Deep Learning A-Z™: Artificial Neural Networks (ANN) - How do Neural Networks Work

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Kirill Eremenko

The document describes a neural network model for predicting housing prices. The model contains an input layer with features like area, bedrooms, distance to city and age. A hidden layer learns relationships between the inputs. The output layer makes predictions using weighted combinations of the inputs. Housing price is predicted as the weighted sum of the input features.

© SuperDataScienceDeep Learning A-Z

© SuperDataScienceDeep Learning A-Z

© SuperDataScienceDeep Learning A-Z
.
Output Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
w1
w2
w3
w4
Price = w1*x1+ w2*x2+ w3*x3+ w4*x4
Input Layer
X4
X3
X2
X1
y

© SuperDataScienceDeep Learning A-Z
Input Layer
X4
X3
X2
X1Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer
.

© SuperDataScienceDeep Learning A-Z
Input Layer
X4
X3
X2
X1Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer
.

© SuperDataScienceDeep Learning A-Z
Input Layer
X4
X3
X2
X1Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer
X4
X3
X2
X1

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer
X4
X3
X2
X1

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
y
Hidden Layer Output Layer
X4
X3
X2
X1

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
Hidden Layer Output Layer
X4
X3
X2
X1
y

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
Hidden Layer Output Layer
X4
X3
X2
X1
y

© SuperDataScienceDeep Learning A-Z
Input Layer
Area (feet2)
Bedrooms
Distance to city (Miles)
Age
Hidden Layer Output Layer
X4
X3
X2
X1
y Price

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Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction. This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.

Operation Blue Star - Saka Neela Tara

Operation Blue Star - Saka Neela Tara

Operation Blue Star - Saka Neela Tara

Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup. The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.

A Strategic Approach: GenAI in Education

A Strategic Approach: GenAI in Education

A Strategic Approach: GenAI in Education

Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction. This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.

BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...

BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...

BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...

Nguyen Thanh Tu Collection

How libraries can support authors with open access requirements for UKRI fund...

How libraries can support authors with open access requirements for UKRI fund...

How libraries can support authors with open access requirements for UKRI fund...

2024.06.01 Introducing a competency framework for languag learning materials ...

2024.06.01 Introducing a competency framework for languag learning materials ...

2024.06.01 Introducing a competency framework for languag learning materials ...

http://sandymillin.wordpress.com/iateflwebinar2024 Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error. Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials. This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.

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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf

Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf

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Mule 4.6 & Java 17 Upgrade | MuleSoft Mysore Meetup #46 Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-exploring-gemini-ai-and-integration-with-mulesoft/ Agenda ● Java 17 Upgrade Overview ● Why and by when do customers need to upgrade to Java 17? ● Is there any immediate impact to upgrading to Mule Runtime 4.6 and beyond? ● Which MuleSoft products are in scope? For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/ YouTube:- youtube.com/@mulesoftmysore Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N Speaker:- Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/ Priya Shaw - https://www.linkedin.com/in/priya-shaw Organizers:- Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/ Giridhar Meka - https://www.linkedin.com/in/giridharmeka Priya Shaw - https://www.linkedin.com/in/priya-shaw Shyam Raj Prasad- https://www.linkedin.com/in/shyam-raj-prasad/

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Sha'Carri Richardson Presentation 202345

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Delapenabediema

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678020731-Sumas-y-Restas-Para-Colorear.pdf

678020731-Sumas-y-Restas-Para-Colorear.pdf

CarlosHernanMontoyab2

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3. © SuperDataScienceDeep Learning A-Z . Output Layer Area (feet2) Bedrooms Distance to city (Miles) Age w1 w2 w3 w4 Price = w1*x1+ w2*x2+ w3*x3+ w4*x4 Input Layer X4 X3 X2 X1 y

4. © SuperDataScienceDeep Learning A-Z Input Layer X4 X3 X2 X1Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer .

5. © SuperDataScienceDeep Learning A-Z Input Layer X4 X3 X2 X1Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer .

6. © SuperDataScienceDeep Learning A-Z Input Layer X4 X3 X2 X1Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer

7. © SuperDataScienceDeep Learning A-Z Input Layer Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer X4 X3 X2 X1

8. © SuperDataScienceDeep Learning A-Z Input Layer Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer X4 X3 X2 X1

9. © SuperDataScienceDeep Learning A-Z Input Layer Area (feet2) Bedrooms Distance to city (Miles) Age y Hidden Layer Output Layer X4 X3 X2 X1

10. © SuperDataScienceDeep Learning A-Z Input Layer Area (feet2) Bedrooms Distance to city (Miles) Age Hidden Layer Output Layer X4 X3 X2 X1 y

11. © SuperDataScienceDeep Learning A-Z Input Layer Area (feet2) Bedrooms Distance to city (Miles) Age Hidden Layer Output Layer X4 X3 X2 X1 y

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