IPAM in persian language -IPAM enables you
to centrally manage DHCP and DNS servers.
whith this service You can use IP address tracking to search the IPAM database on the basis of IP address, MAC address, computer name, or user name.
IPAM in persian language -IPAM enables you
to centrally manage DHCP and DNS servers.
whith this service You can use IP address tracking to search the IPAM database on the basis of IP address, MAC address, computer name, or user name.
The document provides tips for product managers on launching new products and features. It recommends:
1) Partnering with stakeholders early to identify and resolve issues before launch to avoid delays. Work together to evaluate options and escalate decisions as needed.
2) Thinking of a launch date as a range from an earliest to latest date to account for uncertainties and potential delays. Consider factors like design time, engineering estimates, vacations, testing periods, and deployment time.
3) Using launch reviews to improve the customer experience by checking for gaps, inconsistencies, and opportunities across marketing, documentation, pricing, and more.
This document discusses best practices for remote work and different frameworks for defining roles and responsibilities. It recommends managing expectations, using work tracking software, protecting time, reducing communication overload, checking in on people's well-being outside of work, creating space for unstructured communication, and meeting in-person periodically. It also outlines various RACI (responsible, accountable, consulted, informed) models and other acronym-based frameworks for assigning roles like ARCI, PARIS, PACSI, RASCI, RACIQ, RASI, and others.
This document discusses project management growth practices and contains recommendations in several areas:
1) Be available to your team to reduce dependencies, optimize around available resources which may be constrained by project management, engineering or the team itself.
2) Improve processes by setting up project management software, using demos to drive progress, and dedicating special days to areas like bugs, polish or internal tools.
3) Anticipate risks and have mitigation plans to determine if risks are real problems, and have rollout or other plans to address risks like stability issues.
GROWTH PRACTICES - Cracking the PM Career - CHAPTER 7Amir Shokri
This document provides guidance on growth practices for product managers. It discusses several approaches to test hypotheses quickly such as using prototypes and A/B tests. It also discusses designing for sticky usage through notifications, updates, and gamification. The document advises building a product mindset by focusing on the problem to be solved. It recommends prioritizing goals and developing a quality bar by choosing issues to fix while keeping costs in mind. Finally, it suggests considering more radical solutions by expanding perspectives on team size, deadlines, partnerships, and business models.
Numbers, math operation, converting basesAmir Shokri
numbers :
- Base number 10
- Base number 2
- Base number 8
- Base number 16
converting bases :
- Convert base 10 to base 2, to base 8, to base 16
- Convert base 8 to base 2, to base 10, to base 16
- Convert base 16 to base 2, to base 8, to base 10
- Convert base 2 to base 10, to base 8, to base 16
Mathematical operations: addition, subtraction, division, multiplication on numerical bases
GROWTH PRACTICES - Cracking the PM Career - CHAPTER 4Amir Shokri
The document discusses practices for product managers to cultivate growth. It recommends maintaining a beginner's mindset by watching new user sessions to understand their perspective. Product choices should be driven by customer insights, with a standardized process for gaining user feedback. Insights should be categorized by priority, balancing usability issues against tradeoffs and impact. Senior PMs evangelize insights through examples in meetings, research sharing, talks, and presentations to executives, surfacing the key insight that solves the problem by considering past and future implications.
The document discusses image memorability and experiments conducted to measure how memorable images are. Researchers showed Mechanical Turk workers streams of images and tested their ability to detect repeated images (the memory game). They also measured how well workers could identify whether they had seen an image before. The experiments found some images were more memorable than others and identified visual characteristics that influenced memorability.
Key.Net is a keypoint detection network that combines handcrafted and learned CNN filters in a multi-scale pyramid architecture. It extracts features at different scale levels using a combination of handcrafted and learned filters. A novel multi-scale loss and operator are used for detecting and ranking stable keypoints across scales. Experimental results on ImageNet show that Key.Net outperforms state-of-the-art detectors in terms of repeatability, matching performance, and complexity.
The document discusses Bayesian learning exercises for machine learning. It includes sample data on exercises with variables Y, X1, X2 and X3. It shows calculations of probabilities of Y=1 and Y=0 given values of X1, X2 and X3 using Bayes' theorem. It also discusses building a decision tree from the data and calculating conditional probabilities at each node.
This document discusses K-nearest neighbors (KNN) classification. KNN is an instance-based learning algorithm that stores all available cases and classifies new cases based on a vote of its neighbors. It explains how KNN classifies points based on the majority vote of its K closest neighbors, where closeness is typically defined using Euclidean or Hamming distance. It also discusses how to choose the K value, the pros and cons of KNN, and applications where KNN is often used.
This document discusses decision tree algorithms including ID3. It provides examples of applying decision tree algorithms like ID3 to sample datasets with attributes like size, color, and shape to classify examples. The document shows calculating entropy, information gain, and using information gain to select the best attribute to split on at each node when building a decision tree.
This document discusses the ID3 decision tree algorithm and provides examples of calculating information gain at different steps of building a decision tree. It shows calculating the entropy of the initial dataset, then the information gain from splitting on various attributes like Outlook, Temperature, Humidity, and Windy. It demonstrates selecting the attribute with the highest information gain at each step to build the tree, resulting in a decision tree with Temperature and Humidity nodes.
1) The document discusses two concept learning algorithms: FIND-S, which finds the most specific hypothesis consistent with examples, and Candidate Elimination, which outputs all hypotheses consistent with examples.
2) FIND-S initializes the hypothesis to the most specific and generalizes it if examples are inconsistent, while Candidate Elimination maintains a version space of consistent hypotheses.
3) Examples are provided to illustrate how each algorithm processes examples and updates its hypothesis/version space.
This document discusses decision tree, a machine learning algorithm. It explains key concepts like instances, attributes, hypotheses, training and testing sets. It also provides examples of logic gates like AND, OR, XOR, NAND and NOR. References are provided to learn more about decision tree algorithms and their use in machine learning applications.
The document provides tips for product managers on launching new products and features. It recommends:
1) Partnering with stakeholders early to identify and resolve issues before launch to avoid delays. Work together to evaluate options and escalate decisions as needed.
2) Thinking of a launch date as a range from an earliest to latest date to account for uncertainties and potential delays. Consider factors like design time, engineering estimates, vacations, testing periods, and deployment time.
3) Using launch reviews to improve the customer experience by checking for gaps, inconsistencies, and opportunities across marketing, documentation, pricing, and more.
This document discusses best practices for remote work and different frameworks for defining roles and responsibilities. It recommends managing expectations, using work tracking software, protecting time, reducing communication overload, checking in on people's well-being outside of work, creating space for unstructured communication, and meeting in-person periodically. It also outlines various RACI (responsible, accountable, consulted, informed) models and other acronym-based frameworks for assigning roles like ARCI, PARIS, PACSI, RASCI, RACIQ, RASI, and others.
This document discusses project management growth practices and contains recommendations in several areas:
1) Be available to your team to reduce dependencies, optimize around available resources which may be constrained by project management, engineering or the team itself.
2) Improve processes by setting up project management software, using demos to drive progress, and dedicating special days to areas like bugs, polish or internal tools.
3) Anticipate risks and have mitigation plans to determine if risks are real problems, and have rollout or other plans to address risks like stability issues.
GROWTH PRACTICES - Cracking the PM Career - CHAPTER 7Amir Shokri
This document provides guidance on growth practices for product managers. It discusses several approaches to test hypotheses quickly such as using prototypes and A/B tests. It also discusses designing for sticky usage through notifications, updates, and gamification. The document advises building a product mindset by focusing on the problem to be solved. It recommends prioritizing goals and developing a quality bar by choosing issues to fix while keeping costs in mind. Finally, it suggests considering more radical solutions by expanding perspectives on team size, deadlines, partnerships, and business models.
Numbers, math operation, converting basesAmir Shokri
numbers :
- Base number 10
- Base number 2
- Base number 8
- Base number 16
converting bases :
- Convert base 10 to base 2, to base 8, to base 16
- Convert base 8 to base 2, to base 10, to base 16
- Convert base 16 to base 2, to base 8, to base 10
- Convert base 2 to base 10, to base 8, to base 16
Mathematical operations: addition, subtraction, division, multiplication on numerical bases
GROWTH PRACTICES - Cracking the PM Career - CHAPTER 4Amir Shokri
The document discusses practices for product managers to cultivate growth. It recommends maintaining a beginner's mindset by watching new user sessions to understand their perspective. Product choices should be driven by customer insights, with a standardized process for gaining user feedback. Insights should be categorized by priority, balancing usability issues against tradeoffs and impact. Senior PMs evangelize insights through examples in meetings, research sharing, talks, and presentations to executives, surfacing the key insight that solves the problem by considering past and future implications.
The document discusses image memorability and experiments conducted to measure how memorable images are. Researchers showed Mechanical Turk workers streams of images and tested their ability to detect repeated images (the memory game). They also measured how well workers could identify whether they had seen an image before. The experiments found some images were more memorable than others and identified visual characteristics that influenced memorability.
Key.Net is a keypoint detection network that combines handcrafted and learned CNN filters in a multi-scale pyramid architecture. It extracts features at different scale levels using a combination of handcrafted and learned filters. A novel multi-scale loss and operator are used for detecting and ranking stable keypoints across scales. Experimental results on ImageNet show that Key.Net outperforms state-of-the-art detectors in terms of repeatability, matching performance, and complexity.
The document discusses Bayesian learning exercises for machine learning. It includes sample data on exercises with variables Y, X1, X2 and X3. It shows calculations of probabilities of Y=1 and Y=0 given values of X1, X2 and X3 using Bayes' theorem. It also discusses building a decision tree from the data and calculating conditional probabilities at each node.
This document discusses K-nearest neighbors (KNN) classification. KNN is an instance-based learning algorithm that stores all available cases and classifies new cases based on a vote of its neighbors. It explains how KNN classifies points based on the majority vote of its K closest neighbors, where closeness is typically defined using Euclidean or Hamming distance. It also discusses how to choose the K value, the pros and cons of KNN, and applications where KNN is often used.
This document discusses decision tree algorithms including ID3. It provides examples of applying decision tree algorithms like ID3 to sample datasets with attributes like size, color, and shape to classify examples. The document shows calculating entropy, information gain, and using information gain to select the best attribute to split on at each node when building a decision tree.
This document discusses the ID3 decision tree algorithm and provides examples of calculating information gain at different steps of building a decision tree. It shows calculating the entropy of the initial dataset, then the information gain from splitting on various attributes like Outlook, Temperature, Humidity, and Windy. It demonstrates selecting the attribute with the highest information gain at each step to build the tree, resulting in a decision tree with Temperature and Humidity nodes.
1) The document discusses two concept learning algorithms: FIND-S, which finds the most specific hypothesis consistent with examples, and Candidate Elimination, which outputs all hypotheses consistent with examples.
2) FIND-S initializes the hypothesis to the most specific and generalizes it if examples are inconsistent, while Candidate Elimination maintains a version space of consistent hypotheses.
3) Examples are provided to illustrate how each algorithm processes examples and updates its hypothesis/version space.
This document discusses decision tree, a machine learning algorithm. It explains key concepts like instances, attributes, hypotheses, training and testing sets. It also provides examples of logic gates like AND, OR, XOR, NAND and NOR. References are provided to learn more about decision tree algorithms and their use in machine learning applications.