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PAPERSPACE
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COMPLETE GUIDE TO THE DATALOADER CLASS IN PYTORCH Dataset: The first parameter in the DataLoader class is the dataset. This is where we load the data from. 2. Batching the data: batch_size refers to the number of training samples used in one iteration. Usually we split our data into training and testing sets, and we may have different batch sizes for each. 3. MEAN AVERAGE PRECISION (MAP) EXPLAINED By Ahmed Fawzy Gad. To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at the confusion matrix ATTENTION MECHANISMS WITH KERAS GRADIENT JUPYTER NOTEBOOKS Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Notebooks are fully-managed and do not require any setup or management of servers or dependencies.ARTIFACTS - AI WIKI
Artifacts is common ML term used to describe the output created by the training process. The output could be a fully trained model, a model checkpoint (for resuming training later), or simply a file created during the training process such as an image generated while training a Generative Adversarial Network (GAN).. In the case of a Deep Learning model, the model artifacts are the trained FEDERATED LEARNING WITH KERAS To keep the data private but still use it to train machine learning models, privacy-preserving machine learning has been on the rise. This tutorial discusses how to use federated learning to train Keras models while keeping user data private. The code for this tutorial is available at the KerasFederated directory of this GitHub project,which
TRAINING MASK R-CNN WITH TENSORFLOW 2.0 AND KERAS The Mask_RCNN project works only with TensorFlow ≥ ≥ 1.13. Because TensorFlow 2.0 offers more features and enhancements, developers are looking to migrate to TensorFlow 2.0. Some tools may help in automatically convert TensorFlow 1.0 code to TensorFlow 2.0 but they are not guaranteed to produce a fully functional code. PAPERSPACESIGN UPGAMINGAPPPRICINGBUSINESSVFX The Paperspace Stack. Our tools provide a seamless abstraction layer that radically simplifies access to the emerging class of accelerated computing. The Paperspace stack removes costly distractions, enabling individuals and enterprises to focus on what matters. Each product addresses specific use-cases and challenges of accelerated computing.PAPERSPACE
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COMPLETE GUIDE TO THE DATALOADER CLASS IN PYTORCH Dataset: The first parameter in the DataLoader class is the dataset. This is where we load the data from. 2. Batching the data: batch_size refers to the number of training samples used in one iteration. Usually we split our data into training and testing sets, and we may have different batch sizes for each. 3. MEAN AVERAGE PRECISION (MAP) EXPLAINED By Ahmed Fawzy Gad. To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at the confusion matrix ATTENTION MECHANISMS WITH KERAS GRADIENT JUPYTER NOTEBOOKS Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Notebooks are fully-managed and do not require any setup or management of servers or dependencies.ARTIFACTS - AI WIKI
Artifacts is common ML term used to describe the output created by the training process. The output could be a fully trained model, a model checkpoint (for resuming training later), or simply a file created during the training process such as an image generated while training a Generative Adversarial Network (GAN).. In the case of a Deep Learning model, the model artifacts are the trained FEDERATED LEARNING WITH KERAS To keep the data private but still use it to train machine learning models, privacy-preserving machine learning has been on the rise. This tutorial discusses how to use federated learning to train Keras models while keeping user data private. The code for this tutorial is available at the KerasFederated directory of this GitHub project,which
TRAINING MASK R-CNN WITH TENSORFLOW 2.0 AND KERAS The Mask_RCNN project works only with TensorFlow ≥ ≥ 1.13. Because TensorFlow 2.0 offers more features and enhancements, developers are looking to migrate to TensorFlow 2.0. Some tools may help in automatically convert TensorFlow 1.0 code to TensorFlow 2.0 but they are not guaranteed to produce a fully functional code.PAPERSPACE
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Download the Paperspace desktop app for Mac, Windows, or Linux. Enjoy increased performance and a seamless user experience. ATTENTION MECHANISMS WITH KERAS This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, their applications, and how to bring the models to life using Keras. IMPLEMENTING SEQ2SEQ MODELS FOR TEXT SUMMARIZATION WITH KERAS Introduction to Seq2Seq Models. Seq2Seq Architecture and Applications. Text Summarization Using an Encoder-Decoder Sequence-to-Sequence Model. Step 1 - Importing the Dataset. Step 2 - Cleaning the Data. Step 3 - Determining the Maximum Permissible Sequence Lengths. Step 4 - Selecting Plausible Texts and Summaries. Step 5 - Tokenizing theText.
BEGINNER'S GUIDE TO QUANTUM MACHINE LEARNING 1) Quantum Machine Learning to Solve Linear Algebraic Problems. A wide variety of Data Analysis and Machine Learning problems are solved by performing matrix operation on vectors in a high dimensional vector space. In quantum computing, the quantum state of the qubits is a vector in a 2ª-dimensional complex vector space. MAXIMUM LIKELIHOOD ESTIMATION FOR Maximum Likelihood Estimation (MLE) MLE is a way of estimating the parameters of known distributions. Note that there are other ways to do the estimation as well, like the Bayesian estimation. To start, there are two assumptions to consider: The first assumption is that there is a training sample. X = x t N t = 1. GETTING STARTED WITH OPENAI GYM By Ayoosh Kathuria. If you're looking to get started with Reinforcement Learning, the OpenAI gym is undeniably the most popular choice for implementing environments to train your agents. A wide range of environments that are used as benchmarks for proving the efficacy of any new research methodology are implemented in OpenAI Gym, out-of-the-box. HOW TO TRAIN A QUESTION-ANSWERING MACHINE LEARNING MODEL By Rohit Kumar Singh. Question-Answering Models are machine or deep learning models that can answer questions given some context, and sometimes without any context (e.g. open-domain QA). They can extract answer phrases from paragraphs, paraphrase the answer generatively, or choose one option out of a list of given options, and so on. OBJECT DETECTION USING MASK R-CNN WITH TENSORFLOW Overview of the Mask_RCNN Project. The Mask_RCNN project is open-source and available on GitHub under the MIT license, which allows anyone to use, modify, or distribute the code for free.. The contribution of this project is the support of the Mask R-CNN object detection model in TensorFlow $\geq$ 1.0 by building all the layers in the Mask R-CNN model, and offering a simple API to train and HOW TO IMPROVE YOLOV3 How to Improve YOLOv3. YOLO has been a very popular and fast object detection algorithm, but unfortunately not the best-performing. In this article I will highlight simple training heuristics and small architectural changes that can make YOLOv3 perform better than PAPERSPACESIGN UPGAMINGAPPPRICINGBUSINESSVFX The Paperspace Stack. Our tools provide a seamless abstraction layer that radically simplifies access to the emerging class of accelerated computing. The Paperspace stack removes costly distractions, enabling individuals and enterprises to focus on what matters. Each product addresses specific use-cases and challenges of accelerated computing. APPSIGN UP FREEHELP CENTER HOME Download the Paperspace desktop app for Mac, Windows, or Linux. Enjoy increased performance and a seamless user experience.PAPERSPACE CONSOLE
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MEAN AVERAGE PRECISION (MAP) EXPLAINED By Ahmed Fawzy Gad. To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at the confusion matrix CORE MONTHLY SUBSCRIPTION Creating a Monthly Subscription VM. For Monthly Subscriptions, your account is debited the full flat-rate monthly fee when the VM is created.If you have account credit available (from a promo code or customer service), the fee will deduct first from this.If you do not have credit, or it is not enough to cover the full cost, your paymentsource is charged.
GRADIENT JUPYTER NOTEBOOKS Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Notebooks are fully-managed and do not require any setup or management of servers or dependencies. A GUIDE TO TENSORFLOW CALLBACKS A Guide to TensorFlow Callbacks. TensorFlow callbacks are an essential part of training deep learning models, providing a high degree of control over many aspects of your model training. If you are building deep learning models, you may need to sit for hours (or even days) before you can see any real results. You may need to stop modeltraining
GRADIENT - EFFORTLESS DEEP LEARNING AT SCALE Gradient supports practically all frameworks and libraries. Gradient makes it easier to work with your favorite frameworks, libraries, and tools. One platform, from start to finish. Train, tune, evaluate and deploy models 10x faster. Run, track and visualize your work across notebooks, experiments, models, and deployments (inference). INTRO TO OPTIMIZATION IN DEEP LEARNING: MOMENTUM, RMSPROPSEE MORE ONBLOG.PAPERSPACE.COM
IMPLEMENTING GRADIENT BOOSTING REGRESSION IN PYTHON Implementing Gradient Boosting in Python. In this article we'll start with an introduction to gradient boosting for regression problems, what makes it so advantageous, and its different parameters. Then we'll implement the GBR model in Python, use it for prediction, and evaluate it. a year ago • 8 min read. PAPERSPACESIGN UPGAMINGAPPPRICINGBUSINESSVFX The Paperspace Stack. Our tools provide a seamless abstraction layer that radically simplifies access to the emerging class of accelerated computing. The Paperspace stack removes costly distractions, enabling individuals and enterprises to focus on what matters. Each product addresses specific use-cases and challenges of accelerated computing. APPSIGN UP FREEHELP CENTER HOME Download the Paperspace desktop app for Mac, Windows, or Linux. Enjoy increased performance and a seamless user experience.PAPERSPACE CONSOLE
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MEAN AVERAGE PRECISION (MAP) EXPLAINED By Ahmed Fawzy Gad. To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections. In my last article we looked in detail at the confusion matrix CORE MONTHLY SUBSCRIPTION Creating a Monthly Subscription VM. For Monthly Subscriptions, your account is debited the full flat-rate monthly fee when the VM is created.If you have account credit available (from a promo code or customer service), the fee will deduct first from this.If you do not have credit, or it is not enough to cover the full cost, your paymentsource is charged.
GRADIENT JUPYTER NOTEBOOKS Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Notebooks are fully-managed and do not require any setup or management of servers or dependencies. A GUIDE TO TENSORFLOW CALLBACKS A Guide to TensorFlow Callbacks. TensorFlow callbacks are an essential part of training deep learning models, providing a high degree of control over many aspects of your model training. If you are building deep learning models, you may need to sit for hours (or even days) before you can see any real results. You may need to stop modeltraining
GRADIENT - EFFORTLESS DEEP LEARNING AT SCALE Gradient supports practically all frameworks and libraries. Gradient makes it easier to work with your favorite frameworks, libraries, and tools. One platform, from start to finish. Train, tune, evaluate and deploy models 10x faster. Run, track and visualize your work across notebooks, experiments, models, and deployments (inference). INTRO TO OPTIMIZATION IN DEEP LEARNING: MOMENTUM, RMSPROPSEE MORE ONBLOG.PAPERSPACE.COM
IMPLEMENTING GRADIENT BOOSTING REGRESSION IN PYTHON Implementing Gradient Boosting in Python. In this article we'll start with an introduction to gradient boosting for regression problems, what makes it so advantageous, and its different parameters. Then we'll implement the GBR model in Python, use it for prediction, and evaluate it. a year ago • 8 min read.APP
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This article covers a deeper level understanding of Question Answering models in NLP, the datasets commonly used, and how to choose a pre-trained model by considering various factors like the document structure, runtime cost, etc. By Abhijith Neil Abraham. NEW ML SHOWCASE ENTRY: GETTING STARTED WITH AITEXTGEN Today we're introducing a new ML Showcase project for aitextgen, a python library for training and generating text using GPT-2 and GPT-3/GPT Neo.. In this ML Showcase entry, we'll be training a model to generate text based on a sample of input Shakespeare text that we feed it. This ML Showcase entry is a collection of notebooks forked from the original repo and adapted to work in PaperspacePAPERSPACE CONSOLE
Cloud Machine Learning AI and effortless GPU infrastructure THE SWISH ACTIVATION FUNCTION The Swish Activation Function. This blogpost is an in-depth discussion of the Google Brain paper titled "Searching for activation functions" which has since revived research into activation functions. CORE MONTHLY SUBSCRIPTION Creating a Monthly Subscription VM. For Monthly Subscriptions, your account is debited the full flat-rate monthly fee when the VM is created.If you have account credit available (from a promo code or customer service), the fee will deduct first from this.If you do not have credit, or it is not enough to cover the full cost, your paymentsource is charged.
FILE UPLOADS ARRIVE IN GRADIENT NOTEBOOKS We're excited to release a new file uploader for the Gradient Notebooks IDE. Ever since we released the all-new Gradient Notebooks IDE in February we've been focused on adding features and functionality to improve file management and resource management user experiences within notebooks.. In our last release we added a number of quality of life improvements around files and folders, GPU A GUIDE TO PAPERSPACE'S GRADIENT COMMUNITY NOTEBOOKS Gradient Community Notebooks allow users to create, run, and share Jupyter notebooks on free GPUs. In this post Gradient Community Notebooks will be introduced and the steps to get started will be closely discussed, so you can easily create a free Jupyter notebook on a GPU or CPU and share it with the public. HOW TO IMPROVE YOLOV3 How to Improve YOLOv3. YOLO has been a very popular and fast object detection algorithm, but unfortunately not the best-performing. In this article I will highlight simple training heuristics and small architectural changes that can make YOLOv3 perform better than NewIntroducing FREE GPU and CPU-backed Jupyter notebooks!Signup for a free account to get started!Products
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