Towards AI

FEBRUARY 2, 2024

Example: x=9, e=mahendra, t=strength, ………etc. Ex: height of a person=9ft, today temperature temperature=98F, object colour=white,……………etc. It is used to compare the dispersion of various samples. Example: any characters like x, y, z, a, b,……….etc. Example: any strings like height, length, temperature, weight, ……….etc.

Data Scientist 94

Data Scientist Data Analysis Data Analysis Analytics 94

Flipboard

FEBRUARY 13, 2023

Specifically, we will discuss the following in detail: Positive and Negative data samples required to train a network with contrastive loss Specific data preprocessing techniques (e.g., However, its formulation is based on a triplet data sample slightly different from the pairwise loss discussed previously.

Data Pipeline 106

Data Pipeline Deep Learning Deep Learning Python 106

Towards AI

JANUARY 30, 2024

GPT4, Stable Diffusion, Llama, BERT, Gemini Large Language Models (LLMs) Foundation models, trained on the “Transformer Architecture”, that can perform a wide array of Natural Language Processing (NLP) tasks like text generation, classification, summarisation etc. Examples: GPT 3.5, All LLMs are next “token” prediction models.

AI 85

AI AI Natural Language Processing Supervised Learning 85

Dataconomy

APRIL 22, 2024

60% of educators use AI tools in their classrooms, including AI-powered educational games, adaptive learning platforms, automated grading and feedback systems, etc. It offers a sample free version like many AI tools and allows 200 words to be created per day. AI has dramatically changed the educational process.

AI 103

AI AI Analytics Analytics 103

Towards AI

MARCH 23, 2023

Statistical Methods Data samples can be generated from a probability distribution with certain characteristic statistical features like mean, variance, skew, etc. For instance, in the case of COVID detection, one assumes that the negative samples belong to a specific statistical distribution.

Deep Learning 98

Deep Learning Deep Learning Python AI 98

AssemblyAI

MAY 2, 2023

For example, if we are in the healthcare sector we may want to predict whether someone is at risk for cancer given some biometric data - height, weight, smoking history, blood pressure, etc. Discriminative AI works in other domains too, like in the banking sector The given data - height, blood pressure, transaction history, etc. -

AI 129

AI AI Support Vector Machines Artificial Intelligence 129

Mlearning.ai

APRIL 17, 2023

PyTorch Lightning ⚡️: You’re probably using the wrong metric for early-stopping or model checkpointing Photo by David Marcu on Unsplash While using the PyTorch Lightning Trainer API , we monitor some metric for early-stopping, model checkpointing, etc. Mathematically: where, m_b is the metric over the samples of batch b.

Algorithm 52

Algorithm ML ML Machine Learning 52

DrivenData Labs

JANUARY 11, 2023

One particularly powerful capability they have is collecting rock and soil samples and taking measurements that can be used to determine their chemical makeup. Results Over the course of the competition, participants tested nearly 500 solutions and were able to contribute powerful models to the analysis of rock and soil samples using GCMS.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

Data Science Dojo

FEBRUARY 15, 2023

metaverse, quantum-computing, etc.). What problem-solving tools next digital age has to offer Thanks to Moore’s law (e.g., as described via the relevant Wikipedia article here: [link] ) and other factors, the digital age will keep producing hardware and software tools that are both wondrous, and/or overwhelming (e.g., IoT, Web 3.0,

K-nearest Neighbors 195

K-nearest Neighbors Decision Trees Support Vector Machines Algorithm 195

Mlearning.ai

APRIL 11, 2023

To address this, one can increase the capacity of the model by selecting a more powerful class of function or look at some hyperparameters (number of iteration/epochs, learning rate, optimizer, etc). Sample-wise double descent Here the phenomenon is observed by varying the size of the training dataset.

Cross Validation 52

Cross Validation Machine Learning Machine Learning Deep Learning 52

Flipboard

MARCH 6, 2023

Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. We also used Keras and TensorFlow to develop modules that allow us to process our dataset and generate triplet data samples that can be used to train our model.

Deep Learning 97

Deep Learning Deep Learning Data Pipeline Computer Science 97

PyImageSearch

MARCH 20, 2023

This dataset is then used to create our DataLoaders, allowing us to apply pre-processing transformations using the MapFunction Class and generate batches of data samples. Next, we develop our data pipeline to allow us to sample batches for training and validation. Finally, the data pipeline returns two data loaders (i.e.,

Deep Learning 117

Deep Learning Deep Learning Data Pipeline Python 117

Mlearning.ai

MARCH 6, 2023

The generator network creates new data samples, such as images or audio, while the discriminator network distinguishes between the generated and actual samples from a dataset. In contrast, the Discriminator model estimates the likelihood of a given sample originating from the training data rather than the Generator.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Deep Learning 52

Google Research AI blog

JUNE 29, 2023

A straightforward way to produce this unlearned model is to retrain the model on an adjusted training set that excludes the samples from the forget set. demographics, age groups, etc.). An unlearning algorithm takes as input a pre-trained model and one or more samples from the train set to unlearn (the "forget set").

Algorithm 145

Algorithm Machine Learning Machine Learning Deep Learning 145

Dataconomy

NOVEMBER 23, 2023

After adding this additive, the food’s color, flavor display, texture, feel, noise (cackle), etc., Again, per another research , every company uses nutrient preservatives in the foodstuff during processing. remains intact for a long duration. These compounds contribute to the sensory experience of drinking and eating.

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Dataconomy

DECEMBER 11, 2023

Upload writing samples (essays, papers, etc.) Once you’re logged in, click on the “Training” tab and upload your writing samples. If you don’t have an account yet, sign up for free on the JotBot website. This will help JotBot understand your writing style, tone, sentence structure, and preferences.

AI 103

AI AI Artificial Intelligence Artificial Intelligence 103

Towards AI

JULY 20, 2023

Keep shape of each sample dynamic. vision, computing numbers etc.). For example, was it fast or slow, differences across languages etc. Audio samples of the paper is… github.com Connected Papers ? You can store images as big as 100k by 100k! This way you can store small and big arrays as 1 array.

Deep Learning 82

Deep Learning Deep Learning Data Analysis Data Analysis 82

Heartbeat

JANUARY 12, 2024

These 52K instructions span different domains such as text summarization, fashion, maths, food, etc., I have prepared 8 counts of data with food information and another 8 counts with diverse topics such as quantum computers, psychology, fashion, etc. and they are widely employed to fine-tune LLM models.

ML 52

ML ML Deep Learning Deep Learning 52

Heartbeat

MAY 30, 2023

GANs are a type of neural network design used to develop new data samples that are similar to the training data. Healthcare: DRL has been used to improve medical diagnosis accuracy, enhance the treatment plans of patients, predict the growth of a particular disease in a patient, etc. What are Generative Adversarial Networks (GANs)?

Deep Learning 72

Deep Learning Deep Learning Machine Learning Machine Learning 72

Snorkel AI

JULY 19, 2023

Instruction datasets typically contain multiple classes of instructions—for example: brainstorming, question answering, summarization, etc. In order to maintain a target dataset volume of at least 8k-10k samples and hedge against a drop in the diversity of un-modeled dataset features, we chose 0.5

AI 52

AI AI Natural Language Processing Machine Learning 52

Snorkel AI

JULY 19, 2023

Instruction datasets typically contain multiple classes of instructions—for example: brainstorming, question answering, summarization, etc. In order to maintain a target dataset volume of at least 8k-10k samples and hedge against a drop in the diversity of un-modeled dataset features, we chose 0.5

AI 52

AI AI Natural Language Processing Machine Learning 52

Data Science Dojo

MARCH 3, 2023

We’re going to return a trained model as well as the r-squared value for the test sample, so we have a quick and straightforward measure of the model’s performance on the test set that we can return along with the model object. Now we have a model, and we’re ready to put together the app!

Machine Learning 195

Machine Learning Machine Learning Data Science Python 195

Towards AI

AUGUST 14, 2023

TheSequence is a no-BS (meaning no hype, no news, etc) ML-oriented newsletter that takes 5 minutes to read. Inference Enhancement: By integrating rejection sampling during inference, the quality of results saw a significant elevation, bearing testament to the power of thoughtful methodological augmentation.

Machine Learning 89

Machine Learning Machine Learning Artificial Intelligence Artificial Intelligence 89

Dataconomy

NOVEMBER 20, 2023

There is a service for case study solution , which support students with free samples. Efficient and adequate conversations Note that using the same online resources (such Moodle, Zoom, Microsoft Teams, etc.) Indeed true! Along with online classes, you need constant conversation with your teachers as well.

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Snorkel AI

JUNE 12, 2023

summarization, chat, Q&A, etc.). Snorkel’s Foundation Model Data Platform Snorkel’s Foundation Model Data Platform consists of three core solutions: Snorkel Foundry: For programmatic curation and management of datasets for domain-specific pre-training of foundation models.

AI 145

AI AI Algorithm Azure 145

phData

JULY 19, 2023

Snowflake is also improving Snowsight to support newer features like Task Observability, Snowpark, etc. With the introduction of Snowpark, customers can write stored procedures in Python, Scala, Java, etc. Snowflake will create a worksheet with sample Python code, as shown below. What is Snowflake’s Snowpark?

Python 52

Python SQL Database 52

phData

JULY 19, 2023

Snowflake is also improving Snowsight to support newer features like Task Observability, Snowpark, etc. With the introduction of Snowpark, customers can write stored procedures in Python, Scala, Java, etc. Snowflake will create a worksheet with sample Python code, as shown below. What is Snowflake’s Snowpark?

Python 52

Python SQL Database 52

Snorkel AI

JUNE 12, 2023

summarization, chat, Q&A, etc.). Snorkel’s Foundation Model Data Platform Snorkel’s Foundation Model Data Platform consists of three core solutions: Snorkel Foundry: For programmatic curation and management of datasets for domain-specific pre-training of foundation models.

AI 141

AI AI Algorithm Azure 141

ODSC - Open Data Science

MARCH 14, 2023

One of the simplest and most popular methods for creating audience segments is through K-means clustering, which uses a simple algorithm to group consumers based on their similarities in areas such as actions, demographics, attitudes, etc. These aspects could be demographic information, behaviors, attitudes, etc.

Clustering 98

Clustering Python Algorithm Data Science 98

Towards AI

MAY 3, 2024

As you can see, we are dealing with relatively simple formats, and using relatively simple, well-established technologies (convolutional networks, vision transformers, etc.), One of the first neural network architectures I encountered was the Tensorflow CNN example, which categorizes images from the well-known CIFAR-10 dataset.

AI 75

AI AI Python Artificial Intelligence 75

Google Research AI blog

JULY 11, 2023

Enabled by ArchGym, our results indicate that with a sufficiently large number of samples, any of a diverse collection of ML algorithms are capable of finding the optimal set of architecture design parameters for each target problem; no one solution is necessarily better than another. learning rate, warm-up steps, etc.).

Machine Learning 74

Machine Learning Machine Learning ML ML 74

Snorkel AI

JULY 19, 2023

Instruction datasets typically contain multiple classes of instructions—for example: brainstorming, question answering, summarization, etc. In order to maintain a target dataset volume of at least 8k-10k samples and hedge against a drop in the diversity of un-modeled dataset features, we chose 0.5

AI 40

AI AI Natural Language Processing Machine Learning 40

Snorkel AI

AUGUST 22, 2023

.” Meanwhile, pattern-related tags include “Chevron Rug,” “Striped Curtain,” “Floral Accent Pillow,” etc. Examples of theme-related tags include “Outer Space Rug,” “Bird Decorative Object,” etc. style, design, product types, shape, pattern, season etc.) due to two main reasons.

Machine Learning 52

Machine Learning Machine Learning Data Preparation Artificial Intelligence 52

Snorkel AI

AUGUST 22, 2023

.” Meanwhile, pattern-related tags include “Chevron Rug,” “Striped Curtain,” “Floral Accent Pillow,” etc. Examples of theme-related tags include “Outer Space Rug,” “Bird Decorative Object,” etc. style, design, product types, shape, pattern, season etc.) due to two main reasons.

Machine Learning 52

Machine Learning Machine Learning Data Preparation Artificial Intelligence 52

Towards AI

JULY 19, 2023

The other approach is to use a sampling technique: Either down-sampling the majority class or oversampling the minority class, or both [1]. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Towards AI

JULY 17, 2023

Whereas we do not know much about F(X), we know almost everything about H(X): its formula, parameters, etc. We can generate sample data by: #include <random>std::default_random_engine dre(time(0));std::normal_distribution<double> gaussian_dist(0., The answer is: we know H(X). where N(0, 0.1)

Deep Learning 68

Deep Learning Deep Learning Machine Learning Machine Learning 68

The MLOps Blog

DECEMBER 19, 2022

Read also 24 Evaluation Metrics for Binary Classification (And When to Use Them) The primary issue while solving a large-scale classification problem using any of the above-mentioned algorithms is that, given a class, the number of samples collected could sometimes be very limited. Some groups also call this field Extreme Classification.

ML 52

ML ML Algorithm Deep Learning 52