AWS Machine Learning Blog

JUNE 22, 2023

The final ML model combines CNN and Transformer, which are the state-of-the-art neural network architectures for modeling sequential machine log data. The model consists of the following layers: Convolutional layers (CNN) – Each CNN layer consists of two 1-dimensional convolutional operations with residual connections.

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Pickl AI

AUGUST 3, 2023

CNNs have achieved groundbreaking results in computer vision and are widely used in various real-world applications, including autonomous vehicles, medical image analysis, and facial recognition systems. Is CNN a deep-learning algorithm? This is where LSTM networks come into play. Which is an example of a Deep Learning algorithm?

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Heartbeat

MARCH 8, 2023

Monitoring A Convolutional Neural Network (CNN) in Comet — by Oluseye Jeremiah A convolutional neural network (CNN) is primarily used for image classification. Convolutional, pooling, and fully linked layers are some of the layers that make up a CNN. Day 2 kicks off shortly and you can register for live sessions here.

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Mlearning.ai

JANUARY 28, 2023

It provides support for both general recurrent neural networks (GRCNN) as well as more specialized models such as Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN).

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Explosion

JANUARY 29, 2023

We’ve developed a new end-to-end neural coref component for spaCy , improved the speed of our CNN pipelines up to 60%, and published new pre-trained pipelines for Finnish, Korean, Swedish and Croatian. Videos We’ve expanded our YouTube channel with two new playlists: spaCy Shorts and Prodigy Shorts.

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Mlearning.ai

APRIL 21, 2023

Select the answer category as short, concise, YES, or NO. Then click on import Image by author Drag your documents here. Image by author Click on add custom questions. Image by author Select a paragraph where there is an answer to your question. These things Haystack added recently]. We will export it in SQuAD. Let’s create a community!

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Google Research AI blog

APRIL 21, 2023

Formative study: Design goals for rapid ML prototyping To better understand the challenges of existing rapid prototyping ML solutions ( LIME , VAC-CNN , EnsembleMatrix ), we conducted a formative study (i.e., Visual Blocks uses a node-graph editor that facilitates rapid prototyping of ML-based multimedia applications.

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Victor Zhou

JANUARY 10, 2020

A simple CNN could be taught to recognize that our image contains a triangle that is blue. As we’ve previously mentioned, we’ll build a Convolutional Neural Network (CNN) to extract information from the image input. An Example Here’s a very simple example of how a VQA system might answer the question “what color is the triangle?”

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Heartbeat

SEPTEMBER 20, 2023

In image captioning, a pre-trained CNN is often utilized to extract image features. The CNN is typically trained on a large-scale dataset, such as ImageNet, using techniques like supervised learning. When processing an image, the pre-trained CNN takes it as input and passes it through its layers.

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Dataconomy

FEBRUARY 28, 2023

How do CNNs work? Convolutional layers:  The first layer of a CNN applies filters to the input image to extract features such as edges, corners, and other visual patterns. In this article, we have covered 13 of the most popular deep learning models that are widely used in the industry today.

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Heartbeat

AUGUST 15, 2023

Implementing CNNs in Python is relatively easy due to the availability of popular machine learning libraries such as PyTorch and TensorFlow. These libraries provide pre-built functions and modules that can be used to create CNN models for handwritten recognition.

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Flipboard

FEBRUARY 2, 2023

The player data was used to derive features for model development: X – Player position along the long axis of the field Y – Player position along the short axis of the field S – Speed in yards/second; replaced by Dis*10 to make it more accurate (Dis is the distance in the past 0.1

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Pickl AI

AUGUST 1, 2023

Common types of deep learning models include: Convolutional Neural Networks (CNN): Ideal for image and video analysis, capturing spatial patterns through convolutional layers. Long Short-Term Memory Networks (LSTM): A specialized type of RNN, effective in capturing long-term dependencies in sequential data.

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Mlearning.ai

JUNE 6, 2023

SSDs are very fast in Object Detection when compared to those big boys like R-CNN or Fast R-CNN, etc. Pedestrian Detection using HOGs In this project, we will perform pedestrian detection using HOG short for Histogram for Gradients. This is going to be a very short project. Sudoku is a 9X9 grid puzzle.

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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? Who should use deep learning visualization?

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PyImageSearch

DECEMBER 29, 2022

PBI 3 Train and deploy a CNN model to a cloud endpoint and verify results with a holdout dataset scoring above 98% accuracy. Can you spot which PBIs are groomed and which aren’t? PBI 1 Enable two factor authentication PBI 2 Improve accessibility by improving our Lighthouse accessibility score from 73 to above 85.

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Google Research AI blog

MAY 10, 2023

Using graphs to build pangenomes In the typical analysis workflow for high-throughput DNA sequencing, a sequencing instrument reads millions of short pieces of an individual’s genome, and a program called a mapper or aligner then estimates where those pieces best fit relative to the single, linear human reference sequence.

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ML Review

DECEMBER 16, 2019

you need to predict correct relations between two objects (that you obtained, say, as bounding boxes from you favorite Computer Vision CNN pipeline). Conversational AI & Graphs Let’s have a short break from hardcore ML algorithms and talk about NLP applications a bit. Short answer: Yes ? to all new shiny 92.77% ?!

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Victor Zhou

AUGUST 3, 2020

We’ll use a Long Short-Term Memory ( LSTM ) layer, which is a popular choice for this kind of problem. Some examples of modifications you could make to our CNN include: Network Depth What happens if we add Recurrent layers? . # This is when the max_tokens most common words (i.e. the vocabulary) are selected. train_texts = train_data.

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Mlearning.ai

JUNE 13, 2023

Faster R-CNN Faster R-CNN, short for “Faster Region-based Convolutional Neural Network,” is a cutting-edge object identification method based on the R-CNN (Region-based Convolutional Neural Network) family. R-CNN that is faster has various advantages over its predecessors.

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Flipboard

MARCH 6, 2023

All that said, are you: Short on time? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in a matter of minutes. Learning on your employer’s administratively locked system?

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Snorkel AI

FEBRUARY 13, 2023

So, if you want to tune to CNN, you could say: “tune to CNN”; “tune to CNN, please”; you can say, “watch CNN”; “CNN right now”; or, even very colloquially, “yo, CNN.” And then, a set of parameters, in this case, CNN, as the name of the channel you want to tune to, right?

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Snorkel AI

FEBRUARY 13, 2023

So, if you want to tune to CNN, you could say: “tune to CNN”; “tune to CNN, please”; you can say, “watch CNN”; “CNN right now”; or, even very colloquially, “yo, CNN.” And then, a set of parameters, in this case, CNN, as the name of the channel you want to tune to, right?

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AWS Machine Learning Blog

FEBRUARY 10, 2023

The image obtained from feature engineering facilitated the modeling of each play frame through a CNN. with a branching structure that is comprised of a shallow one-layer CNN and a deep three-layer CNN. with a branching structure that is comprised of a shallow one-layer CNN and a deep three-layer CNN.

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PyImageSearch

APRIL 24, 2023

All that said, are you: Short on time? Figure 3: Speedup in CNN training with TorchDynamo on NVIDIA A6000 GPUs (source: image by author). Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in minutes.

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Data Science Blog

FEBRUARY 23, 2023

And you should multiply the time spent by 80,000, as many as the training images needed for Mask R-CNN , a popular model for instance segmentation. Data augmentation Data augmentation is one of the first techniques you would learn to mitigate overfitting of machine learning, which is in short caused by lack of data.

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Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). CNNs have an uncanny ability to recognize patterns and hierarchies within images, allowing them to excel at object detection, localization, and recognition tasks.

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Heartbeat

FEBRUARY 28, 2023

Source: [ 17 ] Another recent work on cross-teaching between CNN and the transformer network was proposed in the paper Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer , published in Medical Imaging with Deep Learning (MIDL), 2022.

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Mlearning.ai

MAY 23, 2023

SSDs are very fast in Object Detection when compared to those big boys like R-CNN or Fast R-CNN, etc. This is going to be a very short blog. Pedestrian Detection using HOGs in Python In this project, we will perform pedestrian detection using HOG short for Histogram for Gradients. This is going to be a very short project.

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Mlearning.ai

MAY 21, 2023

SSDs are very fast in Object Detection when compared to those big boys like R-CNN or Fast R-CNN, etc. This is going to be a very short blog. Pedestrian Detection using HOGs in Python In this project, we will perform pedestrian detection using HOG short for Histogram for Gradients. This is going to be a very short project.

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Heartbeat

DECEMBER 20, 2023

Transitioning from ResNet, we introduce DenseNet — another influential CNN architecture. Once a CNN is trained to detect things in an image, changing the position of that thing in an image won’t prevent the CNN’s ability to detect it. The dense layers of the CNN take an input vector of the flattened pixels of the image.

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Mlearning.ai

FEBRUARY 25, 2023

While classical machine learning algorithms fell short of analyzing big data, artificial neural networks performed well on big data. In addition, the development of awesome architectures such as CNN, RNN, and Transformers has allowed us to build highly accurate models. Big data made it easy to train ANNs.

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Heartbeat

DECEMBER 11, 2023

Approach to Creating the Model The model is inspired by Implementing an image captioning model using a CNN and a Transformer and Image captioning with visual attention on TensorFlo w. Using a pre-trained CNN model to obtain image features. The CNN model will take as input the input tensor of feature maps from the EfficientNetB0 model.

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Mlearning.ai

NOVEMBER 29, 2023

Long Short-Term Memory (LSTM) It’s a kind of RNN, but while maintaining RNN’s ability to learn the temporal dynamics of sequential data, LSTM can furthermore handle the vanishing and exploding gradients problem. However, CNNs can also be joined with RNNs to get the best of both worlds — i.e.,

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Pickl AI

JANUARY 15, 2023

The use of Radial Basis Function networks is mainly for power restoration systems for the restoration of power in a short span of time. Convolution Neural Network (CNN) This is one of the variations of the multilayer perceptron It contains more than one convolution layer and it contains a layer which is deeper with fewer parameters.

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Shreyansh Singh

DECEMBER 17, 2021

Before we get into the maths, I’ll define a few terms - The baseline algorithm, was called FedSGD, short for Federated SGD. For the CNN, the B = ꝏ; E = 1 FedSGD model reaches 99.22% accuracy in 1200 rounds, while the B = 10 ;E = 20 FedAvg model reaches an accuracy of 99.44% in 300 rounds.

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Becoming Human

MAY 15, 2023

Deep neural networks can be further classified into Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). Things to be learned: Keras , PyTorch , TensorFlow , RNN , LSTM , CNN , GAN , and Transfer Learning.

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