Analytics Vidhya

NOVEMBER 3, 2021

Overview Keras is a Python library including an API for working with neural networks and deep learning frameworks. Keras includes Python-based methods and components for working with various Deep Learning applications. The post Training Neural Network with Keras and basics of Deep Learning appeared first on Analytics Vidhya.

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Analytics Vidhya

FEBRUARY 16, 2022

The post Classification without Training Data: Zero-shot Learning Approach appeared first on Analytics Vidhya. Since 2012 after convolutional neural networks(CNN) were introduced, we moved away from handcrafted features to an end-to-end approach using deep neural networks. These are easy to develop […].

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Data Science Analytics Analytics Deep Learning 260

Machine Learning Research at Apple

DECEMBER 17, 2023

*= Equal Contributors Federated Learning (FL) is a technique to train models using data distributed across devices. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.

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Analytics Vidhya

JANUARY 28, 2022

Whether you’ve been in the Data Science and Machine Learning arena for quite some time or have just stepped into this exciting avenue – you sure have stumbled upon Python’s popular Machine Learning model building library, you guessed it right – Scikit learns. One […].

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Analytics Vidhya

MARCH 10, 2021

ArticleVideo Book Objective Image augmentation is an efficacious technique when we don’t have an ample amount of data for training a deep learning model. The post Image Augmentation Techniques for Training Deep Learning Models appeared first on Analytics Vidhya.

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Deep Learning Deep Learning Analytics Analytics 212

KDnuggets

NOVEMBER 4, 2021

Toloka is a crowdsourced data labeling platform that handles data collection and annotation projects for machine learning at any scale. In this Nov 11 Live Demo, Learn how to get reliable training data for machine learning.

Machine Learning 250

Machine Learning Machine Learning 250

Machine Learning Research at Apple

JUNE 1, 2023

Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. We present a multimodal robustness framework to provide a systematic analysis of common multimodal representation learning methods.

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Analytics Vidhya

DECEMBER 18, 2020

The post Tutorial to data preparation for training machine learning model appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction It happens quite often that we do not have all the.

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Data Preparation Machine Learning Machine Learning Data Science 227

Machine Learning Research at Apple

JULY 23, 2023

Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large population of devices, FL with DP produces a performant model in a timely manner. In this work, we thus propose expanding the population based on…

Machine Learning 100

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Data Science Dojo

APRIL 29, 2024

These AI systems learn from large datasets to recognize patterns and structures, which they then use to generate new, original outputs similar to the data they trained on. How Generative AI Works Generative AI operates by learning from vast amounts of data to generate new content that mimics the original data in form and quality.

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Analytics Vidhya

DECEMBER 7, 2020

Summary Share my knowledge of training the style GAN step by step. The post Training StyleGAN using Transfer learning on a custom dataset in google colaboratory appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

Data Science 218

Data Science Analytics Analytics Deep Learning 218

BAIR

JULY 14, 2023

Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. However, most use cases of diffusion models are not directly concerned with matching the training data, but instead with a downstream objective.

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Towards AI

AUGUST 27, 2023

A step-by-step tutorial on training state-of-the-art AI models with MMdetection 3.1, Terminal output of a running training process with MMDetection If you want to train neural networks, you can use the popular deep learning libraries TensorFlow or PyTorch. CVAT, and TensorBoard This member-only story is on us.

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Data Science Dojo

NOVEMBER 10, 2023

This is done by training machine learning models on large datasets of existing content, which the model then uses to generate new and original content. Want to build a custom large language model ? PyTorch: PyTorch is another popular open-source machine learning library that is well-suited for generative AI.

Python 524

Python AI AI Machine Learning 524

SAS Software

DECEMBER 14, 2023

Developing an accurate understanding of statistics will help you build robust machine learning models that are optimized for a given business problem. The post New SAS Training Course: Statistics You Need to Know for Machine Learning appeared first on SAS Blogs.

Machine Learning 75

Machine Learning Machine Learning Data Science 75

Analytics Vidhya

MAY 9, 2024

Introduction Cross-validation is a machine learning technique that evaluates a model’s performance on a new dataset. It involves dividing a training dataset into multiple subsets and testing it on a new set. This prevents overfitting by encouraging the model to learn underlying trends associated with the data.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. However, there is less focus today on the efficiency of the training data itself.

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Analytics Vidhya

MARCH 28, 2024

Machine learning (ML) can seem complex, but what if you could train a model without writing any code? This guide unlocks the power of ML for everyone by demonstrating how to train a ML model with no code.

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Hacker News

APRIL 6, 2024

Schedule-Free Optimization in PyTorch. Contribute to facebookresearch/schedule_free development by creating an account on GitHub.

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NYU Center for Data Science

MAY 3, 2023

These object detection models are typically trained on generic data, instead of datasets specific for the needs of people with blindness. They gathered images of the objects from publicly available datasets and trained a YOLOv5x model to recognize the selected items. By Meryl Phair

Machine Learning 98

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Analytics Vidhya

APRIL 15, 2024

Introduction Overfitting in ConvNets is a challenge in deep learning and neural networks, where a model learns too much from training data, leading to poor performance on new data. This phenomenon is especially prevalent in complex neural architectures, which can model intricate relationships.

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Analytics Vidhya

APRIL 22, 2024

Introduction Deep learning is a fascinating field that explores the mysteries of gradients and their impact on neural networks. Solutions like ReLU activation and gradient clipping promise to revolutionize deep learning, unlocking secrets for training success.

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Deep Learning Deep Learning Analytics Analytics 273

Analytics Vidhya

MAY 2, 2024

However, pre-trained vanilla models are often biased and can produce harmful content. To improve performance, algorithms like Reinforcement Learning with Human Feedback and Direct Preference Optimization (DPO) can be used.

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Algorithm Analytics Analytics SQL 283

Analytics Vidhya

MAY 6, 2024

Introduction Machine learning (ML) has become a game-changer across industries, but its complexity can be intimidating. This article explores how to use ChatGPT to build machine learning models.

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Data Science Dojo

FEBRUARY 7, 2024

The development of generative AI relies on important machine-learning techniques in today’s technological advancement. It makes machine learning (ML) a critical component of data science where algorithms are statistically trained on data. An ML model learns iteratively to make accurate predictions and take actions.

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Machine Learning Machine Learning ML ML 243

KDnuggets

OCTOBER 3, 2019

There is no clear outline on how to study Machine Learning/Deep Learning due to which many individuals apply all the possible algorithms that they have heard of and hope that one of implemented algorithms work for their problem in hand.

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Analytics Vidhya

MAY 8, 2024

Introduction Have you ever thought robots would learn independently with the power of LLMs? In robotics, sim-to-real transfer refers to transferring policies learned in simulation to the real world. It’s happening now! DrEureka is automating sim-to-real design in robotics.

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Analytics Vidhya

APRIL 23, 2024

The answer lies in the vast datasets used to train them. Just like humans learn from exposure to information, LLMs […] The post 10 Open Source Datasets for LLM Training appeared first on Analytics Vidhya. But have you ever wondered what fuels these robust AI systems?

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Analytics Vidhya

SEPTEMBER 29, 2023

Introduction In deep learning, optimization algorithms are crucial components that help neural networks learn efficiently and converge to optimal solutions.

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Data Science Dojo

MARCH 18, 2024

The model might offer generic advice based on its training data but lacks depth or specificity – and, most importantly, accuracy. Additional training on a focused dataset sharpens the model’s expertise in a particular area, enabling it to perform with greater precision and understanding. Why use RAG?

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Database AI AI Natural Language Processing 369

insideBIGDATA

JULY 24, 2023

In this contributed article, Ihar Rubanau, Senior Software Developer at Sigma Software Group, discusses how transfer learning has become a popular technique in computer vision, allowing deep neural networks to be trained with limited data by leveraging pre-trained models.

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Becoming Human

MAY 22, 2023

[link] Introduction: Reinforcement Learning (RL) is a fascinating field of Artificial Intelligence (AI) that enables machines to learn and make decisions through interaction with their environment. In this blog, we will explore the steps involved in training your first RL agent, along with code snippets to illustrate the process.

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KDnuggets

MARCH 18, 2024

Learn how to automate machine learning training and evaluation using scikit-learn pipelines, GitHub Actions, and CML.

Machine Learning 298

Machine Learning Machine Learning 298

Mlearning.ai

FEBRUARY 2, 2023

Mind Map: Mistakes in ML model training This blog highlights some important mistakes that one can make while training a machine learning model. Machine Learning model training is the process of teaching a model how to recognize patterns in data. What can go wrong in ML model training?

Machine Learning 52

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Data Science Dojo

DECEMBER 8, 2023

However, mastering LLMs requires a comprehensive understanding of their underlying principles, architectures, and training techniques. GPT (Generative Pre-training Transformer) : GPT is known for its ability to generate human-quality text, making it suitable for tasks like creative writing and chatbots.

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Analytics Vidhya

MAY 2, 2024

Introduction Large Language Models are often trained rather than built, requiring multiple steps to perform well. These steps, including Supervised Fine Tuning (SFT) and Preference Alignment, are crucial for learning new things and aligning with human responses.

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Analytics Vidhya

NOVEMBER 16, 2023

Introduction A goal of supervised learning is to build a model that performs well on a set of new data. The problem is that you may not have new data, but you can still experience this with a procedure like train-test-validation split.

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