This document discusses the principles of training, including the FITT principles of frequency, intensity, time, and type of training. It explains that training needs to be regular and consistent to produce adaptations while avoiding overtraining. Intensity needs to be managed appropriately for the individual and training goal. A variety of training methods and progressive overload over time are important to continued improvement without plateauing. Specificity of training and adequate rest are also principles to keep in mind for optimal training.
ಪ್ರತಿದಿನ ಚೆನ್ನಾಗಿ ನಿದ್ದೆ, ವ್ಯಾಯಾಮ ಮಾಡಿ. ಧ್ಯಾನ ಯೋಗದ ಮೂಲಕ ಸಕ್ರಿಯ ಜೀವನ ಅಳವಡಿಸಿಕೊಳ್ಳಿ. ಆರೋಗ್ಯಕರ ಜೀವನ ಶೈಲಿ ನಿಮ್ಮ ಯೌವನಕ್ಕೂ ಆರೋಗ್ಯಕರ ತ್ವಚೆಗೂ ಕಾರಣವಾಗುತ್ತದೆ.
This workshop presentation discuss theory and guidelines of Preparation, Rest and Recovery for Athletes or the Athletic minded. Topics include discussion of over-training syndrome and 10 ways to recover after exercise.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
ಪ್ರತಿದಿನ ಚೆನ್ನಾಗಿ ನಿದ್ದೆ, ವ್ಯಾಯಾಮ ಮಾಡಿ. ಧ್ಯಾನ ಯೋಗದ ಮೂಲಕ ಸಕ್ರಿಯ ಜೀವನ ಅಳವಡಿಸಿಕೊಳ್ಳಿ. ಆರೋಗ್ಯಕರ ಜೀವನ ಶೈಲಿ ನಿಮ್ಮ ಯೌವನಕ್ಕೂ ಆರೋಗ್ಯಕರ ತ್ವಚೆಗೂ ಕಾರಣವಾಗುತ್ತದೆ.
This workshop presentation discuss theory and guidelines of Preparation, Rest and Recovery for Athletes or the Athletic minded. Topics include discussion of over-training syndrome and 10 ways to recover after exercise.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
mubeen.ppt
1. Presented by Mubeen
Roll no 22011512-035
Department PESS
Course title
Course code PE100
Presented to Sir Syed Muhammad
Ishaq
Topic Principle of training
and coaching
PESS
2. Principles of Training
For any training programme to improve
an athletes performance, the coach, or
the athlete themselves should follow
some basic training principles.
Basic training programmes sometimes
use the FITT principles.
3. The FITT Principle
The FITT Principle is a basic framework
for training programmes that will
improve performance:
Frequency: how often you exercise
Intensity: how hard you work
Time: how long you spend exercising
Type: of training you are doing
We will look at some more detailed
principles which are derived from the
FITT Principle.
However, firstly lets look at FITT.
4. Frequency
The training frequency, or, how often you will
train is dependent on;
The type of training that is being done (strength
or endurance, etc, etc) and…..
The type of sport or activity you are training for.
We can, however, make the following general
statements:
Endurance athletes: Should train 4 - 6 times per week
Non-Endurance athletes : 2-4 times per week
5. Frequency
Summary
Training needs to be regular and consistent for
your body to be able to adapt and improve.
The frequency of training sessions is important,
because…….
If you don’t train frequently enough, you will not
produce enough stimulation for the body to make
positive adaptations and improve and…..
if you train too often, you risk being too tired to
train effectively, becoming stale or getting injured.
6. Intensity
Knowing how hard to work during a training
session is important if you want to improve in
performance.
If intensity is too high you risk fatiguing too
early and not finishing a training session. If
intensity is too low you risk not working hard
enough and you will not get improvements in
fitness or performance.
7. Intensity
Training intensity can be managed
many different ways, because there are
many different ways & reasons to train.
You can train using different target heart rate zones.
You can train for longer sessions (60mins instead of 30)
You can train for longer periods of time (6 wks instead of 4)
You can increase resistance in resistance training
You can decrease rest periods between exercises
You can train at a faster tempo
You can run dragging a tyre or paddle towing a bucket
8. Time
The length of a training session will be
determined by a number of factors
including:
What type of training you are doing
What you are training for
Which fitness component(s) you are
working at developing
10. The Principles of Training
Progressive
Overload
Variety
Specificity
Reversibility
Rest
11. Progressive Overload
This is the foundation principle behind all
training programmes.
If an individual wants to improve their fitness
or performance they must exercise at an
intensity greater than his or her existing
capacity.
If the training load exceeds the load to which
the body is accustomed, the body will adapt
and make improvements.
12. Progressive Overload
The body cant adapt overnight to increased
demands. It can only adapt gradually or
progressively.
Exercising too hard or too soon, can lead to
injury, over tiredness and premature burn
out.
Also; your training program must get harder
& harder (increase in intensity) as your skill
and fitness levels get better and better, or…..
you will plateau and stop improving.
13. Variety
They say ‘variety is the spice of life’. This saying
applies equally to the training situation.
A variety of activities in a training programme have
benefits for the athlete. These include:
Improved motivation
Preventing boredom
Help overcome plateaus in training
Variety can be added to a training programme by:
Using different training methods
Training in different locations
Using a range of games at training
14. Specificity
Basically, ‘you get what you train for’.
You should train the energy systems
and muscle groups specific to the
sport/activity being played.
This means:
You must first decide what you want to
improve
Then choose suitable exercises
15. Reversibility
The gains you make when training for a period of
time can quite easily be lost if you stop frequently
training. As a general rule, the longer the build up,
the slower the loss of adaptations.
This means:
You must keep to the training programme if you really want
to improve performance
If you take a break because of injury / illness / holiday, you
need to start again at a lower level.
E.g. The gains from weight training will be lost if you
stop training. That is a big strong weight lifter will
not be as strong when he stops lifting weights.
16. Rest
Perhaps the most important thing about
rest is when it takes place.
Rest plays a key role in recovery after
training and competing and preventing
overuse injuries as a result of
overtraining.