Marktechpost

AUGUST 21, 2023

The authors have introduced P-DNN as an innovative solution to the reconfigurability problems of ONNs. Unlike the traditional methods that require complete retraining when a new task arises, P-DNN can switch recognition tasks by swapping the pluggable values in the network.

Neural Network 98

Neural Network Deep Learning Machine Learning AI Researcher 98

Marktechpost

NOVEMBER 12, 2023

This idea came from comparing the two approaches to effective finetuning: p-tuning learns a task prefix embedding in the input. Since the picture features in the shallow alignment techniques behave similarly to the prefix embedding in p-tuning, a similar occurrence may also occur in VLM.

NLP 122

NLP Natural Language Processing AI Researcher AI Research 122

Extreme Tech

AUGUST 10, 2023

The mobile chip will feature a 4+8 P-core and E-Core configuration.

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75

Marktechpost

JULY 22, 2023

This process results in high-resolution and diverse images that match the input text, achieved through a U-net architecture that captures and incorporates visual features of the input text. The conditioning space in these models is referred to as the P space, defined by the language model’s token embedding space.

Neural Network 52

Neural Network Artificial Intelligence Artificial Intelligence AI Researcher 52

Towards AI

MAY 4, 2023

An overview of the model architecture has been provided below: Input Layer: we will use 2 inputs (x1 and x2), relating to each feature in each training example. Output Layer: we will use a layer for the prediction (p) consisting of one neuron. For each training example, two input features (x1 and x2) will be used.

Neural Network 81

Neural Network Python AI AI 81

Marktechpost

DECEMBER 18, 2023

To do this, a tiny Multilayer Perceptron (MLP) has been added on top of the word features, allowing it to regress to each word’s pixel location. The architecture of the model comprises an image encoder, a prompt encoder, and a prompt feature extractor. With remarkable performance metrics, including 89.8

Large Language Models 119

Large Language Models Computer Vision Natural Language Processing Artificial Intelligence 119

Pickl AI

DECEMBER 27, 2023

This powerful tool empowers businesses to analyze user behavior, optimize product features, and elevate performance, ensuring informed choices in a competitive landscape. Python’s statistical libraries provide functions to calculate p-values, allowing you to ascertain whether the observed differences are statistically significant.

Data Scientist 52

Data Scientist Data Science Python Data Analysis 52

BAIR

NOVEMBER 14, 2023

Asymmetric Certified Robustness via Feature-Convex Neural Networks TLDR : We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios. Illustration of feature-convex classifiers and their certification for sensitive-class inputs.

Neural Network 100

Neural Network Machine Learning Deep Learning 100

Mlearning.ai

JUNE 29, 2023

i) Min Pts:- First let us know first what is density at a point “P”. It can defined as the following. *=> Density at a point P = The no. So, at a given point “P”. Then the density at this point “P” is said to be 7. P has 7 points within it’s epsilon (ε) radius considering itself as a point. P is the Core Point.

Algorithm 52

Algorithm Data Mining Categorization Deep Learning 52

Towards AI

AUGUST 25, 2023

Describe the dataset, including the input features and target feature(s). Include summary statistics of the data, including counts of any discrete or categorical features and the target feature. Russell and P. In a few sentences, describe the following: What is the goal? Classify, predict, detect, translate, etc.

Software Engineer 96

Software Engineer Machine Learning Neural Network Artificial Intelligence 96

Mlearning.ai

FEBRUARY 11, 2023

Calculate the p-value Compare the p-value with the level of significance. p-value: The probability of null hypothesis being true. The p-values are compared against the level of significance for making decisions. It is denoted as p. It is denoted as Ha or H1. Steps: Formulate Null and Alternate Hypothesis.

Categorization 40

Categorization Explainability ML Data Science 40

Towards AI

NOVEMBER 1, 2023

Moreover, we will utilise the information from our time-series model as regression features to strengthen our regression’s predictive power. With that being said, before we get into that part, let’s get back to our clean dataset and perform some feature engineering. The way we perform feature engineering depends on the data types.

Categorization 98

Categorization Data Extraction AI AI 98

Artificial Corner

JULY 15, 2023

get() runtime = movie.xpath(' //p/span[@class = "runtime"]/text()').get() get() genre = (movie.xpath(' //p/span[@class = "genre"]/text()').get()).strip() get() director = movie.xpath(' //p[@class=""]/a[1]/text()').get() strip() rating = movie.xpath(' //strong/text()').get()

Data Analysis 98

Data Analysis Data Science Data Scientist Python 98

Towards AI

JULY 19, 2023

Data Preparation — Collect data, Understand features 2. Hypothesis for ADF— Null Hypothesis: Series is non-stationary or series has a unit root Alternative Hypothesis: Series is stationary or series has no unit root If the Null hypothesis cannot be rejected (and p-value >0.05) then the data is non-stationary.

Explainability 89

Explainability Deep Learning Python ML 89

Towards AI

MAY 15, 2024

This served as the first reality check and pushed me to do better at Feature Engineering. In another task, I asked the LLM to animate a hypothetical feature matrix with a column matrix representing labels, along with a rectangle focusing each of the columns one by one. AI visualizes a feature matrix. 0.3], [0, 0.4,

Convolutional Neural Networks 70

Convolutional Neural Networks Explainability Python AI 70

Towards AI

FEBRUARY 26, 2024

Introduction Project Objectives or Goals Dataset features Description Summary of the Approach 2. Data Pre-Processing Handling Missing Values Encoding Categorical Variables Feature Scaling Data Splitting (Training and Validation) 4. tick_params(axis='x', rotation=12) for p in ax[i].patches: Table of Contents 1.

Machine Learning 94

Machine Learning Categorization Data Analysis Python 94

Mlearning.ai

JULY 23, 2023

The algorithm learns from this data, understanding the distinguishing features of each category. These gentlemen Naive Bayes is a “naive” algorithm because it makes a very simplifying assumption: it assumes that every feature of the text (like each word in an email) is independent of every other feature. x 0.11 = 0.00009801 ?

Explainability 52

Explainability Categorization Algorithm NLP 52

IBM Journey to AI blog

AUGUST 22, 2023

We leverage retrieval-augmented generation tasks to create a question-answer feature or multi-lingual conversational chatbot (based on a documents or dynamic content from a broad knowledge base) that provides field service assistance in real time. Field services.

Generative AI 200

Generative AI Neural Network AI AI 200

Towards AI

JULY 17, 2023

The likelihood of each word being eliminated is determined by the p parameter. The likelihood of each word being eliminated is determined by the p parameter. pip install --upgrade googletrans==4.0.0-rc1from text back_translation = translator.translate(translation, dest=source_language).text

NLP 80

NLP Python OpenAI ChatGPT 80

Towards AI

APRIL 1, 2023

The two prominent ones are: Feature drift: The distribution of one or more input variables changes over time. P(X) changes but P(y|X) remains the same. Concept drift: The decision boundary change, i.e., P(y/X) changes but P(X) remains the same. Compute the centroids of P and Q in the Hilbert space.

Data Drift 52

Data Drift Machine Learning Python Algorithm 52

Artificial Corner

JULY 17, 2023

breakdown and show you about the zoom-in/zoom-out feature that also dropped with 5.2, So it should look something like “C L U B” or “H A P P Y”. Follow AI CREATION STATION As I mentioned earlier, I’ll be covering the zoom-in/zoom-out feature and how to turn them into your very own videos! I’ll do a full v5.2

AI 52

AI AI ChatGPT Artificial Intelligence 52

Heartbeat

NOVEMBER 15, 2023

It uses probability concepts to make predictions based on feature independence. It’s called “naive” because it assumes that a data point’s features (or characteristics) are independent. It computes the probabilities of various classes based on observed features and updates them using Bayes’ theorem as new evidence is introduced.

Machine Learning 52

Machine Learning Categorization Data Scientist Deep Learning 52

Dlabs.ai

MARCH 7, 2024

In this competitive era, companies are vying to innovate their digital products with GPT-based features or even spearhead entire projects centered around this technology. This innovative feature brings a touch of human-like, AI-generated interactions right to your wristwatch. Keen on staying ahead in the race and seeking inspiration?

ChatGPT 110

ChatGPT AI AI AI Chatbots 110

ODSC - Open Data Science

DECEMBER 28, 2023

This feat is achieved by integrating a tiny Multilayer Perceptron on top of the word features, allowing the model to regress to each word’s pixel location. It features an architecture comprising an image encoder, a prompt encoder, and a prompt feature extractor. Notably, PixelLLM achieved 89.8

Natural Language Processing 52

Natural Language Processing Computer Vision Large Language Models Data Science 52

Towards AI

MAY 31, 2023

Now after mapping all the data points on the Cartesian plane, when there is a new data point in square feet feature, the K neighbor’s average price value of their square feet of the house is the price of the new data point. This is computed as the pth root of the sum of absolute difference raised to the power of p.

Algorithm 88

Algorithm Python AI AI 88

Towards AI

AUGUST 16, 2023

Monitoring Models in Production There are several types of problems that Machine Learning applications can encounter over time [4]: Data drift: sudden changes in the features values or changes in data distribution. Russell and P. MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. References [1] E. 15, 2022. [4]

Machine Learning 88

Machine Learning Software Development Data Drift AI 88

Marktechpost

APRIL 29, 2024

As an additional useful feature, SynthEval’s benchmark module permits the simultaneous evaluation of multiple synthetic renditions of the same dataset. If you specify a file path as a preset, SynthEval will try to load the file. If users use any non-standard setup, a new config file will be saved in JSON format for repeatability.

Machine Learning 116

Machine Learning Computer Vision Algorithm Categorization 116

Mlearning.ai

JULY 1, 2023

for p in face.location_data.relative_keypoints]) # convert normalize values into pixel coordinates, and turn them into integers key_points_coords = np.multiply(key_points,[frame_width,frame_height],).astype(int) for p in face.location_data.relative_keypoints]) key_points_coords = np.multiply(key_points,[frame_width,frame_height]).astype(int)

Python 52

Python Computer Vision Neural Network Explainability 52

Heartbeat

SEPTEMBER 22, 2023

K-Nearest Neighbors (KNN) Classifier: The KNN algorithm relies on selecting the right number of neighbors and a power parameter p. Additionally, the p parameter influences the distance metric used for calculating the neighbors. When p = 1, the Manhattan distance is used, while p = 2 corresponds to the Euclidean distance.

Machine Learning 64

Machine Learning ML Algorithm Automation 64

Mlearning.ai

FEBRUARY 28, 2023

[link] As a final note, I want to note that it’s crucial to check the p-value of the Chi-Square Test to validate how the association between the values is statistically significant. Categorical - Continuous As a final destination, we arrived at the correlation between categorical and continuous features.

Categorization 52

Categorization Data Analysis Neural Network ML 52

Towards AI

AUGUST 16, 2023

When the agent is a computer, the learning process is called machine learning (ML) [6, p. Data description: This step includes the following tasks: describe the dataset, including the input features and target feature(s); include summary statistics of the data and counts of any discrete or categorical features, including the target feature.

Software Engineer 83

Software Engineer Machine Learning Artificial Intelligence Artificial Intelligence 83

Towards AI

DECEMBER 20, 2023

What sets PostgreSQL apart is its extensibility, standards compliance, and advanced features, making it a powerful choice for handling diverse data scenarios. It’s known for being adaptable, following standards, and having advanced features, making it great for handling different types of data.

Python 99

Python AI AI Machine Learning 99

AI Impacts

MAY 16, 2023

Featured image by Pxfuel. Thanks for reading AI Impacts blog! Subscribe for free to receive new posts and support my work. 2 Sun & Rose. Supply Chain Complexity in the Semiconductor Industry: Assessment from System View and the Impact of Changes.

Robotics 130

Robotics Artificial Intelligence Artificial Intelligence AI 130

ODSC - Open Data Science

NOVEMBER 30, 2023

The H200 Tensor Core is based on the NVIDIA Hopper architecture the GPU features HBM3e, providing an unprecedented 141 gigabytes of memory at an astounding 4.8 Over the last few years, more and more research teams have been depending on the power of models to find new p rotein combinations and detect Parkinson’s disease.

Large Language Models 52

Large Language Models Data Science Artificial Intelligence Artificial Intelligence 52

Mlearning.ai

APRIL 17, 2023

Feature Engineering The researcher had followed a 3-step process for getting the features in the dataset ready for model testing: a baseline feature set containing only original features, an augmented feature set containing engineered features and a reduced feature set that removes correlated and unimportant features.

Neural Network 52

Neural Network Categorization Algorithm Python 52

Towards AI

SEPTEMBER 22, 2023

The Encoder processes the input and generates a feature representation that encodes the meaning and context of the input. The Decoder takes the feature representation from the Encoder and starts generating new text based on that context token by token. The tokenized input text is passed to the Encoder part of the pre-trained model.

LLM 57

LLM Large Language Models Algorithm AI 57

Marktechpost

APRIL 12, 2024

Moreover, the book also features some advanced topics including generalized linear models, Bayesian additive regression trees, and survival analysis. It also touches upon topics like p-values, significance, insignificance, confidence intervals, and regression.

Data Scientist 104

Data Scientist Python Data Science Data Analysis 104