Once the training is complete, the final prediction is output. This example demonstrates the basic principles of a neural network, including the training process and the adjustment of weights. It is intended for training purposes and to illustrate the concepts presented in the article. Conclusion: The Art of the Possible The journey into the world of artificial intelligence and machine learning is not just about learning about technology; it is about exploring how we can push the boundaries of what is possible. We started with the basics - neurons and how they work, going through the creation of simple neural networks that can tell the difference between a cat and a dog using just a few characteristics. And while our examples were simplistic, they serve as a powerful demonstration of what artificial intelligence can do. Standing on the cusp of a new era, we see that AI and machine learning are no longer just tools for processing data or automating tasks. They are a new way of interacting with the world and understanding the complexities of nature and even our own minds. They are tools that help us solve problems we couldn't even dream of before. Each of us can be part of this amazing journey. Regardless of your background in programming or math, it's easier than ever to start learning AI today. Plenty of resources, courses, and communities are ready to support your interest and help you grow. Our simple examples and basic models are just a starting point from which you can begin your journey into the world of artificial intelligence. Remember, every great discovery begins with a first step. Your research and experimentation can lead to the development of new technologies that help solve global problems, improve lives, and open new horizons for all of humanity. Artificial intelligence isn't just the technology of the future; it's shaping our future right now. And each of us can be part of this amazing process. Together, we can create a world where machines and humans work side by side, pushing the boundaries of our understanding and abilities. So, get inspired, experiment, and don't be afraid to explore the unknown. After all, it is in the unknown that lies the future we create together. Let's go, to new discoveries!

Decoding AI: Dive Deep Into Neural Networks and Create Your Own from Scratch!

This paper is under CC 4.0 license. available on arxiv Authors: (1) Amir Noorizadegan, Department of Civil Engineering, National Taiwan University; (2) D.L. Young, Core Tech System Co. Ltd, Moldex3D, Department of Civil Engineering, National Taiwan University & dlyoung@ntu.edu.tw; (3) Y.C. Hon, Department of Mathematics, City University of Hong Kong; (4) C.S. Chen, Department of Civil Engineering, National Taiwan University & dchen@ntu.edu.tw. Table of Links Abstract & Introduction Neural Networks PINN for Solving Inverse Burgers’ Equation Residual Network Numerical Results Results, Acknowledgments & References 4 Residual Network In this study, we propose a power-enhanced variant of the ResNet that skips every other layer, denoted as the “Power-Enhanced SkipResNet.” The modification involves altering the recursive definition in (9) as follows: For the purpose of comparison among Plain NN, ResNet, and SQR-SkipResNet (Figs. 2(a)-(c), respectively), we evaluate the output of the third hidden layer concerning the input y0 = X . The results for the plain neural network are as follows: Figure 2(d) visually represents the “expression tree” for the case with p = 2, providing an insightful illustration of the data flow from input to output. The graph demonstrates the existence of multiple paths that the data can traverse. Each of these paths represents a distinct configuration, determining which residual modules are entered and which ones are skipped. Our extensive numerical experiments support our approach, indicating that a power of 2 is effective for networks with fewer than 30 hidden layers. However, for deeper networks, a larger power can contribute to network stability. Nonetheless, deploying such deep networks does not substantially enhance accuracy and notably increases CPU time. In tasks like interpolation and solving PDEs, a power of 2 generally suffices, and going beyond may not justify the added complexity in terms of accuracy and efficiency

Physics-Informed with Power-Enhanced Residual Network: Residual Network

Your grandfather wears those comfy slipper-y shoes all day, every day, and they’re starting to get holes in the toe and a detached sole. For his birthday, you’d love to surprise him with a new pair of those. You jump on your favorite retail website and land in the search box, only to realize that your aging human brain doesn’t remember the name of those. Not slippers, not loafers…some Indian name. If after scratching your head, your best guess is still “soft shoes my grandpa wears”, all is not lost. Because if the search engine is on the ball, it will simply dig a little deeper, harnessing the power of data science to decipher what you’re actually looking: your underlying . search intent Slippers, shoes, or something else? In the digital world nowadays, search intent is a very hot topic. To succeed in providing an excellent user experience, website managers must know more than just which keywords people are entering. Now, the best practice is for search engines to have the ability to what users want, even if these humans can’t come close to articulating it. fully comprehend That’s where “brainy” neural network logistics come in. Neural network–based search technology facilitates “understanding” of search intent and related themes to find exactly what people need. A neural network takes into account the to improve search . So when you search for something like “soft shoes my grandpa wears”, a savvy search engine will instantly read between the lines and show you all the moccasins your granddad could ever need. context of words and connected ideas relevance What is a neural network? Neural networks were conceived as mathematical models by of the University of Chicago (1944). This concept exists in two realms: Warren McCullough and Walter Pitts Animal brains, including humans’, have biological neural networks and feedback loops that can process input data from the senses — sights, sounds, and scents — plus learn from the surroundings. Nature: Computer scientists have figured out how to replicate the model of neurons in natural neural networks. Artificial neural networks (ANNs) are the basis for artificial intelligence (AI) and machine learning, sitting at the center of many contemporary AI tools. These versions, complete with artificial neurons, allow computers to more effectively process an input dataset. As for the “intelligent” machines involved, the quality of their output is limited to the material they receive, but they do “think” like an animal does. Technology: Three Processing Layers With naturally occurring networks, information is processed through nodes (computational units) that sit in various layers. An artificial neural net is also a series of nodes, organized in layers and connected through inputs and outputs. Neural networks work their magic through three layers: this first layer is where data is received before being passed along to the next-layer nodes. Input layer: where weighted connections and non-linear activation functions generate the output  (this level could include multiple layers). Hidden layer: where the finished data is expressed. Output layer: The number of layers in a neural network is a clue to its classification. A basic neural network has two or three layers. One that has at least two layers — which adds some complexity — is technically a neural network. A very large neural network is a deep-learning tool; IBM’s definition is that more than three layers (including the input and output) constitutes a deep-learning algorithm. deep How ANNs Work Artificial neural networks contain a number of weighted responses that trigger different reactions within the hidden layer. These responses dictate what gets produced as the outcome. To do their jobs, a little training data is in order. Neural networks (or neural nets, for short) are “trained” by adjusting weighting and testing on different types of outcomes. A neural network must have the right rules and weighted responses to do the particular job for which it’s intended. For example, the ChatGPT neural network contains nodes in the hidden layer that allow it to effectively process language. Different Types of Neural Networks There are multiple kinds of artificial neural networks. Some are generative and can “learn” as they work. Some simply focus on automating and improving tasks such as facial recognition or data analysis. Some are fantastic for aiding image processing and automation; others are excellent for enhancing . Different applications of neural networks can also be tailored for the type of use case. search and discovery Here are six types of neural network with their key data points: Feed Forward Feed-forward neural networks are linear: they process information in one direction until an output is ready. This is the simplest form of neural network architecture. When used alone, as opposed to as part of a modular network, they don’t contain the feedback loops required to build artificial intelligence. This architecture is often used in classification software like spam filter, where each step in the analysis brings us closer to the final result without intermediate results needing to be reused later downstream. Recurrent In terms of complexity, recurrent neural networks (RNNs) pick up where feed-forward networks leave off, reprocessing their own outputs to generate more-accurate future outputs. This creates a feedback loop, — a recurring process — in the hidden layer. Because an RNN can create more-valuable insights by reusing its output — pushing data back through the network to contextualize and “understand” the information — it’s ideal for utilizing with AI-powered tools like ChatGPT. Convolutional (CNNs) are often used for pattern recognition, image recognition, and image classification applications. This subset of machine learning categorizes data as it’s processed through multiple convolutional layers, breaking it down and recategorizing based on importance. The resulting information can then be used to differentiate similar images from one another, spotting critical pixel differences, for instance, that the human eye might not see. One consideration: if a CNN isn’t set up optimally, it can consider some information “unimportant”. Convolutional neural networks That’s where a convolutional neural network comes in handy. de Deconvolutional Deconvolutional neural networks (DNNs) work in the opposite way to CNNs. They start with “raw” data and work backward toward usable image output. DNNs can be used to identify information discarded by or missed when using CNNs, and they can be used in conjunction with CNNs to self-test outcomes. Modular Modular neural networks are made up of multiple neural networks that work separately toward a common goal by using ensemble learning, a method that connects different models to pool results and test outcomes before presenting findings. An organization can use this variation of network technology to ensure accurate outcomes. For example, a finance company might utilize it to generate more-accurate stock-market predictions as the different networks produce different outcomes; the findings can then be used to accurately peg averages and outliers. Transformers Did you know that the in ChatGPT and GPT-4 stands for transformer? The transformer, the element that makes generative AI so powerful, is a relatively new form of that utilizes modular neural networks. Transformers use something called a self-attention layer, along with feed-forward neural networks and RNNs, to focus on complex tasks such as language processing. T machine learning Take your search to neural levels As in many industries, AI is steadily changing the fabric of real-world search. Neural networks are being pressed into service to improve software’s comprehension of what consumers want. These tools can help among people, content, and data, as well as connections between user interests and search queries (both current and past). ecommerce identify relationships At Algolia, we harness machine learning and (NLP) to get at the heart of search intent; then the best query results can be and provided to your users. natural language processing ranked Want to smarten your search engine with the ? Maybe improve your (image recognition or computer vision skills), or perhaps optimize your ? advantages of neural search visual search voice search To assist your decision-making, let’s look at your needs and weigh the options. with us today. Get in touch Also published . here

What are the Types of Neural Networks and How Do They Work?

Python is a high-level programming language that is widely used for Machine Learning (ML) applications. It is known for its readability, versatility and ease of use, making it an ideal choice for developers, data scientists, and machine learning engineers alike. The Python ecosystem has a large number of libraries and tools that support machine learning, such as NumPy, Pandas, Matplotlib, TensorFlow, and scikit-learn. These libraries provide powerful algorithms and tools that enable developers to perform complex data analysis, build predictive models and perform data visualization. Python is also popular in machine learning projects due to its robust and active development community, which is constantly updating and improving the libraries and tools available. Why Is Python Preferred for Machine Learning and AI? Python has long been the preferred language for building . There are several reasons why AI and ML developers choose Python: machine learning models : Python is known for its simple and readable syntax, making it easier for developers to write and understand code. Ease of use : Python has a large and active community of developers and data scientists who continuously contribute to the development of libraries and tools for machine learning. Large community : Python has a vast library of powerful tools and algorithms for machine learning. Robust libraries : Python can be used for a variety of purposes, including web development, data Versatility analysis, and scientific computing, making it a popular choice for AI and ML applications. : Python integrates well with other programming languages and tools, making it easy to integrate with existing systems and technologies. Interoperability 10 Best Python Machine Learning Tutorials These Python ML tutorials can help you get off the ground if you are new to Python, machine learning, or both. This tutorial provides a step-by-step guide to creating your first machine learning project using Python. The tutorial is designed to be accessible to beginners and provides a comprehensive guide to machine learning using Python. Step-By-Step: Your First Machine Learning Project in Python The tutorial starts with downloading and installing the Python SciPy library and loading a dataset. Next, you will perform statistical summaries and data visualizations to understand the dataset structure. Then, you will create 6 different machine learning models, evaluate their performance and choose the best one. Finally, you will use k-fold cross-validation to build confidence in the accuracy of the chosen model and make predictions on new data. Read the tutorial This tutorial is a step-by-step guide to implementing the fundamental machine learning technique in Python. The tutorial covers the prerequisites, including basic knowledge of statistics and probability, familiarity with Python and its libraries, and understanding of gradient descent. Tutorial on Linear Regression in Python The tutorial includes the steps to load the data, split it into training and test sets, fit the linear regression model to the training data, make predictions using the test data, evaluate the performance of the model, and plot the data and the regression line. By the end of this tutorial, you will have learned how to implement linear regression in Python and how to evaluate the performance of the model. Read the tutorial This tutorial teaches how to build a neural network from scratch using Python for AI applications. It covers the basics of artificial intelligence, machine learning, and deep learning, and explains how a neural network functions internally. The tutorial includes step-by-step instructions on defining input and output layers, creating a hidden layer, and using the sigmoid activation function. It also covers how to make predictions with the neural network and evaluate its accuracy. Tutorial: Build a Neural Network and Make Predictions Read the tutorial This tutorial provides an introduction to Machine Learning and Python, covering various algorithms and techniques including k-nearest neighbor classifier, neural networks, Naive Bayes classifier, linear regression, decision trees, and clustering algorithms. It also covers various libraries and tools such as NumPy, scikit-learn, and TensorFlow. Introduction to Machine Learning with Python The tutorial will help beginners understand the basics of machine learning and provide a solid foundation in implementing these algorithms in Python. It covers both supervised and unsupervised learning, model evaluation, cross-validation, and hyperparameter tuning. Read the tutorial This tutorial provides a comprehensive introduction to NumPy, a Python library for data science. It covers the core concepts and techniques involved in using NumPy, including creating arrays, manipulating arrays to perform useful calculations, and indexing and slicing arrays. NumPy Tutorial: Introduction to Data Science in Python The tutorial also covers more advanced topics such as broadcasting arrays, basic array operations, and advanced array operations such as matrix multiplication and statistical functions. By the end of the tutorial, the reader will have a solid understanding of NumPy and will be equipped with the skills needed to use it for data science. Read the tutorial This tutorial provides an introduction to the basics of Python machine learning using the scikit-learn library. It covers data exploration, preprocessing, model construction, prediction, validation, and performance evaluation. The tutorial teaches how to use the KMeans algorithm to build an unsupervised model and the Support Vector Machines (SVM) algorithm to build a classification model. Scikit-Learn Tutorial: Python Machine Learning By the end of the tutorial, the reader will have a solid understanding of how to use scikit-learn to build and evaluate machine learning models in Python. Whether a beginner or an experienced practitioner, the tutorial provides the knowledge and skills to get started with scikit-learn and Python machine learning. Read the tutorial This tutorial provides an introduction to sentiment analysis using the NLTK library in Python. The tutorial covers the important features of NLTK for processing text data and the different approaches used for sentiment analysis. The tutorial covers topics such as splitting and filtering text data, analyzing word frequency, finding concordance and collocations, and performing sentiment analysis using built-in and custom classifiers. Sentiment Analysis Tutorial: Introduction to the Python NLTK Library By the end of the tutorial, the user will have a good understanding of the basic features of NLTK and the different approaches used for sentiment analysis, allowing them to perform their own sentiment analysis and make data-driven decisions based on the insights gathered. Read the tutorial This tutorial provides an overview of how to use a tool called DVC for data version control in machine learning and data science projects. It explains how to install and initialize the tool, track datasets and models, commit changes, and share work with team members. It also highlights the importance of data version control in accurately reproducing experiments and avoiding data loss. Tutorial: Data Version Control with Python and DVC By using DVC, developers can effectively manage and version control their data and models, creating reproducible experiments and improving collaboration among team members. Read the tutorial This tutorial introduces . It covers the process of how computers understand features in images and how to analyze these features to detect human faces. It uses the OpenCV library and demonstrates how to use a minimal Python solution to detect faces in images. Face Detection Tutorial with Python face detection with Python The tutorial also covers the advantages and disadvantages of using traditional face detection techniques, and provides tips on improving accuracy through data augmentation techniques. It is designed for beginner to intermediate-level programmers who are familiar with Python programming and have some knowledge of computer vision and image processing. By the end of the tutorial, participants will have the ability to detect faces in images using a simple Python script. Read the tutorial This tutorial provides an introduction to speech recognition and how to incorporate it into a Python application. It covers the basics of how speech recognition works and the various packages available on PyPI. It focuses on the SpeechRecognition package, explaining how to install it and use it to recognize speech from audio files or microphone input. An example code is provided to demonstrate how to use the package. Speech Recognition Tutorial with Python The tutorial concludes by highlighting the benefits of incorporating speech recognition into a Python application, such as improved accessibility and interactivity. Read the tutorial Conclusion The field of machine learning is constantly evolving and the demand for skilled professionals in this area is growing. Python is a popular language for ML projects, with a vast ecosystem of libraries and tools. The tutorials discussed in this article are some of the best resources available to help individuals get started with Python machine learning. These tutorials cover a range of topics, from the basics of machine learning algorithms to advanced techniques such as deep learning and neural networks. If you are getting started with data science in Python, these tutorials are a great starting point for building your machine learning skills and knowledge.

10 Best Python Machine Learning Tutorials

This is an introductory article explaining the basic intuition, mathematical idea & scope of radial basis functions in the development of predictive machine learning models. Table of Contents Introduction Basic intuition of a Radial Basis Function Types of Radial Basis Function The concept of the RBF Network Scope & Advantages of RBF Conclusion References Introduction In machine learning, problem-solving based on hyperplane-based algorithms heavily depends upon the distribution of the data points in the space. However, it is a known fact that real-world data rarely follows theoretical assumptions. There are a lot of transformation functions that can convert the natural shape of the data points into theoretically recommended distributions persevering the hidden patterns of the data. Radial Basis is one such renowned function which is discussed in a lot of machine learning textbooks. In this article, we will learn about basic intuition, types and usage of the Radial basis function. The Basic Intuition of a Radial Basis Function The radial basis function is a mathematical function that takes a real-valued input and outputs a real-valued output based on the distance between the input value projected in space from an imaginary fixed point placed elsewhere. This function is popularly used in many machine learning and deep learning algorithms such as Support Vector Machines, Artificial Neural Networks, etc. Let us understand the concept and the usage of this mathematical function. In real-time, whenever we solve complex machine learning problems using algorithms such as SVM, we need to project all of our data points in an imaginary multidimensional space where each feature will be a dimension. Let's assume we have a classification problem to predict whether a student will pass or fail the examination. We have the following features as independent variables: Marks in internal exams Marks in projects Attendance percentage So, these 3 independent variables become 3 dimensions of a space like this- Let’s consider that our data points look like this where- The green colour represents the students who passed the examination The red colour represents the students who failed the examination Now, SVM will create a hyperplane that travels through these 3 dimensions in order to differentiate the failed and passed students- So, technically now the model understands that every data points which falls on one side of the hyperplane belong to the students who passed the exams and vice versa. In our example, it was easy to create the hyperplane because a linear and straight hyperplane was enough to discriminate the 2 categories. But in real-time complex projects, these relations may get violated in many scenarios. Especially when you have hundreds of independent variables, there is no possibility of getting a linear relationship between data points such that it will be difficult to create an optimal hyperplane. In such scenarios, researchers usually apply the Radial basis function to each of the data points so that they will be able to pass a linear hyperplane across the data points to easily solve the problem. Consider that our data points are looking like this in the space- It is clear that we cannot use a linear hyperplane such that it can group the data points according to their classes. RBF will help us in these kinds of scenarios. Some researchers will usually project these data points in much higher dimensions so that the distance between the data points will be increasing so that they can apply some function (RBF or any other function) to build a hyperplane. But it is not necessary to build high dimensions since it is always the decision of the statistician/researcher who understands the patterns in the data. Next, we have to mark an imaginary point in the space like this wherever we need. After that, we need to draw some concentric circles based on this imaginary point. The distance between the centre and any data point positioned in the boundary of the circle is called the radius. After calculating the radius, we need to pass this value inside a mathematical function (RBF) that will return a real value. The returned value will be the transformed magnitude of a particular data point used for further proceedings. Types of Radial Basis Functions There are multiple types of radial basis functions. Each of them will transform the input value in a different way. Some of them are- Radial Basis Function Multiquadratic Where, r is the radius ε is a constant The function will look like this with respect to time, Radial Basis Function Inverse Multiquadric Where, r is the radius ε is a constant Radial Basis Function Gaussian Where, r is the radius ε is a constant I will explain intuitively what these functions will do intuitively in the space. There are 2 different processes that are done by these functions- Expanding the data points across the horizontal direction Compressing the data points across the vertical direction The process of expansion will visually somewhat look like this- The process of compression will visually somewhat look like this- After the expansion and compression, the data points would have been transformed like this- Now, we can easily construct a linear hyperplane that can classify the data points like this- The Concept of the RBF Network Sometimes, RBF is also used along with artificial neural networks with one hidden layer. In such types of networks, RBF will be used as activation functions in the hidden layers. Apart from the hidden layer, there will be an input layer that contains several neurons where each one of them represents a feature variable and the output layer will be having a weighted sum of outputs from the hidden layer to form the network outputs. Such networks are called RBF networks. Scope & Advantages of RBF With the help of the RBF function, it is possible to solve the problems in datasets that have complex non-linear distributions. RBF function has a strong tolerance to input noise In RBF neural network, there will be only one hidden layer which is very easy to handle. Hidden patterns in the distribution can be generalized better after applying the RBF function. In RBF neural network,  we can easily interpret what is the meaning/function of each node in the hidden layer of the RBNN. This is difficult in Multi-layer perception. Some of the hyperparameters present in Multi-layer perceptron such as the number of nodes in the hidden layer, number of hidden layers etc. are difficult to optimise. But these are not found in RBF neural networks. Conclusion In this article, we discussed one of the most useful transformation functions in machine learning. I have tried to explain this complicated concept without many in-depth mathematical calculations in a lucid manner targeting beginners in the AIML learning space. This function is available as an inbuilt library in most data science-oriented programming languages such as Python or R. Hence, it is easy to implement this once you understand the theoretical intuition. I have added the links to some of the advanced materials in the references section where you can deep dive into the complex calculations if you are interested. References Radial Basis Functions - Wikipedia 2014-04-23 at the Radial Basis Function networks Archived Wayback Machine Broomhead, David H.; Lowe, David (1988). (PDF). . : 321–355. Archived from (PDF) on 2014-07-14. "Multivariable Functional Interpolation and Adaptive Networks" Complex Systems 2 the original (1977). "Restart procedures for the conjugate gradient method". . (1): 241–254. : . . Michael J. D. Powell Mathematical Programming 12 doi 10.1007/bf01593790 S2CID 9500591 Sahin, Ferat (1997). (M.Sc.). . p. 26. : . Radial basis functions were first introduced by Powell to solve the real multivariate interpolation problem. A Radial Basis Function Approach to a Color Image Classification Problem in a Real-Time Industrial Application Virginia Tech hdl 10919/36847

Radial Basis Functions: Types, Advantages, and Use Cases

Funciones de base radial: tipos, ventajas y casos de uso

Fonctions de base radiales : types, avantages et cas d'utilisation

रेडियल बेसिस फ़ंक्शंस: प्रकार, लाभ और उपयोग के मामले

放射基底関数: タイプ、利点、および使用例

Funções de base radial: tipos, vantagens e casos de uso

Các chức năng cơ sở xuyên tâm: Loại, Ưu điểm và Trường hợp sử dụng

径向基函数：类型、优点和用例

Artificial Neural Networks (ANNs) Have Transformed the Way We Interact With Computers

Hope you already know the basics of Recurrent Neural Networks (RNN)s. If not, feel free to refer . In Natural Language Processing (NLP), the RNNs have played a major role in sequence modeling. Let’s see what RNNs can do and what are their weak points. Without jumping into the topic, let’s move slowly! this article In an RNN, we feed the output of the previous timestep as an input in the next timestep. We found that they work well with sequential information like sentences. For clarity, look at the example below: If we consider an example of predicting the next word based on previous words in a sentence, using an RNN, the below image depicts how the RNN behaves in each timestep. The entire hidden state is depicted in yellow color rectangle. You can see that finally the hidden state contains a summary of the sentence. This is how the neurons are connected and RNN appears. But at each timestep, they have individual losses. (You can sum up them together to get a single loss value). If considered backpropagation, in RNN we have an additional parameter called ‘time’ other than the weight matrix. Timestep 5 will propagate the gradient in the usual way. At timestep 4, we have to consider the gradient of timestep 5 also. At timestep 3 we have to consider the gradients of all the timesteps from last timestep. In this way, in the backpropagation of RNN for timestep r, we have to propagate the gradients of last timestep to timestep r-1. What you could see from the above? ✔ Rather than having one or two previous words to predict the next word in a sentence, this approach is dependency preserving! By considering just the previous word for example ‘a’ in above context, neural network may have many possibilities to predict: a river, a student, a car etc. (The possibilities depend on the words in its’ corpus). But in this RNN-based approach, we have a sequence ‘Anne bought a’ to predict next word. So now it has very low possibilities for the next word to become ‘river’ or ‘student’. This is a significant outcome that can be gained using an RNN. So, what’s wrong? Just recall the theoretical stuff I just explained with the example. : Although we discuss that RNN remembers previous content, all the internal executions happen in mathematically bounded environments. The hidden layer’s output is a vector which has a maximum size. So that, when the information exceeds that size, the RNN starts forgetting the stuff. This happens over long distances. For example if you look at the given example, in timestep 2 hidden state had no word to remember, in timestep 2 it was only one word. But at timestep 5 it had 4 words. If that exceeds the capacity of the output vector, it will decay the information. Information decay In the backpropagation process, if the gradient of the activation function is a value between 0 and 1 (example : 0.3), the gradients of the last timesteps will be repeatedly multiplied with that value. (For the sake of understanding, just take the gradient of last timestep as 1 and, at timesteps 4,3,2,1 multiply it with the gradient of activation function(0.3) : 1 x 0.3 x 0.3 x 0.3 x 0.3 = 0.0081 . So now you can understand that it may reach 0 in a longer sequence! The gradient vanishes for the time being! Vanishing gradient : This is the opposite of vanishing gradient. Just imagine the gradient of your activation function as 4.75 and the gradient of last timestep as 1 . What happens when backpropagating over 4 timesteps? 1 x 4.75 x 4.75 x 4.75 x 4.75 =509.06. The gradient grows rapidly with the time and it may have a very large value for the time being. If the sequence is very large the gradient may go beyond the value range of data type which is being used and the value may be marked as ‘NaN’, which will make the entire work as mess! Exploding gradient : For the time being, LSTM (Long Short Term Memory) was introduced, which is able to address the pitfalls of RNNs! First Published here *Lead Image by chenspec from Pixabay*

What Can Recurrent Neural Networks in NLP Do?

Artificial Intelligence is currently one of the most sought-after technologies, and so is NLP, a subset of AI. Recent years have been marked by tremendous research and development in AI. NLP is an important technology, and we use it every day. This article will give us some perspective on this emerging technology. We will look at its history and some of its typical applications. How and where can we learn NLP to create various real-life projects. We also briefly look at Python, arguably the most favorable programming language for NLP and some of its essential NLP libraries. Along the way, some learning resources have also been recommended. What is NLP? First things first: what exactly is NLP? We all know that computers can only understand 0s and 1s, but we humans communicate in various languages, thousands of languages. , to be exact. This creates a deadlock in our interaction with the computers. The traditional solution to this deadlock was a methodical interaction with computers by clicking on various options to perform the desired task. But nowadays, we can achieve this by talking to our computers through Cortana or Siri in our own language, and they can serve our tasks and even talk back to us in the same language. The underlying technology that makes this happen is Natural Language Processing or the NLP. 7139 NLP is an emerging interdisciplinary field of linguistics, computer science and artificial intelligence that makes computers understand and generate human language. This is done by feeding a large amount of data or corpus in case of language data to the computer, which analyses them to understand and generate meanings. History of NLP Although NLP has become very popular in recent years, its history goes back to the 1950s. The genesis of all the AI technologies lies with the computer scientist Alan M. Turing and his seminal paper titled .” The central question of his article was ‘ ’. In the report, Mr Turing proposed a benchmark to test the intelligence of computers in comparison to humans. He called it the Imitation Game, which later became known as the . Among other criteria was the machine’s ability to ‘understand and speak’ natural languages. The of 1954 was another development early in machine translation. The experiment demonstrated a fully automatic translation from Russian to English. The field of NLP, along with AI, has undergone continuous development since then. The growth in the field of NLP can be divided into three categories based on the underlying method or approach for solving NLP problems described in the next section. “ Computing Machinery and Intelligence (1950) Can machines think? Turing Test Georgetown experiment NLP Methods Rule based NLP (1950s — 1990s) The earliest methodologies used to achieve natural language processing by computers was based on a pre-defined set of rules, also called Symbolic NLP. A collection of hand-coded rules was fed into the computer, and it yielded results based on that. The early research in NLP was focused primarily on machine translation. Rule-based machine translation (RBMT) required a thorough linguistic description of the source and the target languages. The basic approach involved two steps: 1) finding a structural equivalent of the source sentence and the output using a parser and an analyzer for the source language and a generator for the target language, and 2) using a bilingual dictionary for a word-to-word translation to finally produce the output sentence. Given that a human language is pervasive and ambiguous, infinitely many such rules are possible. Obviously, hand-coding such a large number of rules is not possible. Thus, these systems were very narrow and could only produce results in some given scenarios. For example, the much-celebrated Georgetown experiment could only translate some 60 plus sentences from Russian to English. Statistical NLP (1990s — 2010s) With the advent of more powerful computers with higher processing speed, it was now possible to process a large amount of data. Statistical NLP took advantage of this, and new algorithms based on machine learning came into being. These algorithms are based on statistical models which make a soft, probabilistic decision to yield output(s). Instead of hard-coding rules as in Rule-based NLP, these systems automatically learn such rules by analyzing large amounts of data fed through real-world parallel corpora. In the case of Statistical NLP, the system does not generate one final output. Instead, it outputs several possible answers with relative probability. The drawback, however, was that its algorithms were challenging to build. It required a complex pipeline of separate sub-tasks like tokenization, parts-of-speech tagging, word sense disambiguation, and many more to finally produce the output. Neural NLP (2010s — present) As deep learning became popular during the 2010s, the same was applied to the NLP. Deep neural network-style machine learning methods became widespread in NLP too. This approach, too, uses statistical models to predict the likelihood of an output. However, unlike Statistical NLP, it incorporates the entire sentence in a single integrated model wiping out the need to build that complex pipeline of intermediate subtasks as in statistical models. In this approach, the system uses an artificial neural network. The artificial neural network system, in theory, tries to mimic the neural network of the human brain. An artificial neural network consists of three layers: input, hidden, and output. The input layer receives the input and sends them to the hidden layer, where all the computations are performed on the data. The output is then transferred to the output layer. The connection between the neurons is called weight. The initial value of weights is set randomly, which changes during artificial neural network learning. These weights are crucial in determining the probability of an output. Common NLP Tasks Here is a non-exhaustive list of some of the most common tasks in natural language processing. Note that some of these tasks may not be an end in themselves but serve as subtasks in solving other tasks which have real-world applications. — separate a continuous text into words Tokenisation — determine the parts of speech of each word in a sentence Parts-of-speech tagging — filter out high-frequency words like to, at, the, for, etc Stopword removal — remove inflections (prefix and suffix) (e.g., driving → driv) Stemming — remove inflections and return base form of the word (e.g., driving → drive) Lemmatization — determine which word refers to which words in a sentence/text Coreference resolution — determine and visualize the parse tree of a sentence Parsing — select contextual meaning of a polysemic word Word sense disambiguation — determine the proper nouns in a sentence/text Named entity recognition — identify relationships among named entities Relationship extraction — determine the text printed in an image Optical character recognition (OCR) — convert speech into text Speech Recognition — separate a speech into words Speech Segmentation — convert text to speech Text-to-speech — produce a summary of a larger text Automatic summarisation — detect and correct grammatical errors in a text Grammatical error correction — automatically translate a text from one language to another Machine translation — convert text into machine-readable code Natural language understanding (NLU) — make machine produce natural language Natural language generation (NLG) Python for NLP Python is a preferred programming language for NLP. As of February 2022, it is the programming language. Python’s ubiquitous nature and its application to a wide array of fields make it so popular. most popular While programming languages like Java and R are also used for NLP, Python is a clear winner. Python is easy to learn and understand because of its transparent and straightforward syntax. Python offers perhaps the largest community of developers, which can be really helpful in case the code needs some debugging. In addition, Python seamlessly integrates with other programming languages and tools. Most importantly, Python is backed by an extensive collection of libraries that enables developers to quickly solve NLP tasks. Resources : Start with this five-part specialization program on Coursera. It will provide a complete overview of Python programming. Python for Everybody : Read this free to read online book by Al Sweigart for step-by-step instructions and guided projects. Automate Boring Stuff with Python the official Python tutorial and documentation : Python tutorial NLTK NLTK — the Natural Language Toolkit — is a suite of open-source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. is the most popular library for NLP. It has a massive active community with over 10.4k stars and 2.5k forks on its . It was developed at the University of Pennsylvania by Steven Bird and Edward Loper and was released in 2001. NLTK is freely available for Windows, Mac OS X, and Linux. It has built-in support for more than 100 corpora and trained models. NLTK comes with a free written by its creators, a comprehensive guide for writing Python programs, and working with NLTK. They also have an active on google groups. Natural Language Toolkit or NLTK GitHub repository ebook discussion forum Resources: : Watch this introductory video on freeCodeCamp’s YouTube channel for getting started Natural Language Processing (NLP) Tutorial with Python & NLTK : Read the official NLTK book written by its creators for a deeper understanding of NLP and NLTK Natural Language Processing with Python spaCy Industrial-strength Natural Language Processing (NLP) in Python. is relatively young but very hot right now. Its has more than 22.4k stars and 3.7k forks, much higher than NLTK. It was written in Python and Cython, making it fast and efficient at handling large corpus. It is an industry-ready library and was designed for production usage. spaCy GitHub repository Some features of spaCy are: It provides support for linguistically-motivated tokenization in more than 60 languages. It has 64 pre-trained pipelines in 19 different languages. It provides pretrained transformers like BERT. It provides functionalities for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more. Resources: : freeCodeCamp’s video tutorial for getting started Natural Language Processing with spaCy & Python — Course for Beginners : The most essential concepts, explained in simple terms spaCy 101 CoreNLP A Java suite of core NLP tools. was initially written in Java and was developed at Stanford University. But it is equipped with wrappers for other languages like Python, R, JavaScript, etc. Thus, it is a library that can be used with most programming languages. It is a one-stop-shop destination for all the core NLP functionalities like linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 8 languages: Arabic, Chinese, English, French, German, Hungarian, Italian and Spanish. It has 8.3k stars and 2.6k forks on its . CoreNLP GitHub repository Resources: : Official CoreNLP documentation and pipelines Documentation TextBlob Simplified Text Processing is a Python library for processing textual data. It provides a simple API for the most common NLP tasks like POS tagging, tokenization, n-grams, etc. It is beginner-friendly and the fastest among other libraries. and is made on top of and . It has 8k stars and 1.1k forks on its at the time of writing this article. TextBlob NLTK pattern GitHub repository Resources: : Official TextBlob documentation and quick start guide Documentation Gensim topic modelling, document indexing similarity retrieval Gensim is a Python library for and with large corpora. was created by Radim Řehůřek in 2009. It is implemented in Python and Cython, making it incredibly fast. Plus, all its algorithms are memory-independent, i.e. they can process inputs larger than RAM size. It is mainly used to identify semantic similarities between two documents through vector space and topic modeling. It supports algorithms like Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. It works on vast data collections from specific categories and provides clear insight. Gensim’s has 12.9k stars and 4.2k forks. Gensim GitHub repository Resources: : Official documentation and tutorials. Documentation Conclusion Natural language processing is a sub-field of Artificial Intelligence under active research and development. We can see its practical applications all around us. From automatic captioning in YouTube videos to Chrome automatically translation webpages in foreign languages for us, assistive writing with Grammarly, our iPhone’s keyboard predicting keywords for un and so on. The possibilities are limitless. Natural language processing is indispensable for artificial intelligence and for our future technologies. References Bird, Steven, et al. . 1st ed, O’Reilly, 2009. Natural Language Processing with Python Budditha Hettige. . 2011. , . A COMPUTATIONAL GRAMMAR OF SINHALA FOR ENGLISH-SINHALA MACHINE TRANSLATION DOI.org (Datacite) https://doi.org/10.13140/RG.2.1.2330.6968 Heller, Michael. ‘Study Claims Siri and Google Assistant Are Equal’. , . Accessed 8 Feb. 2022. Phone Arena https://www.phonearena.com/news/Study-claims-Siri-and-Google-Assistant-are-equal_id115667 Singla, Karan. . 2015. , . Methods for Leveraging Lexical Information in SMT DOI.org (Datacite) https://doi.org/10.13140/RG.2.1.2138.7367 Turing, A. M. ‘I. — COMPUTING MACHINERY AND INTELLIGENCE’. , vol. LIX, no. 236, Oct. 1950, pp. 433–60. , . Mind DOI.org (Crossref) https://doi.org/10.1093/mind/LIX.236.433

HiddenLayer

hiddenlayer.com

51-200 employees

Since 2022

COMPANY RANKING

HIDDENLAYER

EVERGREEN INDEX #4832

HiddenLayer WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

HiddenLayer

hiddenlayer.com

51-200 employees

Since 2022

COMPANY RANKING

HIDDENLAYER

EVERGREEN INDEX #4832

RELATED COMPANIESRANK

STORIES ABOUT HIDDENLAYER

HiddenLayer WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!