Mlearning.ai

JULY 4, 2023

If you would like to be able to test different LLMs locally for free and happen to have a GPU powered PC at home you’re in luck — thanks to the wonderful Open Source community, running different LLMs on Windows is very straightforward. What we will use: Windows 10 or 11  — I used Windows 11, but everything should work on Windows 10 as well.

LLM 98

LLM Auto-complete Python ML 98

NVIDIA

SEPTEMBER 21, 2023

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks and demonstrates how NVIDIA Studio technology improves creative workflows. Windows 11. AI-powered Ray Resstruction. With the launch of DLSS 3.5 NVIDIA Driver 536.75.

Neural Network 101

Neural Network Deep Learning AI AI 101

NVIDIA

MAY 24, 2023

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. GeForce RTX 4060 Ti 8GB is available now from a range of providers. The latest community challenge is here!

AI 62

AI AI AI Modeling 62

TheSequence

JANUARY 7, 2024

Created Using DALL-E Next Week in The Sequence: Edge 259: Our series about LLM reasoning dives into the fascinating tree-of-thoughts technique including the original paper. The robot uses imitation learning to master a series of complex tasks following specific demonstrations. What the videos —> Read more.

Robotics 52

Robotics Computer Vision LLM ML 52

ODSC - Open Data Science

OCTOBER 5, 2023

Microsoft Goes All in With AI Through Copilot in Latest Windows Update Microsoft’s Copilot will begin to roll out in its early form as part of a new and free update to the Windows 11 operating system.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Artificial Intelligence 52

PyImageSearch

MARCH 20, 2023

In the previous tutorial of this series, we tried to understand the formulation of triplet loss. Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application.

Computer Vision 117

Computer Vision Deep Learning Neural Network Python 117

DataRobot Blog

JUNE 27, 2022

In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Monitoring Model Metrics. Figure 2: Accuracy tab within a DataRobot model deployment.

Automation 59

Automation Data Drift Machine Learning ML 59

PyImageSearch

APRIL 24, 2023

TorchDynamo and TorchInductor (primarily for developers) In our previous lesson of this series, we saw how PyTorch 2.0’s In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. Ready to run the code immediately on your Windows, macOS, or Linux system? Here, Line 11 (i.e.,

Neural Network 103

Neural Network Computer Vision Deep Learning Python 103

Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Here, “margin” is a hyperparameter similar to the one we discussed in the definition of pairwise contrastive loss in the first part of this series. Looking for the source code to this post?

Computer Vision 97

Computer Vision Deep Learning Neural Network Artificial Intelligence 97

Flipboard

FEBRUARY 13, 2023

In the previous tutorial of this series, we looked into the different face recognition tasks (e.g., In this part of the series, we will discuss the specific techniques required to develop a dataset that can be used to train our face recognition network with contrastive losses. We discussed this in the previous part of this series.

Computer Vision 106

Computer Vision Deep Learning Neural Network Auto-complete 106

PyImageSearch

APRIL 3, 2023

In the previous tutorial of this series, we learned to train a custom image classification network for OAK-D using the TensorFlow framework. This is the last tutorial in our OAK-101 4-part series, and if you followed the series of tutorials from the beginning, we hope you have built strong foundations of the OpenCV AI Kit.

Neural Network 64

Neural Network Computer Vision Deep Learning AI 64

PyImageSearch

FEBRUARY 6, 2023

This is the 3rd lesson in our 4-part series on OAK 101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API Training a Custom Image Classification Network for OAK-D (today’s tutorial) OAK 101: Part 4 To learn how to train an image classification network for OAK-D, just keep reading.

Auto-classification 144

Auto-classification Computer Vision Neural Network Deep Learning 144

NVIDIA

NOVEMBER 15, 2023

Artificial intelligence on Windows 11 PCs marks a pivotal moment in tech history, revolutionizing experiences for gamers, creators, streamers, office workers, students and even casual PC users. TensorRT-LLM for Windows will soon be compatible with OpenAI’s popular Chat API through a new wrapper.

LLM 131

LLM Generative AI Large Language Models AI 131

TensorFlow

SEPTEMBER 11, 2023

Time series are the most commonly used representation for temporal data. However, time series are sometimes not sufficient to represent the richness of available data. Instead, multivariate time series can represent multiple signals together, while time sequences or event sets can represent non-uniformly sampled measurements.

Machine Learning 120

Machine Learning BERT ML Data Analysis 120

PyImageSearch

JANUARY 15, 2024

In the previous tutorial of this series, we discussed how adversarial learning can make models robust to adversarial examples. Ready to run the code immediately on your Windows, macOS, or Linux system? And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Then join PyImageSearch University today!

Computer Vision 64

Computer Vision Neural Network Deep Learning Artificial Intelligence 64

TheSequence

MAY 19, 2024

Context Window Edge: The Gemini 2M token context window is astonishing. Online vs. Offline RLHF Researchers from Google DeepMind published a paper outlining the series of experiments comparing online and offline RLHF alignment methods. LanceDB raised $11 million for building a multimodal AI database.

OpenAI 52

OpenAI LLM Generative AI AI Strategy 52

TheSequence

MARCH 31, 2024

Created Using Ideogram Next Week in The Sequence: Edge 383: Our new series continues with a deep dive into the core capabililties of autonomous agents. The new release boasts a 128k context window and impressive reasoning capabilities. Eliyan raised a $60 million Series B for its chiplet interconnect technology. Samba-CoE 0.2:

LLM 52

LLM Generative AI Neural Network ML 52

PyImageSearch

OCTOBER 2, 2023

Ready to run the code immediately on your Windows, macOS, or Linux system? And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Then, the forward method defines the forward pass of the module on Line 11. So, are you ready to delve into the captivating realm of VAEs with PyTorch?

Computer Vision 120

Computer Vision Deep Learning Neural Network Auto-complete 120

PyImageSearch

JANUARY 8, 2024

In the 2nd part of this tutorial series, we will start building our adversarial attacks and defenses using the TensorFlow NSL (Neural Structured Learning) framework, which makes it very easy to implement such adversarial applications. Ready to run the code immediately on your Windows, macOS, or Linux system? No installation required.

Computer Vision 64

Computer Vision Neural Network Deep Learning Artificial Intelligence 64

PyImageSearch

SEPTEMBER 18, 2023

This lesson is the last of a 2-part series on Segment Anything Model (SAM) from Meta AI : SAM from Meta AI (Part 1): Segmentation with Prompts SAM from Meta AI (Part 2): Integration with CLIP for Downstream Tasks (this tutorial) To learn how to integrate SAM with CLIP for downstream tasks, just keep reading. No installation required.

Computer Vision 67

Computer Vision Deep Learning AI AI 67

PyImageSearch

DECEMBER 19, 2022

This is the 2nd lesson in our 4-part series on OAK-101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API (today’s tutorial) OAK 101: Part 3 OAK 101: Part 4 To learn how DepthAI API works and run neural network inference on OAK-D, just keep reading. No installation required.

Neural Network 52

Neural Network Computer Vision Deep Learning Metadata 52

PyImageSearch

JANUARY 23, 2023

Applying XGBoost on a Problem Statement Applying XGBoost to Our Dataset Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 4 Over the last few blog posts of this series, we have been steadily building up toward our grand finish: deciphering the mystery behind eXtreme Gradient Boosting (XGBoost) itself.

Computer Vision 107

Computer Vision Deep Learning Neural Network Algorithm 107

PyImageSearch

JULY 3, 2023

In this blog post, we will dive deeper into the Netflix movies and series recommendation systems ( Figure 1 ). Netflix Movies and Series Recommendation Systems We will start with the overview of Netflix recommendations and then dig deeper into various aspects (e.g., member profile, interests, and mood), choose an action (e.g.,

Computer Vision 52

Computer Vision Deep Learning Algorithm Metadata 52

PyImageSearch

JUNE 5, 2023

This lesson is the last in a 3-part series on GANs 301 : CycleGAN: Unpaired Image-to-Image Translation (Part 1) CycleGAN: Unpaired Image-to-Image Translation (Part 2) CycleGAN: Unpaired Image-to-Image Translation (Part 3) (this tutorial) To learn to train and use the CycleGAN model in real-time, just keep reading. No installation required.

Computer Vision 56

Computer Vision Auto-complete Deep Learning Explainability 56

PyImageSearch

JANUARY 16, 2023

This lesson is the 3rd of a 4-part series on Deep Learning 108 : Scaling Kaggle Competitions Using XGBoost: Part 1 Scaling Kaggle Competitions Using XGBoost: Part 2 Scaling Kaggle Competitions Using XGBoost: Part 3 (this tutorial) Scaling Kaggle Competitions Using XGBoost: Part 4 To learn how to utilize Gradient Boosting, just keep reading.

Computer Vision 102

Computer Vision Deep Learning Neural Network Algorithm 102

PyImageSearch

JANUARY 29, 2024

Introduction to Advanced Adversarial Techniques in Machine Learning In the first tutorial of this series, we briefly discussed the process of generating perturbations (or noise) and tried to engineer our own adversarial examples using TensorFlow. Note that this is similar to how we normally use SGD to optimize the weights of a model.

Computer Vision 56

Computer Vision Deep Learning Neural Network Machine Learning 56

Flipboard

JULY 10, 2023

This lesson is the 1st of a 4-part series on Autoencoders: Introduction to Autoencoders (this tutorial) Implementing a Convolutional Autoencoder with PyTorch (lesson 2) Lesson 3 Lesson 4 To learn the theoretical aspects of Autoencoders, just keep reading. time series or natural language processing tasks).

Computer Vision 110

Computer Vision Deep Learning Neural Network Machine Learning 110

Viso.ai

DECEMBER 1, 2023

The team created of of the largest segmentation datasets currently available, featuring over 1 billion masks applied to 11 million images. Key features of Mistral 7B include the use of Grouped-query attention (GQA) and Sliding Window Attention (SWA), enhancing its inference speed and capability to process longer sequences more efficiently.

AI Modeling 98

AI Modeling Neural Network Computer Vision Deep Learning 98

PyImageSearch

OCTOBER 23, 2023

Our journey begins with training a VAE on this dataset, setting the stage for a series of captivating experiments. Ready to run the code immediately on your Windows, macOS, or Linux system? And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Let’s dive in! No installation required.

Computer Vision 67

Computer Vision Deep Learning Neural Network Explainability 67

PyImageSearch

SEPTEMBER 25, 2023

This lesson is the 2nd in a 3-part series on Deep Dive into Popular Recommendation Engines 102 : Amazon Product Recommendation Systems YouTube Video Recommendation Systems (this tutorial) Spotify Music Recommendation Systems To learn how YouTube recommender systems work, just keep reading. So, how do their recommendations actually work?

Computer Vision 120

Computer Vision Deep Learning Neural Network Algorithm 120

PyImageSearch

MAY 29, 2023

This lesson is the 2nd in a 3-part series on GANs 301 : CycleGAN: Unpaired Image-to-Image Translation (Part 1) CycleGAN: Unpaired Image-to-Image Translation (Part 2) (this tutorial) CycleGAN: Unpaired Image-to-Image Translation (Part 3) To learn how to implement CycleGAN for Unpaired Image-to-Image Translation, just keep reading.

Computer Vision 56

Computer Vision Deep Learning Artificial Intelligence Artificial Intelligence 56

PyImageSearch

FEBRUARY 19, 2024

This lesson is the 3rd in a 6-part series on Gemini Pro: Introduction to Gemini Pro Vision Image Processing with Gemini Pro Image Classification with Gemini Pro (this tutorial) Lesson 4 Lesson 5 Lesson 6 To learn how to create image classification code in PyTorch using Gemini Pro and compare its performance with ChatGPT-3.5, 1000 car 0.91

Computer Vision 60

Computer Vision ChatGPT Deep Learning Python 60

Flipboard

MARCH 27, 2023

This blog series aims to understand and test the capabilities of PyTorch 2.0 In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. This lesson is the 1st of a 2-part series on Accelerating Deep Learning Models with PyTorch 2.0 : What’s New in PyTorch 2.0? is planned for March 2023.

Neural Network 117

Neural Network Convolutional Neural Networks BERT Computer Vision 117

PyImageSearch

AUGUST 14, 2023

This lesson is the 1st in a 3-part series on Deep Dive into Popular Recommendation Engines 102 : Amazon Product Recommendation Systems (this tutorial) YouTube Video Recommendation Systems Spotify Music Recommendation Systems To learn how the Amazon recommender systems work, just keep reading.

Computer Vision 98

Computer Vision Deep Learning Algorithm Neural Network 98

PyImageSearch

JUNE 19, 2023

This lesson is the 1st of a 3-part series on Deep Dive into Popular Recommendation Engines 101 : Fundamentals of Recommendation Systems (this tutorial) Netflix Movies and Series Recommendation Systems LinkedIn Jobs Recommendation Systems To learn the fundamentals of recommendation systems, just keep reading. Gosthipaty, S.

Neural Network 56

Neural Network Computer Vision Data Mining Algorithm 56

PyImageSearch

SEPTEMBER 11, 2023

Like other foundational models, it is pre-trained on a large-scale dataset with 11 million images annotated with 1 billion masks. file implements the code to integrate SAM with CLIP, which we will discuss in depth in the next tutorial of this series. Ready to run the code immediately on your Windows, macOS, or Linux system?

Computer Vision 117

Computer Vision AI AI Prompt Engineer 117

Dlabs.ai

JUNE 8, 2020

Moreover, the systems cannot spot anomalies in data, nor predict a sensor reading in a specified time window. See also: 11 Ways AI Can Improve The Retail Industry __ As you can see, predictive maintenance can be applied across a variety of contexts. The Models Given the problem, we used three models against the time-series data.

Machine Learning 52

Machine Learning Auto-complete Algorithm AI 52