This section will cover energy-based models like the Boltzmann Machine and Restricted Boltzmann Machine, along with Contrastive Divergence training and how these concepts are used in Deep Belief Networks and Deep Boltzmann Machines.
The document discusses different types of neural networks used for various machine learning applications including regression, classification, computer vision, time series analysis, feature detection, and recommendation systems. Specifically, it mentions that artificial neural networks are used for regression and classification, convolutional neural networks for computer vision, recurrent neural networks for time series analysis, self-organizing maps for feature detection, deep Boltzmann machines and autoencoders for recommendation systems, and whether the neural networks are supervised or unsupervised.
Deep Learning A-Z™: Regression & Classification - Module 7Kirill Eremenko
The document discusses different types of regression models:
- Simple linear regression predicts a dependent variable from a single independent variable.
- Multiple linear regression predicts a dependent variable from multiple independent variables.
- Logistic regression predicts a binary dependent variable (e.g. yes/no) from independent variables by modeling the probability.
Deep Learning A-Z™: Autoencoders - Sparse AutoencodersKirill Eremenko
This document provides links to 3 articles about sparse autoencoders, a type of neural network that learns efficient data encodings by setting most of the activations in the hidden layer to 0. The first article from 2014 provides a tutorial on sparse autoencoders. The second article also from 2014 discusses deep learning with sparse autoencoders. The third article is from the same year and presents k-sparse autoencoders, which learn representations where exactly k hidden units are active.
Deep Learning A-Z™: Regression & Classification - Simple Linear Regression - ...Kirill Eremenko
Simple linear regression is a method to model the relationship between a dependent variable (y) and an independent variable (x) using a best fit straight line. The model finds the best fit straight line that minimizes the sum of the squared residuals between the actual y values (yi) and the predicted y values (yî). This allows predicting a dependent variable (salary) based on an independent variable (experience) with the goal of minimizing the difference between predicted (yî) and actual values (yi).
Deep Learning A-Z™: Regression & Classification - Simple Linear Regression - ...Kirill Eremenko
Simple linear regression is a technique for modeling the relationship between a dependent variable (y) and a single independent variable (x) using an equation of y = b0 + b1*x. The dependent variable is represented on the y-axis and the independent variable on the x-axis, with the goal of finding the best fit linear relationship between the two.
Deep Learning A-Z™: Autoencoders - Contractive AutoencodersKirill Eremenko
This paper proposes contractive auto-encoders as a method for feature extraction that explicitly enforces invariance to small perturbations of the input. Contractive auto-encoders train the model parameters to produce representations that are robust to noise by adding a regularization term to the standard auto-encoder objective function that penalizes the squared Frobenius norm of the Jacobian of the hidden unit activations with respect to the input. This forces nearby datapoints to be mapped to similar representations, improving generalization to new examples.
Deep Learning A-Z™: Regression & Classification - Logistic RegressionKirill Eremenko
The document discusses different machine learning models including linear regression, multiple linear regression, and logistic regression. Linear regression is used to model relationships between variables to predict a continuous dependent variable. Multiple linear regression extends this to include multiple independent variables. Logistic regression can be used for classification problems to predict a binary dependent variable. It calculates the probability of an event occurring versus not occurring based on independent variable values.
Deep Learning A-Z™: Autoencoders - Stacked AutoencodersKirill Eremenko
The document discusses stacked denoising autoencoders and how they can be used to learn useful representations in a deep network by training each layer to reconstruct its input while adding noise corruption. It provides the title of a 2010 paper by Pascal Vincent et al. that presents stacked denoising autoencoders and a link to access the full paper, as well as a note about additional recommended reading on the topic.
The document discusses different types of neural networks used for various machine learning applications including regression, classification, computer vision, time series analysis, feature detection, and recommendation systems. Specifically, it mentions that artificial neural networks are used for regression and classification, convolutional neural networks for computer vision, recurrent neural networks for time series analysis, self-organizing maps for feature detection, deep Boltzmann machines and autoencoders for recommendation systems, and whether the neural networks are supervised or unsupervised.
Deep Learning A-Z™: Regression & Classification - Module 7Kirill Eremenko
The document discusses different types of regression models:
- Simple linear regression predicts a dependent variable from a single independent variable.
- Multiple linear regression predicts a dependent variable from multiple independent variables.
- Logistic regression predicts a binary dependent variable (e.g. yes/no) from independent variables by modeling the probability.
Deep Learning A-Z™: Autoencoders - Sparse AutoencodersKirill Eremenko
This document provides links to 3 articles about sparse autoencoders, a type of neural network that learns efficient data encodings by setting most of the activations in the hidden layer to 0. The first article from 2014 provides a tutorial on sparse autoencoders. The second article also from 2014 discusses deep learning with sparse autoencoders. The third article is from the same year and presents k-sparse autoencoders, which learn representations where exactly k hidden units are active.
Deep Learning A-Z™: Regression & Classification - Simple Linear Regression - ...Kirill Eremenko
Simple linear regression is a method to model the relationship between a dependent variable (y) and an independent variable (x) using a best fit straight line. The model finds the best fit straight line that minimizes the sum of the squared residuals between the actual y values (yi) and the predicted y values (yî). This allows predicting a dependent variable (salary) based on an independent variable (experience) with the goal of minimizing the difference between predicted (yî) and actual values (yi).
Deep Learning A-Z™: Regression & Classification - Simple Linear Regression - ...Kirill Eremenko
Simple linear regression is a technique for modeling the relationship between a dependent variable (y) and a single independent variable (x) using an equation of y = b0 + b1*x. The dependent variable is represented on the y-axis and the independent variable on the x-axis, with the goal of finding the best fit linear relationship between the two.
Deep Learning A-Z™: Autoencoders - Contractive AutoencodersKirill Eremenko
This paper proposes contractive auto-encoders as a method for feature extraction that explicitly enforces invariance to small perturbations of the input. Contractive auto-encoders train the model parameters to produce representations that are robust to noise by adding a regularization term to the standard auto-encoder objective function that penalizes the squared Frobenius norm of the Jacobian of the hidden unit activations with respect to the input. This forces nearby datapoints to be mapped to similar representations, improving generalization to new examples.
Deep Learning A-Z™: Regression & Classification - Logistic RegressionKirill Eremenko
The document discusses different machine learning models including linear regression, multiple linear regression, and logistic regression. Linear regression is used to model relationships between variables to predict a continuous dependent variable. Multiple linear regression extends this to include multiple independent variables. Logistic regression can be used for classification problems to predict a binary dependent variable. It calculates the probability of an event occurring versus not occurring based on independent variable values.
Deep Learning A-Z™: Autoencoders - Stacked AutoencodersKirill Eremenko
The document discusses stacked denoising autoencoders and how they can be used to learn useful representations in a deep network by training each layer to reconstruct its input while adding noise corruption. It provides the title of a 2010 paper by Pascal Vincent et al. that presents stacked denoising autoencoders and a link to access the full paper, as well as a note about additional recommended reading on the topic.
Deep Learning A-Z™: Autoencoders - Denoising AutoencodersKirill Eremenko
This document discusses using denoising autoencoders to extract and compose robust features from data. Denoising autoencoders are a type of neural network that learn to reconstruct a clean input from a corrupted version. They can be stacked to learn increasingly complex features and representations of the original data in an unsupervised manner. The document provides a link to a 2008 paper that introduces denoising autoencoders and their use for feature extraction.
Deep Learning A-Z™: Boltzmann Machines - Restricted Boltzmann MachineKirill Eremenko
The document discusses a neural network with visible and hidden nodes. It includes information about movies, genres, directors, actors and awards. The neural network is used to classify 6 movies into genres based on features of the movies like directors, actors, and whether they won specific awards.
Deep Learning A-Z™: Regression - Multiple Linear Regression IntuitionKirill Eremenko
Simple linear regression uses one independent variable to predict a dependent variable and finds the linear relationship between them as y = b0 + b1*x1, where b0 is the y-intercept and b1 is the slope. Multiple linear regression expands on this by using multiple independent variables to predict the dependent variable according to the equation y = b0 + b1*x1 + b2*x2 + ... + bn*xn, where there are coefficients for each independent variable that contribute to predicting y. Both types of regression analyze the linear relationships between variables.
Deep Learning A-Z™: Boltzmann Machines - Deep Belief NetworksKirill Eremenko
This document discusses two papers on deep learning techniques: a 2006 paper by Yoshua Bengio et al. that proposed greedy layer-wise training of deep networks, and a 1995 paper by Geoffrey Hinton et al. that introduced the wake-sleep algorithm for unsupervised neural networks training. Links and references for additional reading on both papers are provided.
Deep Learning A-Z™: Boltzmann Machines - Contrastive DivergenceKirill Eremenko
This document discusses a 2006 paper that introduced a fast learning algorithm for deep belief nets. It allowed these types of networks to be trained efficiently for the first time. The paper presented contrastive divergence, an approximation algorithm that makes it possible to train deep belief networks layer-by-layer in a greedy, unsupervised manner. Additional reading on contrastive divergence and notes are also referenced.
Deep Learning A-Z™: Boltzmann Machines - Energy Based Models (ebm)Kirill Eremenko
This document contains information about two separate topics. The first topic discusses a tutorial on energy-based learning by Yann LeCun et al from 2006 including a link to the paper. The second topic provides information about the 2009 film Mr. Nobody directed by Jaco Van Dormael, including a link to its Internet Movie Database page. No additional details are given about the content of the paper or film.
Deep Learning A-Z™: AutoEncoders - Training an AutoEncoderKirill Eremenko
The document describes the steps to build an autoencoder using deep learning. It starts with a matrix of user ratings for movies as input data. It then walks through 8 steps: 1) preparing the input data, 2) feeding an individual user's ratings into the network as input, 3) encoding the input into a lower dimensional representation, 4) decoding the encoded representation back into ratings, 5) calculating the reconstruction error, 6) backpropagating the error to update weights, 7) repeating for each user or batch of users, and 8) repeating the process over multiple epochs until completion.
The document discusses autoencoders, an unsupervised machine learning technique. It provides an overview of different types of autoencoders including sparse, denoising, contractive, stacked and deep autoencoders. Autoencoders learn an efficient compressed representation of input data in an unsupervised manner by training the network to ignore noise or corruptions in the input data. They are commonly used for dimensionality reduction, feature learning and compression.
Deep Learning A-Z™: Boltzmann Machines - Deep Boltzmann MachinesKirill Eremenko
This document discusses Deep Boltzmann Machines, an approach to deep learning using probabilistic models of pairwise connections between observed and hidden variables. Deep Boltzmann Machines can learn complex distributions by forming a deep network of hidden layers between observed input and output. They allow for efficient approximate inference using Markov chain Monte Carlo methods and can be trained in an unsupervised manner.
Deep Learning A-Z™: Boltzmann Machines - Boltzmann MachineKirill Eremenko
The document discusses different types of neural networks used for various machine learning applications including regression, classification, computer vision, time series analysis, feature detection, and recommendation systems. Specifically, it mentions that artificial neural networks are used for regression and classification, convolutional neural networks for computer vision, recurrent neural networks for time series analysis, self-organizing maps for feature detection, deep Boltzmann machines and autoencoders for recommendation systems, and whether the neural networks are supervised or unsupervised.
Deep Learning A-Z™: AutoEncoders - AutoEncodersKirill Eremenko
This document discusses different types of neural networks and their uses. It mentions that artificial neural networks are used for regression and classification problems, convolutional neural networks are used for computer vision tasks, recurrent neural networks are used for time series analysis, and self-organizing maps and deep Boltzmann machines can be used for recommendation systems and feature detection. Autoencoders are also discussed as being used for recommendation systems and involving an encoding and decoding process with visible input and output nodes.
Deep Learning A-Z™: Boltzmann Machines - Module 5Kirill Eremenko
This document discusses unsupervised and supervised deep learning models including Boltzmann machines, restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and autoencoders. It provides an overview of what will be covered, such as energy-based models and contrastive divergence training, and examples of applications for different neural network types. References and readings are also included for additional information.
Deep Learning A-Z™: Self Organizing Maps (SOM) - How do SOMs learn (part 2)Kirill Eremenko
The document discusses Kohonen's Self Organizing Feature Maps, an unsupervised machine learning algorithm. Self Organizing Maps use competitive learning to map higher-dimensional input onto a lower-dimensional grid of nodes. They retain the topological properties of the input space and are useful for visualizing and clustering multidimensional data.
Deep Learning A-Z™: Self Organizing Maps (SOM) - Reading an Advanced SOMKirill Eremenko
This document provides image sources and attribution for images appearing in a deep learning course. It also includes a link and reference for additional reading on self-organizing maps and creating hexagonal heatmaps using D3.js. The sources include Wikipedia, various websites, and the deep learning course itself. A link is given to a blog post about using D3.js to create hexagonal heatmaps for self-organizing maps.
The document outlines the k-means clustering algorithm. It describes the steps of choosing the number of clusters k, randomly selecting k points as initial centroids, assigning all points to the closest centroid, recomputing the centroids, and reassigning points in an iterative process until cluster assignments stop changing. It notes that a bad initial random selection of centroids could negatively impact the resulting clusters.
The document describes the K-Means clustering algorithm. It involves choosing the number of clusters K, selecting initial centroid points, assigning data points to the closest centroid, recomputing the centroids, and reassigning points in an iterative process until centroids are stable. The algorithm partitions a dataset into K number of groups based on feature similarity between data points so that the resulting internal homogeneity is high.
The document introduces convolutional neural networks and how they process images through feature detectors that identify patterns in the input image and create a feature map. The feature detectors slide over the image, extracting features at each location and building up the feature map one value at a time.
This document provides an overview of recurrent neural networks and long short-term memory (LSTM) networks. It discusses the vanishing gradient problem with traditional RNNs and how LSTMs address this issue using gating mechanisms. Several LSTM variations and practical intuition on LSTMs are also covered.
This document provides an overview of self-organizing maps (SOM). It discusses what will be covered, including how SOM works, K-means clustering, and how SOM learns. It also includes examples of SOM and references for additional reading on the topic.
This document provides an overview of recurrent neural networks and references two related papers. It introduces the concept of RNN effectiveness and provides links to blog posts by Andrej Karpathy on the unreasonable effectiveness of RNNs and visualizing and understanding RNNs, along with their citations. Additional readings on the topic are also referenced.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Deep Learning A-Z™: Autoencoders - Denoising AutoencodersKirill Eremenko
This document discusses using denoising autoencoders to extract and compose robust features from data. Denoising autoencoders are a type of neural network that learn to reconstruct a clean input from a corrupted version. They can be stacked to learn increasingly complex features and representations of the original data in an unsupervised manner. The document provides a link to a 2008 paper that introduces denoising autoencoders and their use for feature extraction.
Deep Learning A-Z™: Boltzmann Machines - Restricted Boltzmann MachineKirill Eremenko
The document discusses a neural network with visible and hidden nodes. It includes information about movies, genres, directors, actors and awards. The neural network is used to classify 6 movies into genres based on features of the movies like directors, actors, and whether they won specific awards.
Deep Learning A-Z™: Regression - Multiple Linear Regression IntuitionKirill Eremenko
Simple linear regression uses one independent variable to predict a dependent variable and finds the linear relationship between them as y = b0 + b1*x1, where b0 is the y-intercept and b1 is the slope. Multiple linear regression expands on this by using multiple independent variables to predict the dependent variable according to the equation y = b0 + b1*x1 + b2*x2 + ... + bn*xn, where there are coefficients for each independent variable that contribute to predicting y. Both types of regression analyze the linear relationships between variables.
Deep Learning A-Z™: Boltzmann Machines - Deep Belief NetworksKirill Eremenko
This document discusses two papers on deep learning techniques: a 2006 paper by Yoshua Bengio et al. that proposed greedy layer-wise training of deep networks, and a 1995 paper by Geoffrey Hinton et al. that introduced the wake-sleep algorithm for unsupervised neural networks training. Links and references for additional reading on both papers are provided.
Deep Learning A-Z™: Boltzmann Machines - Contrastive DivergenceKirill Eremenko
This document discusses a 2006 paper that introduced a fast learning algorithm for deep belief nets. It allowed these types of networks to be trained efficiently for the first time. The paper presented contrastive divergence, an approximation algorithm that makes it possible to train deep belief networks layer-by-layer in a greedy, unsupervised manner. Additional reading on contrastive divergence and notes are also referenced.
Deep Learning A-Z™: Boltzmann Machines - Energy Based Models (ebm)Kirill Eremenko
This document contains information about two separate topics. The first topic discusses a tutorial on energy-based learning by Yann LeCun et al from 2006 including a link to the paper. The second topic provides information about the 2009 film Mr. Nobody directed by Jaco Van Dormael, including a link to its Internet Movie Database page. No additional details are given about the content of the paper or film.
Deep Learning A-Z™: AutoEncoders - Training an AutoEncoderKirill Eremenko
The document describes the steps to build an autoencoder using deep learning. It starts with a matrix of user ratings for movies as input data. It then walks through 8 steps: 1) preparing the input data, 2) feeding an individual user's ratings into the network as input, 3) encoding the input into a lower dimensional representation, 4) decoding the encoded representation back into ratings, 5) calculating the reconstruction error, 6) backpropagating the error to update weights, 7) repeating for each user or batch of users, and 8) repeating the process over multiple epochs until completion.
The document discusses autoencoders, an unsupervised machine learning technique. It provides an overview of different types of autoencoders including sparse, denoising, contractive, stacked and deep autoencoders. Autoencoders learn an efficient compressed representation of input data in an unsupervised manner by training the network to ignore noise or corruptions in the input data. They are commonly used for dimensionality reduction, feature learning and compression.
Deep Learning A-Z™: Boltzmann Machines - Deep Boltzmann MachinesKirill Eremenko
This document discusses Deep Boltzmann Machines, an approach to deep learning using probabilistic models of pairwise connections between observed and hidden variables. Deep Boltzmann Machines can learn complex distributions by forming a deep network of hidden layers between observed input and output. They allow for efficient approximate inference using Markov chain Monte Carlo methods and can be trained in an unsupervised manner.
Deep Learning A-Z™: Boltzmann Machines - Boltzmann MachineKirill Eremenko
The document discusses different types of neural networks used for various machine learning applications including regression, classification, computer vision, time series analysis, feature detection, and recommendation systems. Specifically, it mentions that artificial neural networks are used for regression and classification, convolutional neural networks for computer vision, recurrent neural networks for time series analysis, self-organizing maps for feature detection, deep Boltzmann machines and autoencoders for recommendation systems, and whether the neural networks are supervised or unsupervised.
Deep Learning A-Z™: AutoEncoders - AutoEncodersKirill Eremenko
This document discusses different types of neural networks and their uses. It mentions that artificial neural networks are used for regression and classification problems, convolutional neural networks are used for computer vision tasks, recurrent neural networks are used for time series analysis, and self-organizing maps and deep Boltzmann machines can be used for recommendation systems and feature detection. Autoencoders are also discussed as being used for recommendation systems and involving an encoding and decoding process with visible input and output nodes.
Deep Learning A-Z™: Boltzmann Machines - Module 5Kirill Eremenko
This document discusses unsupervised and supervised deep learning models including Boltzmann machines, restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and autoencoders. It provides an overview of what will be covered, such as energy-based models and contrastive divergence training, and examples of applications for different neural network types. References and readings are also included for additional information.
Deep Learning A-Z™: Self Organizing Maps (SOM) - How do SOMs learn (part 2)Kirill Eremenko
The document discusses Kohonen's Self Organizing Feature Maps, an unsupervised machine learning algorithm. Self Organizing Maps use competitive learning to map higher-dimensional input onto a lower-dimensional grid of nodes. They retain the topological properties of the input space and are useful for visualizing and clustering multidimensional data.
Deep Learning A-Z™: Self Organizing Maps (SOM) - Reading an Advanced SOMKirill Eremenko
This document provides image sources and attribution for images appearing in a deep learning course. It also includes a link and reference for additional reading on self-organizing maps and creating hexagonal heatmaps using D3.js. The sources include Wikipedia, various websites, and the deep learning course itself. A link is given to a blog post about using D3.js to create hexagonal heatmaps for self-organizing maps.
The document outlines the k-means clustering algorithm. It describes the steps of choosing the number of clusters k, randomly selecting k points as initial centroids, assigning all points to the closest centroid, recomputing the centroids, and reassigning points in an iterative process until cluster assignments stop changing. It notes that a bad initial random selection of centroids could negatively impact the resulting clusters.
The document describes the K-Means clustering algorithm. It involves choosing the number of clusters K, selecting initial centroid points, assigning data points to the closest centroid, recomputing the centroids, and reassigning points in an iterative process until centroids are stable. The algorithm partitions a dataset into K number of groups based on feature similarity between data points so that the resulting internal homogeneity is high.
The document introduces convolutional neural networks and how they process images through feature detectors that identify patterns in the input image and create a feature map. The feature detectors slide over the image, extracting features at each location and building up the feature map one value at a time.
This document provides an overview of recurrent neural networks and long short-term memory (LSTM) networks. It discusses the vanishing gradient problem with traditional RNNs and how LSTMs address this issue using gating mechanisms. Several LSTM variations and practical intuition on LSTMs are also covered.
This document provides an overview of self-organizing maps (SOM). It discusses what will be covered, including how SOM works, K-means clustering, and how SOM learns. It also includes examples of SOM and references for additional reading on the topic.
This document provides an overview of recurrent neural networks and references two related papers. It introduces the concept of RNN effectiveness and provides links to blog posts by Andrej Karpathy on the unreasonable effectiveness of RNNs and visualizing and understanding RNNs, along with their citations. Additional readings on the topic are also referenced.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.