Artificial Neural Networks_Part-1.pptx

•Download as PPTX, PDF•

0 likes•8 views

This document appears to be a lecture presentation on deep learning. It includes: 1) An evaluation scheme for the course with various assessments worth different percentages of the total marks. 2) An overview of topics to be covered including neural networks, neuron types, and feedforward networks. 3) A textbook definition of deep learning as a machine learning approach that trains models on examples rather than explicit rules.

•
Amrita
School
of
Engineering,
Bangalore
19ECE354 - Deep Learning
Ms. HARIKA PUDUGOSULA
Lecturer
Department of Electronics & Communication Engineering

Type of Evaluation Maximum
Marks
Weightage
Periodical 1 50 15
Periodical 2 50 15
End Semester
End Semester (Written) 100 30
2 Lab Tests (10+10) 20 20
Continuous Assessment
2 MCQ-Type Quizzes (05) 30 05
3 Programming Assignments (15) 30 15
Total 280 100
Evaluation Pattern
HARIKA 4

• The Neuron
• Expressing Linear Perceptrons as Neurons
• Feed-Forward Neural Networks
• Linear Neurons and Their Limitations
• Sigmoid, Tanh, and ReLU Neurons
• Softmax Output Layers
6
HARIKA

HARIKA 7
fig: Image from MNIST handwritten digit dataset
A zero that’s
algorithmically
difficult to distinguish
from a six

HARIKA 8
Text book defination of Deep Learning -
• Deep learning is a subset of a more general field of AI called machine
learning, which is predicated on this idea of learning from example.
• In machine learning, instead of teaching a computer a massive list of
rules to solve the problem, we give it a model with which it can
evaluate examples, and a small set of instructions to modify the model
when it makes a mistake.

19
HARIKA
https://cdn.vectorstock.com/i/composite/12,25/neuron-cell-vector-81225.jpg

https://cdn.vectorstock.com/i/composite/12,25/neuron-cell-vector-81225.jpg
20
HARIKA

This document appears to be notes from a lecture on deep learning and artificial neural networks given by Ms. Harika Pudugosula. It discusses key concepts like binary classification, linear perceptrons, feed-forward neural networks, activation functions like sigmoid and ReLU, and softmax output layers. Examples are provided to explain how neural networks can be used for problems like predicting exam performance based on sleep and study hours. Links are included at the end to additional explanatory videos on neural network topics.

Introduction.pptx

Harika Pudugosula

This document summarizes a lecture on deep learning and artificial neural networks. It includes definitions of deep learning and machine learning. It discusses the neuron as the basic unit of an artificial neural network. It also explains how linear perceptrons can be expressed as neurons, and how neurons are more expressive than linear perceptrons since they can model problems that perceptrons cannot. Examples of parameter optimization and dealing with uneven data distributions are also briefly covered.

CPU Scheduling Part-III.pdf

Harika Pudugosula

CPU scheduling is the basis of multiprogrammed operating systems. By switching the CPU among processes, the operating system can make the computer more productive - To introduce CPU scheduling, which is the basis for multiprogrammed operating systems - To describe various CPU-scheduling algorithms - To discuss evaluation criteria for selecting a CPU-scheduling algorithm for a particular system - To examine the scheduling algorithms of several operating systems

CPU Scheduling Part-II.pdf

Harika Pudugosula

This document discusses different CPU scheduling algorithms and their optimization criteria. It describes the First Come First Serve (FCFS) algorithm and provides examples to show how process wait times can vary based on arrival order. The Shortest Job First (SJF) algorithm is also covered, explaining how it selects the process with the shortest remaining CPU burst time to optimize average wait time. The document concludes by discussing how exponential averaging can be used to predict future CPU burst lengths for SJF scheduling.

CPU Scheduling Part-I.pdf

Harika Pudugosula

CPU scheduling is the basis of multi-programmed operating systems. By switching the CPU among processes, the operating system can make the computer more productive - To introduce CPU scheduling, which is the basis for multi-programmed operating systems - To describe various CPU-scheduling algorithms - To discuss evaluation criteria for selecting a CPU-scheduling algorithm for a particular system - To examine the scheduling algorithms of several operating systems

Multithreaded Programming Part- III.pdf

Harika Pudugosula

The objectives of Multithreaded Programming in Operating Systems are: - To introduce the notion of a thread—a fundamental unit of CPU utilization that forms the basis of multithreaded computer systems. - To discuss the APIs for the Pthreads, Windows, and Java thread libraries - To explore several strategies that provide implicit threading. - To examine issues related to multithreaded programming. - To cover operating system support for threads in Windows and Linux.

Multithreaded Programming Part- II.pdf

Harika Pudugosula

Multithreaded Programming Part- I.pdf

Harika Pudugosula

The objectives of Multithreaded programming in Operating systems are: - To introduce the notion of a thread—a fundamental unit of CPU utilization that forms the basis of multithreaded computer systems. - To discuss the APIs for the Pthreads, Windows, and Java thread libraries - To explore several strategies that provide implicit threading. - To examine issues related to multithreaded programming. - To cover operating system support for threads in Windows and Linux.

This document provides an overview of operating system services, the user and operating system interface, system calls, and system programs. It describes the various services an operating system provides, including user interfaces, program execution, I/O operations, file manipulation, communications, error detection, resource allocation, accounting, and protection. It discusses system call implementation and different types of system calls. Finally, it explains common system programs for file management, system status information, file modification, programming language support, program loading and execution, communications, and background services.

Lecture-4_Process Management.pdf

Harika Pudugosula

Lecture_3-Process Management.pdf

Harika Pudugosula

Lecture- 2_Process Management.pdf

Harika Pudugosula

Lecture - 1_Process Management.pdf

Harika Pudugosula

Introduction to Operating Systems.pdf

Harika Pudugosula

Digital Artifact 1 - 10VCD Environments Unit

chanes7

Your Skill Boost Masterclass: Strategies for Effective Upskilling

Excellence Foundation for South Sudan

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

Mohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia

Natural birth techniques - Mrs.Akanksha Trivedi Rama University

Akanksha trivedi rama nursing college kanpur.

A Independência da América Espanhola LAPBOOK.pdf

Jean Carlos Nunes Paixão

Digital Artefact 1 - Tiny Home Environmental Design

amberjdewit93

Recently uploaded

Digital Artifact 1 - 10VCD Environments Unit

chanes7

Your Skill Boost Masterclass: Strategies for Effective Upskilling

Excellence Foundation for South Sudan

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

Mohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia

Natural birth techniques - Mrs.Akanksha Trivedi Rama University

Akanksha trivedi rama nursing college kanpur.

A Independência da América Espanhola LAPBOOK.pdf

Jean Carlos Nunes Paixão

Digital Artefact 1 - Tiny Home Environmental Design

amberjdewit93

MARY JANE WILSON, A “BOA MÃE” .

Colégio Santa Teresinha

ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf

Priyankaranawat4

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

Nguyen Thanh Tu Collection

Liberal Approach to the Study of Indian Politics.pdf

WaniBasim

Main Java[All of the Base Concepts}.docx

adhitya5119

South African Journal of Science: Writing with integrity workshop (2024)

Academy of Science of South Africa

Lapbook sobre os Regimes Totalitários.pdf

Jean Carlos Nunes Paixão

DRUGS AND ITS classification slide share

taiba qazi

C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx

mulvey2

The History of Stoke Newington Street Names

History of Stoke Newington

Advanced Java[Extra Concepts, Not Difficult].docx

adhitya5119

How to Add Chatter in the odoo 17 ERP Module

Celine George

Life upper-Intermediate B2 Workbook for student

NgcHiNguyn25

World environment day ppt For 5 June 2024

ak6969907

Recently uploaded (20)

Digital Artifact 1 - 10VCD Environments Unit

Your Skill Boost Masterclass: Strategies for Effective Upskilling

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

Natural birth techniques - Mrs.Akanksha Trivedi Rama University

A Independência da América Espanhola LAPBOOK.pdf

Digital Artefact 1 - Tiny Home Environmental Design

MARY JANE WILSON, A “BOA MÃE” .

ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

Liberal Approach to the Study of Indian Politics.pdf

Main Java[All of the Base Concepts}.docx

South African Journal of Science: Writing with integrity workshop (2024)

Lapbook sobre os Regimes Totalitários.pdf

DRUGS AND ITS classification slide share

C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx

The History of Stoke Newington Street Names

Advanced Java[Extra Concepts, Not Difficult].docx

How to Add Chatter in the odoo 17 ERP Module

Life upper-Intermediate B2 Workbook for student

World environment day ppt For 5 June 2024

Artificial Neural Networks_Part-1.pptx

1. • Amrita School of Engineering, Bangalore 19ECE354 - Deep Learning Ms. HARIKA PUDUGOSULA Lecturer Department of Electronics & Communication Engineering

2. HARIKA 2

3. HARIKA 3

4. Type of Evaluation Maximum Marks Weightage Periodical 1 50 15 Periodical 2 50 15 End Semester End Semester (Written) 100 30 2 Lab Tests (10+10) 20 20 Continuous Assessment 2 MCQ-Type Quizzes (05) 30 05 3 Programming Assignments (15) 30 15 Total 280 100 Evaluation Pattern HARIKA 4

5. Artificial Neural Networks HARIKA 5

6. • The Neuron • Expressing Linear Perceptrons as Neurons • Feed-Forward Neural Networks • Linear Neurons and Their Limitations • Sigmoid, Tanh, and ReLU Neurons • Softmax Output Layers 6 HARIKA

7. HARIKA 7 fig: Image from MNIST handwritten digit dataset A zero that’s algorithmically difficult to distinguish from a six

8. HARIKA 8 Text book defination of Deep Learning - • Deep learning is a subset of a more general field of AI called machine learning, which is predicated on this idea of learning from example. • In machine learning, instead of teaching a computer a massive list of rules to solve the problem, we give it a model with which it can evaluate examples, and a small set of instructions to modify the model when it makes a mistake.

9. 9 HARIKA

10. 10 HARIKA

11. 11 HARIKA

12. 12 HARIKA

13. 13 HARIKA

14. 14 HARIKA

15. 15 HARIKA

16. 16 HARIKA

17. 17 HARIKA

18. 18 HARIKA

19. 19 HARIKA https://cdn.vectorstock.com/i/composite/12,25/neuron-cell-vector-81225.jpg

20. https://cdn.vectorstock.com/i/composite/12,25/neuron-cell-vector-81225.jpg 20 HARIKA