This document provides information about an internship in artificial intelligence using Python. It includes abbreviations commonly used in AI and machine learning and compares human organs to AI tools. It also discusses basics of AI, concepts in AI like machine learning and neural networks, qualities of humans and AI, important software for AI like Anaconda and TensorFlow, and types of machine learning algorithms. The document provides an overview of the topics that will be covered in the internship.
Hot Topics in Machine Learning for Research and ThesisWriteMyThesis
Machine Learning is a hot topic for research for research. There are various good thesis topics in Machine Learning. Writemythesis provides thesis in Machine Learning along with proper guidance in this field. Find the list of thesis topics in this document.
http://www.writemythesis.org/master-thesis-topics-in-machine-learning/
Hello guys! The ppt consists of a machine learning introduction.
What are the things we will be learning on this ppt?
1. Prerequisites before learning machine learning
- Python(programming language)
- Python libraries
2. Machine learning
3. Types of machine learning
4. Applications of Machine learning
5. Advantages of Machine learning
6. Simple Example of Machine learning
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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These slides are from a presentation on understanding Machine Learning at a high level. The talk touches on linear regression, neural networks, and how Deep Learning fits into Machine Learning.
Hot Topics in Machine Learning for Research and ThesisWriteMyThesis
Machine Learning is a hot topic for research for research. There are various good thesis topics in Machine Learning. Writemythesis provides thesis in Machine Learning along with proper guidance in this field. Find the list of thesis topics in this document.
http://www.writemythesis.org/master-thesis-topics-in-machine-learning/
Hello guys! The ppt consists of a machine learning introduction.
What are the things we will be learning on this ppt?
1. Prerequisites before learning machine learning
- Python(programming language)
- Python libraries
2. Machine learning
3. Types of machine learning
4. Applications of Machine learning
5. Advantages of Machine learning
6. Simple Example of Machine learning
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
These slides are from a presentation on understanding Machine Learning at a high level. The talk touches on linear regression, neural networks, and how Deep Learning fits into Machine Learning.
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
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
4. Human Organ – AI Tools
Eye Camera
Nose Sensor
Ear MIC, Mircrophone
Mouth Speaker
Tongue (Taste) Sensors
Hands & Legs Motors (Robotics)
Emotions Software
5. AI – Basics
Father of Artificial Intelligence is John McCarthy.
The science and engineering of making intelligent
machines, especially intelligent computer programs.
Artificial Intelligence is a way of making a
computer, a computer-controlled robot, or a software
think intelligently, in the similar manner the intelligent
humans think.
AI is accomplished by studying how human brain
thinks and how humans learn, decide, and work while
trying to solve a problem, and then using the outcomes
of this study as a basis of developing intelligent
software and systems.
6.
7.
8.
9. Concepts in AI
Machine Learning
NLTK Package (Natural Language Toolkit Package) ,
NLP(Natural Language Processing)
Speech Recognition
Heuristic Search
Gaming
Neural Networks
Genetic Algorithms
Computer Vision
Deep Learning
Robotics
10. Good Qualities of Human & AI
Human is good with emotions, thinking and can
perform huge number of activities with the support of
all the external organs.
Machines lifting 1000 kg weight (JCB),
AI JCB (Robotics) with camera, emotions
(sensors and SW), Speaker to tell about issues.
11. Human - AI
AI Goal to create system which can function
Intelligently and Independently.
Human Speak &Listen Speech Recognition
Write & Read Text NLP (Natural Language
Processing).
See with Eyes, Process, Recognize Computer
Vision (Image Processing)
Understand & Move freely Robotics
Like & Unlike objects grouping and Patterns
Pattern Recognition (Machine Learning, more data
and dimensions of data)
12. Human - AI
Brain, Network of Neurons Neural Network
More complex & deeper Deep Learning or Deep
Neural Network
13. Key Points
AI ML, IP, NN, CNN, DS, DL all these topics are
part of AI
AI or ML Data to train the algorithm.
17. Numpy
NumPy is an open source library available in Python
that aids in mathematical, scientific, engineering, and
data science programming.
Multi-dimensional arrays and matrices
multiplication.
The library’s name is actually short for "Numeric
Python" or "Numerical Python".
NumPy is memory efficiency, meaning it can handle
the vast amount of data more accessible than any other
library.
18. scikit-learn
Simple and efficient tools for data mining and data
analysis
Accessible to everbody, and reusable in various
contexts
Built on Numpy, SciPy, and matplotlib
Open source, commercially usable - BSD license
19. Tensorflow
Developed by Google
Open Source
The Advantages of TensorFlow are - It has excellent
community support, It is designed to use various backend
software (GPUs, ASIC), etc. and also highly parallel, It has a
unique approach that allows monitoring the training progress
of our models and tracking several metrics, & Its performance
is high and matching the best in the industry.
There are 4 main tensor type you can create in TensorFlow -
tf.Variable, tf.constant, tf.placeholder, & tf.SparseTensor.
20. AI - Types
Symbolic Based Computer Vision, Image
Processing, Camera, Video, Image, Robotics.
Machine Learning Based on data. Feed machine
with lot of data, so it can learn and perform. (Ex – Lot
of data of Sales versus Advertising spent, then ML can
learn and draw pattern in more than 100 and 1000
dimensions)
ML Classification & Prediction (Shop Ex –
classifying customers as per data (old / new, age) and
assign toys, and predict their next interest of purchase)
21. Machine Learning
Machine learning field of computer science, an
application of artificial intelligence, which provides
computer systems the ability to learn with data and
improve from experience without being explicitly
programmed.
The main focus of machine learning is to allow the
computers learn automatically without human
intervention.
Observations of data The data can be some
examples, instruction or some direct experiences too.
Then on the basis of this input, machine makes better
decision by looking for some patterns in data.
25. Types of Machine Learning Algorithms
Supervised machine learning algorithms
Unsupervised machine learning algorithms
Reinforcement machine learning algorithms
AI is a combination of complex algorithms
from the various mathematical domains such as
Algebra, Calculus, and Probability and
Statistics.
26. Supervised machine learning algorithms
This is the most commonly used machine learning
algorithm.
It is called supervised because the process of
algorithm learning from the training dataset can be
thought of as a teacher supervising the learning
process. In this kind of ML algorithm, the possible
outcomes are already known and training data is also
labeled with correct answers.
27. Supervised machine learning algorithms
Mainly supervised leaning problems can be divided
into the following two kinds of problems −
Classification − A problem is called classification
problem when we have the categorized output such as
“black”, “teaching”, “non-teaching”, etc. (Ex: Classify
all types of flowers, Classify Dolphin or Seahorse)
Regression − A problem is called regression problem
when we have the real value output such as “distance”,
“kilogram”, etc.
30. Supervised - Regression
Regression is one of the most important statistical and
machine learning tools.
It may be defined as the parametric technique that allows us
to make decisions based upon data or in other words allows us
to make predictions based upon data by learning the
relationship between input and output variables. Here, the
output variables dependent on the input variables, are
continuous-valued real numbers.
In regression, the relationship between input and output
variables matters and it helps us in understanding how the
value of the output variable changes with the change of input
variable. Regression is frequently used for prediction of prices,
economics, variations, and so on.
32. Unsupervised machine learning algorithms
algorithms do not have any supervisor to provide
any sort of guidance. That is why unsupervised
machine learning algorithms are closely aligned with
what some call true artificial intelligence.
Suppose we have input variable x, then there will be
no corresponding output variables as there is in
supervised learning algorithms.
In simple words, we can say that in unsupervised
learning there will be no correct answer and no teacher
for the guidance. Algorithms help to discover
interesting patterns in data.
33. Unsupervised machine learning algorithms
Unsupervised learning problems can be divided into the
following two kinds of problem −
Clustering − In clustering problems, we need to discover the
inherent groupings in the data. The main goal of clustering is
to group the data on the basis of similarity and dissimilarity.
For example, grouping customers by their purchasing
behavior.
Association − A problem is called association problem
because such kinds of problem require discovering the rules
that describe large portions of our data. For example, finding
the customers who buy both x and y.
37. Reinforcement machine learning algorithms
These kinds of machine learning algorithms are used
very less.
These algorithms train the systems to make specific
decisions. Basically, the machine is exposed to an
environment where it trains itself continually using the
trial and error method. These algorithms learn from
past experience and try to capture the best possible
knowledge to make accurate decisions.
Markov Decision Process is an example of
reinforcement machine learning algorithms.
38.
39.
40.
41. Block or Architecture Diagram
Laptop python
Dataset images
/ Excel Dataset
Algorithm
Output display
Loan available
or not
Library opencv,
sklearn
42. Methodology
• Input Image – Real time Camera / Static Image / Excel
Dataset
• Import Libraries
• Dataset Visualization
• processing of the data
• feature extraction
• Graphical View output
• Split Train and Test Data
• Machine learning Analysis
• Output Prediction
43. Dataset Analysis
Dataset Columns are called as Features, Dimensions,
Attributes, Variables, Parameters etc.
Balanced or Imbalanced Data.
Output Class or Label
Parameters
Check value counts of required parameters (Ex output)
Fill empty cells with Average value of that parameters.
Remove empty Cells.
Remove imbalanced data
Delete unwanted columns
44. Preprocessing the Data
ML require formatted data to start the training
process. We must prepare or format data in a certain
way so that it can be supplied as an input to ML
algorithms.
In our daily life, we deal with lots of data but this
data is in raw form. To provide the data as the input of
machine learning algorithms, we need to convert it
into a meaningful data.
In other simple words, we can say that before
providing the data to the machine learning algorithms
we need to preprocess the data.
45. Preprocessing Steps
Importing the useful packages import numpy, sklearn,
matplotlib etc.
Example Dataset online, library or download in excel csv
format.
Binarization all the values above 0.5(threshold value)
would be converted to 1 and all the values below 0.5 would be
converted to 0.
Mean Removal
Scaling
Normalization
Labeling the data
Train data & Test data
46. Input Image
When a computer sees an image, it will see an array of pixel
values.
Let's say we have a color image in JPG form and its size is
480 x 480. The representative array will be 480 x 480 x 3
array of numbers (The 3 refers to RGB values).
Each of these numbers is given a value from 0 to 255 which
describes the pixel intensity at that point.