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WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production.HOME
Weights & Biases, developer tools for machine learning REPRODUCIBILITY CHALLENGE For Deep Convolutional Neural Networks, ML Reproducibility Challenge 2020. W&B: A Reproducibility (Research) Perspective. How Weights & Biases optimised my attempt for the ML Reproducibility Challenge 2020. Reformer Reproducibility. Fast.ai community submission to the Reproducibility Challenge 2020. Rigging the Lottery. Making alltickets winners.
RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss). ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toWEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production.HOME
Weights & Biases, developer tools for machine learning REPRODUCIBILITY CHALLENGE For Deep Convolutional Neural Networks, ML Reproducibility Challenge 2020. W&B: A Reproducibility (Research) Perspective. How Weights & Biases optimised my attempt for the ML Reproducibility Challenge 2020. Reformer Reproducibility. Fast.ai community submission to the Reproducibility Challenge 2020. Rigging the Lottery. Making alltickets winners.
RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss). ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed to ENTERPRISE - WANDB.AI W&B offers scalable tools for managing growing machine learning teams, including solutions governance, data provenance, and data security.Use Weights &
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Weights & Biases, developer tools for machine learningTWO MINUTE PAPERS
Try our free tools for experiment tracking to easily visualize all your experiments in one place, compare results, and share findings. If you're doing machine learning, I FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS WITH WEIGHTS Introduction to Convolutional Neural Networks with Weights & Biases. In this tutorial we'll walk through a simple convolutional neural network to classify the images in the cifar10 dataset. You can find the accompanying code here. We highly encourage you to fork this notebook, tweak the parameters, or try the model with your owndataset!
REPRODUCIBILITY CHALLENGE At W&B, we strongly believe that research should be reproducible and accessible, so we’re excited to support people participating in the Reproducibility Challenge. We believe this initiative is very important, and we’re happy to do what we can to supportparticipants.
INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Welcome! In this tutorial we'll walk through a simple convolutional neural network to classify the images in the Simpson dataset using Keras. We’ll also set up Weights & Biases to log models metrics, inspect performance and share findings about the best architecture for the network. In this example we're using Google Colab as a convenient hosted environment, but you can run your own trainingWANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
PROJECT PAGE
Project Page. Compare versions of your model, explore results in a scratch workspace, and export findings to a report to save notes and visualizations. The project Workspace gives you a personal sandbox to compare experiments. Use projects to organize models that can be compared, working on the same problem with different architectures THE REALITY BEHIND THE OPTIMIZATION OF IMAGINARY VARIABLES Exploring the capabilities of neural networks to map the imaginary loss landscape and studying their applications in modern research.WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toWEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toTWO MINUTE PAPERS
Try our free tools for experiment tracking to easily visualize all your experiments in one place, compare results, and share findings. If you're doing machine learning, I FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS WITH WEIGHTS Introduction to Convolutional Neural Networks with Weights & Biases. In this tutorial we'll walk through a simple convolutional neural network to classify the images in the cifar10 dataset. You can find the accompanying code here. We highly encourage you to fork this notebook, tweak the parameters, or try the model with your owndataset!
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Welcome! In this tutorial we'll walk through a simple convolutional neural network to classify the images in the Simpson dataset using Keras. We’ll also set up Weights & Biases to log models metrics, inspect performance and share findings about the best architecture for the network. In this example we're using Google Colab as a convenient hosted environment, but you can run your own trainingAUTHORIZE - W&B
Weights & Biases, developer tools for machine learning RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON Typical Hyperparameters in Neural Network Architecture - Source Hyperparameter Sweeps organize search in a very elegant way, allowing us to: Set up hyperparameter searches using declarative configurations; Experiment with a variety of hyperparameter tuning methods including grid search, random search, Bayesian optimization, and Hyperband; Running Hyperparameter Sweeps using PART 1 – INTRODUCTION TO GRAPH NEURAL NETWORKS WITH GATEDGCN Graph Representation Learning is the task of effectively summarizing the structure of a graph in a low dimensional embedding. With the rise of deep learning, researchers have come up with various architectures that involve the use of neural networks for graph representation learning. We call such architectures Graph Neural Networks. PYTORCH LIGHTNING WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Pytorch Lightning with Weights & Biases. PyTorch Lightning lets you decouple science code from engineering code. Try this quick tutorial to visualize Lightning models and optimize hyperparameters with an easy Weights & Biases integration. Try Pytorch Lightning THE REALITY BEHIND THE OPTIMIZATION OF IMAGINARY VARIABLES Exploring the capabilities of neural networks to map the imaginary loss landscape and studying their applications in modern research.WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toTWO MINUTE PAPERS
Try our free tools for experiment tracking to easily visualize all your experiments in one place, compare results, and share findings. If you're doing machine learning, I FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS WITH WEIGHTS Introduction to Convolutional Neural Networks with Weights & Biases. In this tutorial we'll walk through a simple convolutional neural network to classify the images in the cifar10 dataset. You can find the accompanying code here. We highly encourage you to fork this notebook, tweak the parameters, or try the model with your owndataset!
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Welcome! In this tutorial we'll walk through a simple convolutional neural network to classify the images in the Simpson dataset using Keras. We’ll also set up Weights & Biases to log models metrics, inspect performance and share findings about the best architecture for the network. In this example we're using Google Colab as a convenient hosted environment, but you can run your own trainingAUTHORIZE - W&B
Weights & Biases, developer tools for machine learning RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON Typical Hyperparameters in Neural Network Architecture - Source Hyperparameter Sweeps organize search in a very elegant way, allowing us to: Set up hyperparameter searches using declarative configurations; Experiment with a variety of hyperparameter tuning methods including grid search, random search, Bayesian optimization, and Hyperband; Running Hyperparameter Sweeps using PART 1 – INTRODUCTION TO GRAPH NEURAL NETWORKS WITH GATEDGCN Graph Representation Learning is the task of effectively summarizing the structure of a graph in a low dimensional embedding. With the rise of deep learning, researchers have come up with various architectures that involve the use of neural networks for graph representation learning. We call such architectures Graph Neural Networks. PYTORCH LIGHTNING WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Pytorch Lightning with Weights & Biases. PyTorch Lightning lets you decouple science code from engineering code. Try this quick tutorial to visualize Lightning models and optimize hyperparameters with an easy Weights & Biases integration. Try Pytorch Lightning THE REALITY BEHIND THE OPTIMIZATION OF IMAGINARY VARIABLES Exploring the capabilities of neural networks to map the imaginary loss landscape and studying their applications in modern research.WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toWEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toTWO MINUTE PAPERS
Try our free tools for experiment tracking to easily visualize all your experiments in one place, compare results, and share findings. If you're doing machine learning, I FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS WITH WEIGHTS Introduction to Convolutional Neural Networks with Weights & Biases. In this tutorial we'll walk through a simple convolutional neural network to classify the images in the cifar10 dataset. You can find the accompanying code here. We highly encourage you to fork this notebook, tweak the parameters, or try the model with your owndataset!
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Welcome! In this tutorial we'll walk through a simple convolutional neural network to classify the images in the Simpson dataset using Keras. We’ll also set up Weights & Biases to log models metrics, inspect performance and share findings about the best architecture for the network. In this example we're using Google Colab as a convenient hosted environment, but you can run your own trainingAUTHORIZE - W&B
Weights & Biases, developer tools for machine learning RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON Typical Hyperparameters in Neural Network Architecture - Source Hyperparameter Sweeps organize search in a very elegant way, allowing us to: Set up hyperparameter searches using declarative configurations; Experiment with a variety of hyperparameter tuning methods including grid search, random search, Bayesian optimization, and Hyperband; Running Hyperparameter Sweeps using PART 1 – INTRODUCTION TO GRAPH NEURAL NETWORKS WITH GATEDGCN Graph Representation Learning is the task of effectively summarizing the structure of a graph in a low dimensional embedding. With the rise of deep learning, researchers have come up with various architectures that involve the use of neural networks for graph representation learning. We call such architectures Graph Neural Networks. PYTORCH LIGHTNING WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Pytorch Lightning with Weights & Biases. PyTorch Lightning lets you decouple science code from engineering code. Try this quick tutorial to visualize Lightning models and optimize hyperparameters with an easy Weights & Biases integration. Try Pytorch Lightning THE REALITY BEHIND THE OPTIMIZATION OF IMAGINARY VARIABLES Exploring the capabilities of neural networks to map the imaginary loss landscape and studying their applications in modern research.WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
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Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toWEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
HOME
Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes. Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production. RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss).WANDB DOCKER
Summary. W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we'll choosea
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
GET STARTED WITH TENSORFLOW LITE EXAMPLES USING ANDROID STUDIO TensorFlow Lite Examples. Now, the reason why it's so easy to get started here is that the TensorFlow Lite team actually provides us with numerous examples of working projects, including object detection, gesture recognition, pose estimation & much, much more. And trust me, that is a big deal and helps a lot with getting started.. These example projects are essentially folders with specially DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toTWO MINUTE PAPERS
Try our free tools for experiment tracking to easily visualize all your experiments in one place, compare results, and share findings. If you're doing machine learning, I FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS WITH WEIGHTS Introduction to Convolutional Neural Networks with Weights & Biases. In this tutorial we'll walk through a simple convolutional neural network to classify the images in the cifar10 dataset. You can find the accompanying code here. We highly encourage you to fork this notebook, tweak the parameters, or try the model with your owndataset!
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Welcome! In this tutorial we'll walk through a simple convolutional neural network to classify the images in the Simpson dataset using Keras. We’ll also set up Weights & Biases to log models metrics, inspect performance and share findings about the best architecture for the network. In this example we're using Google Colab as a convenient hosted environment, but you can run your own trainingAUTHORIZE - W&B
Weights & Biases, developer tools for machine learning RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON Typical Hyperparameters in Neural Network Architecture - Source Hyperparameter Sweeps organize search in a very elegant way, allowing us to: Set up hyperparameter searches using declarative configurations; Experiment with a variety of hyperparameter tuning methods including grid search, random search, Bayesian optimization, and Hyperband; Running Hyperparameter Sweeps using PART 1 – INTRODUCTION TO GRAPH NEURAL NETWORKS WITH GATEDGCN Graph Representation Learning is the task of effectively summarizing the structure of a graph in a low dimensional embedding. With the rise of deep learning, researchers have come up with various architectures that involve the use of neural networks for graph representation learning. We call such architectures Graph Neural Networks. PYTORCH LIGHTNING WITH WEIGHTS & BIASES ON WEIGHTS & BIASES Pytorch Lightning with Weights & Biases. PyTorch Lightning lets you decouple science code from engineering code. Try this quick tutorial to visualize Lightning models and optimize hyperparameters with an easy Weights & Biases integration. Try Pytorch Lightning THE REALITY BEHIND THE OPTIMIZATION OF IMAGINARY VARIABLES Exploring the capabilities of neural networks to map the imaginary loss landscape and studying their applications in modern research.WEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASESSEE MORE ONWANDB.AI
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss). KERAS - DOCUMENTATION Keyword argument. Default. Description. monitor. val_loss. The training metric used to measure performance for saving the best model. i.e. val_loss mode. auto 'min', 'max', or 'auto': How to compare the training metric specified in monitor between steps save_weights_only HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. IMAGE CLASSIFICATION USING PYTORCH LIGHTNING A practical introduction on how to use PyTorch Lightning to improve the readability and reproducibility of your PyTorch code. DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed toWEIGHTS & BIASES
Debug ML models. Focus your team on the hard machine learning problems. Let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues. try w&B. DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
INTRO TO KERAS WITH WEIGHTS & BIASES ON WEIGHTS & BIASESSEE MORE ONWANDB.AI
FULLY CONNECTED: FASTAI Our collection of posts that leverage W&B and the fastai deep learning library, all in one handy place RUNNING HYPERPARAMETER SWEEPS TO PICK THE BEST MODEL ON How to Add a Parallel Coordinates Chart. Step 1: Click ‘Add visualization’ on the project page. Step 2: Choose the parallel coordinates plot. Step 3: Pick the dimensions (hyperparameters) you would like to visualize. Ideally, you want the last column to be the metric you are optimizing for (e.g. validation loss). KERAS - DOCUMENTATION Keyword argument. Default. Description. monitor. val_loss. The training metric used to measure performance for saving the best model. i.e. val_loss mode. auto 'min', 'max', or 'auto': How to compare the training metric specified in monitor between steps save_weights_only HYPERPARAMETER TUNING Initialize sweep: Launch the sweep server. We host this central controller and coordinate between the agents that execute the sweep. Launch agent (s): Run a single-line command on each machine you'd like to use to train models in the sweep. The agents ask the central sweep server what hyperparameters to try next, and then they execute theruns.
ONE-TO-MANY, MANY-TO-ONE AND MANY-TO-MANY LSTM EXAMPLES IN One-to-Many. One-to-many sequence problems are sequence problems where the input data has one time-step, and the output contains a vector of multiple values or multiple time-steps. Thus, we have a single input and a sequence of outputs. A typical example is image captioning, where the description of an image is generated. IMAGE CLASSIFICATION USING PYTORCH LIGHTNING A practical introduction on how to use PyTorch Lightning to improve the readability and reproducibility of your PyTorch code. DIFFERENCE BETWEEN ‘SAME’ AND ‘VALID’ PADDING IN TENSORFLOW In TensorFlow, tf.nn.max_pool performs max pooling on the input. Max pooling is used to downsample the spatial dimension of input to reduce the number of parameters and computation needed to DASHBOARD - WANDB.AI A system of record for your model results. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Fast integration. Set up your code in 5 minutes. Add a few lines to your script to start logging results. Our lightweight integration works with any Pythonscript.
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Weights & Biases, developer tools for machine learning WEIGHTS & BIASES PRICING We're building developer tools for deep learning. Add a couple lines of code to your training script and we'll keep track of your hyperparameters, system metrics, and outputs so you can compare experiments, see live graphs of training, and easily share your findings with colleagues. FUNDAMENTALS OF NEURAL NETWORKS ON WEIGHTS & BIASES Around 2^n (where n is the number of neurons in the architecture) slightly-unique neural networks are generated during the training process, and ensembled together to make predictions. A good dropout rate is between 0.1 to 0.5; 0.3 for RNNs, and 0.5 for CNNs. Use larger rates for bigger layers. CONVOLUTIONAL NEURAL NETWORKS Convolutional Neural Networks. All of the work we’ve done so far applies to any data set where we can convert the input and outputs to fixed length list of numbers. But we have thrown stout some crucial information. When we out flatten that image, we lose the fact that there’s meaning in MULTI-GPU HYPERPARAMETER SWEEPS IN THREE SIMPLE STEPS ON Hyperparameter sweeps are ways to automatically test different configurations of your model. They address a wide range of needs, including running experiments with different test conditions, exploration of your dataset, or large scale tuning hyperparameters..Setting
HYPERPARAMETER TUNING FOR KERAS AND PYTORCH MODELS ON We’re excited to launch a powerful and efficient way to do hyperparameter tuning and optimization - W&B Sweeps, in both Keras and Pytoch.. With just a few lines of code Sweeps automatically search through high dimensional hyperparameter spaces to find the best REPRODUCIBILITY: DOCKER FOR MACHINE LEARNING ON WEIGHTS Docker supports reproducibility. Much has been written about the reproducibility crisis in machine learning, and the difficulty is real. Using Docker removes one major source of variability. Using Docker allows your code to continue to run painlessly in the future. When I clone a github repo of an ML experiment, I always prepare foran unknown
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# Flexible integration for any Python scriptimport wandb
# 1. Start a W&B run wandb.init(project='gpt3') # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01 # Model training here # 3. Log metrics over time to visualize performance wandb.log({"loss": loss})import wandb
# 1. Start a W&B run wandb.init(project='gpt3') # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01 # Model training here # 3. Log metrics over time to visualize performance with tf.Session() as sess:# ...
wandb.tensorflow.log(tf.summary.merge_all())import wandb
# 1. Start a new run wandb.init(project="gpt-3") # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01 # 3. Log gradients and model parameterswandb.watch(model)
for batch_idx, (data, target) in enumerate(train_loader): if batch_idx % args.log_interval == 0: # 4. Log metrics to visualize performance wandb.log({"loss": loss})import wandb
from wandb.keras import WandbCallback # 1. Start a new run wandb.init(project="gpt-3") # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01... Define a model
# 3. Log layer dimensions and metrics over time model.fit(X_train, y_train, validation_data=(X_test, y_test),callbacks=)
import wandb
wandb.init(project="visualize-sklearn") # Model training here # Log classifier visualizations wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels, model_name='SVC', feature_names=None) # Log regression visualizations wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test,model_name='Ridge')
# Log clustering visualizations wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name='KMeans') # 1. Import wandb and loginimport wandb
wandb.login()
# 2. Define which wandb project to log to and name your run wandb.init(project="gpt-3", run_name='gpt-3-base-high-lr') # 3. Add wandb in your Hugging Face `TrainingArguments` args = TrainingArguments(... , report_to='wandb') # 4. W&B logging will begin automatically when your start trainingyour Trainer
trainer = Trainer(... , args=args)trainer.train()
import wandb
# 1. Start a new run wandb.init(project="visualize-models", name="xgboost") # 2. Add the callback bst = xgboost.train(param, xg_train, num_round, watchlist,callbacks=)
# Get predictions
pred = bst.predict(xg_test)02
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Semantic Segmentation Semantic segmentation for scene parsing on Berkeley Deep Drive 100KDebugging Models
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How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFsOpenAI Jukebox
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Exploring generative models that create music based on raw audioLighting Effects
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Use RGB-space geometry to generate digital painting lighting effects Political Advertising03
Fuzzy string search (or binary matching) on entity names from receiptPDFs
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Visualize images, videos, audio, tables, HTML, metrics, plots, 3dobjects, and more
Semantic Segmentation Semantic segmentation for scene parsing on Berkeley Deep Drive 100KDebugging Models
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How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFsOpenAI Jukebox
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Exploring generative models that create music based on raw audioLighting Effects
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Use RGB-space geometry to generate digital painting lighting effects Political Advertising03
Fuzzy string search (or binary matching) on entity names from receiptPDFs
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Visualize images, videos, audio, tables, HTML, metrics, plots, 3dobjects, and more
Semantic Segmentation Semantic segmentation for scene parsing on Berkeley Deep Drive 100KDebugging Models
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How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFs
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