Mlearning.ai

JUNE 1, 2023

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. This allows me to capture features of cat and dog sounds for the neural network.

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Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

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ODSC - Open Data Science

FEBRUARY 13, 2023

How Deep Neural Networks Work and How We Put Them to Work at Facebook Deep learning is the technology driving today’s artificial intelligence boom. We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network.

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Mlearning.ai

JANUARY 28, 2023

TensorFlow: TensorFlow is an open source library for building neural networks and other deep learning algorithms on top of GPUs. PyTorch: PyTorch is a library for Torch Machine Learning Platform (MLP) development on CPU devices as well as GPUs with Tensor Cores 2nd Generation (L1/L2). What is a Library?

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Mlearning.ai

MAY 16, 2023

Deployment of PyTorch Model Using NCNN for Mobile Devices — Part 2 An introductory example of deploying a pretrained PyTorch model into an Android app using NCNN for mobile devices. Deployment of deep neural network on mobile phone. (a) to boost the usages of the deep neural networks in our lives.

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Towards AI

SEPTEMBER 1, 2023

those calculations allow dimensioning prototypes and ensuring safe processes without having to build them. Numerical simulations have been used for years to understand the behavior of physical systems; how the fluids interact with a structure, how a geometry is deformed under stress, or even the thermal distribution under heating conditions.

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Implementing CNNs in Python is relatively easy due to the availability of popular machine learning libraries such as PyTorch and TensorFlow.

Python 52

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Mlearning.ai

MARCH 26, 2023

Heart Attack Prediction Project link: [link] Prepare the data for neural network Split the dataset and create a simple neural network model using a library like TensorFlow or PyTorch. (I The advent of generative AI models like ChatGPT has made this an even more exciting time to delve into data science.

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Mlearning.ai

JULY 11, 2023

Step 3: Explore Neural Networks and Deep Learning Basics Deep learning is based on neural networks. Start by understanding the basics of neural networks, which are networks of connected nodes that imitate the human brain. Also, learn about common algorithms used in machine learning.

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Towards AI

JANUARY 31, 2023

What happened this week in AI by Louis Following recent advancements in image, code, and text generation, there have been new developments in AI-generated music and text-to-speech. In 2019, everyone was already impressed with OpenAI’s Musenet, built on GPT-2 techniques applied to MIDI files. human-written music-text pairs, is available.

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Towards AI

SEPTEMBER 28, 2023

Graph Neural Networks and Transformers for Time Series Forecasting on Heterogeneous Graphs to Predict Butte Trade Volumes. Their inherent structure allows for efficient storage of complex information, such as the ongoing protein interactions in your body or the ever-evolving social network surrounding you and your friends.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

We used a convolutional neural network (CNN) architecture with ResNet152 for image classification. They operate in multiple ecommerce channels such as quick commerce, slotted delivery, and daily subscriptions. You can also buy from their physical stores and vending machines. BigBasket serves over 10 million customers.

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DrivenData Labs

JANUARY 11, 2023

The goal of the Mars Spectrometry 2: Gas Chromatography Challenge was to build a model to automatically analyze gas chromatography–mass spectrometry (GCMS) data collected for Mars exploration. The Challenge Mass spectrometers are now, and will continue to be, a key instrument for missions searching for life and habitability on other planets.

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. Recurrent neural networks (RNNs), are a significant deep learning approach used in time series analysis . Long short-term memory networks are yet another crucial method.

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Heartbeat

MARCH 28, 2023

A complete guide to building a deep learning project with PyTorch, tracking an Experiment with Comet ML, and deploying an app with Gradio on HuggingFace Image by Freepik AI tools such as ChatGPT, DALL-E, and Midjourney are increasingly becoming a part of our daily lives. These tools were developed with deep learning techniques.

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Heartbeat

FEBRUARY 27, 2023

Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends. This article explores the latest research and developments, impact on various industries, and challenges faced in Generative Adversarial Networks and Deep Reinforcement Learning. A diagram of how a GAN works.

Machine Learning 52

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Flipboard

MARCH 27, 2023

Table of Contents What’s New in PyTorch 2.0? torch.compile Configuring Your Development Environment Installation Verification Overview of PyTorch 2.0 What’s New in PyTorch 2.0? torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 The stable release of PyTorch 2.0 just keep reading.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? Deep learning models are typically highly complex.

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DagsHub

MARCH 19, 2024

It helped build applications around image classification, object detection, face recognition and so much more! let's discuss five best practices for building scalable and future-proof computer vision projects and how to avoid some common mistakes. Therefore, in 2024, you will very much run into apps driven by computer vision.

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PyImageSearch

OCTOBER 2, 2023

Home Table of Contents A Deep Dive into Variational Autoencoder with PyTorch Introduction Comparison with Convolutional Autoencoder Architecture Latent Space Loss Function Why Does VAE Stand Out? As the tutorial progresses, you’ll delve into setting up prerequisites, crafting utilities, and designing the VAE network.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. Then, you repeat this process until you are satisfied with the results.

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DagsHub

MARCH 5, 2024

The list goes on, and there's a lot of things that can go wrong when you're building a computer vision project. The list goes on, and there's a lot of things that can go wrong when you're building a computer vision project. Managing a computer vision project is not easy. That's not all. What is a Computer Vision Project?

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PyImageSearch

NOVEMBER 6, 2023

Home Table of Contents Image Segmentation with U-Net in PyTorch: The Grand Finale of the Autoencoder Series Introduction U-Net Framework Configuring Your Development Environment Need Help Configuring Your Development Environment? Let’s embark on this grand finale together!

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Victor Zhou

MARCH 3, 2019

Here’s something that might surprise you: neural networks aren’t that complicated! The term “neural network” gets used as a buzzword a lot, but in reality they’re often much simpler than people imagine. We’ll understand how neural networks work while implementing one from scratch in Python. dot ( self.

Machine Learning 59

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PyImageSearch

OCTOBER 23, 2023

Home Table of Contents Generating Faces Using Variational Autoencoders with PyTorch Configuring Your Development Environment Need Help Configuring Your Development Environment? By the time we conclude, you’ll have a comprehensive understanding of how to implement, train, and experiment with VAEs using PyTorch.

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Pickl AI

JUNE 7, 2023

How to create a Data Science Project on GitHub? Data Science being the most demanding career fields today with millions of job opportunities flooding in the market. in order to ensure that you have a great career in Data Science, one of the major requirements is to create and have a Github Data Science project. Let’s take a look!

Data Science 52

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DrivenData Labs

APRIL 10, 2024

To help participants get started, MathWorks published a benchmark solution that walks through how to train a basic semantic segmentation model using a SegNet network. These pressures can manifest locally or globally, and with complicated spatial dynamics. Better tools are needed to monitor and preserve kelp forests on a large scale.

Deep Learning 130

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Flipboard

JULY 10, 2023

Revisiting the Story Types of Autoencoder Vanilla Autoencoder Convolutional Autoencoder (CAE) Denoising Autoencoder Sparse Autoencoder Variational Autoencoder (VAE) Sequence-to-Sequence Autoencoder What Are the Applications of Autoencoders? An autoencoder is an artificial neural network used for unsupervised learning tasks (i.e.,

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DrivenData Labs

DECEMBER 13, 2022

The goal of this benchmark is to: Demonstrate how to explore and work with the data Provide a basic framework for building a model Demonstrate how to package your work correctly for submission You can either expand on and improve this benchmark, or start with something completely different!

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Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? In the same way, artificial neural networks (ANNs) were developed inspired by neurons in the brain. ANN approach is a machine learning algorithm inspired by biological neural networks. Neural networks were trained faster with GPUs.

Machine Learning 52

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PyImageSearch

JULY 17, 2023

Home Table of Contents Implementing a Convolutional Autoencoder with PyTorch Configuring Your Development Environment Need Help Configuring Your Development Environment? Moreover, you will gain valuable insights into the capabilities and limitations of convolutional autoencoders. Let’s rewind a bit.

Deep Learning 98

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Victor Zhou

MAY 29, 2019

In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN , including deriving gradients, implementing backprop from scratch (using only numpy ), and ultimately building a full training pipeline! This post assumes a basic knowledge of CNNs.

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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3.

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Heartbeat

SEPTEMBER 25, 2023

Convolutional Neural Networks (CNNs), a machine learning algorithm, has successfully identified and classified abnormalities, lesions, tumors, and other critical features in medical images such as X-rays, MRIs, CT scans, and mammograms. This is where machine learning comes in.

Machine Learning 52

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Becoming Human

MAY 15, 2023

Prerequisite to follow this roadmap of data science There are such prerequisites to follow this roadmap, your dedication of 2–3 hours per day till 6–8 months, where your first 4–6 months cover all your learning part and the last 2–3 months cover your end-to-end projects, resume building, networking, and applying for a job.

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The MLOps Blog

APRIL 20, 2023

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Hopefully, at the end of this blog, you will know a bit more about finding your way around computer vision projects.

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Mlearning.ai

JUNE 16, 2023

The current practice of building AI applications in the Medical Imaging space often sticks to a suboptimal approach. Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. The most common transfer learning recipe is suboptimal. There’s a better approach.

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