Deep learning ppt

Aug 23, 2020Download as PPT, PDF1 like1,810 views

This document provides an overview of deep learning, including definitions of AI, machine learning, and deep learning. It discusses neural network models like artificial neural networks, convolutional neural networks, and recurrent neural networks. The document explains key concepts in deep learning like activation functions, pooling techniques, and the inception model. It provides steps for fitting a deep learning model, including loading data, defining the model architecture, adding layers and functions, compiling, and fitting the model. Examples and visualizations are included to demonstrate how neural networks work.

1
DEEP LEARNING
by
Mrs.Balne Sridevi
M.Tech(Ph.D),MISTE,MCSI
Asst. Professor
Dept. of CSE
Balaji Institute of Technology and Science

Contents
2
 AI, ML and DL?
 Why Deep Learning
 Neural Network Models
 Schematic Diagram of Inception Model
 Activation Functions
 Pooling Techniques
 Demo

Neural Network Models
7
 Artificial Neural Network (ANN)
 Convolution Neural Network (CNN)
 Recurrent Neural Network (RNN)

Activation Functions
13
 Sigmoid Activation Function
 ReLu Activation Function

Basic Steps to Fit a Model
 Linear Regression
 NN Model
 Load Data
 Define a model
 Add Layers, Activation Function
 Compile Model
 Fit Model
23

Scatter() And Show() Methods
 plt.scatter(x,y,color=‘r’)
Where r is red in color
 plt.show() is used to display the x and y
coordinates
25

Load Data
 Import pandas Library
 Create CSV File
 Load Data by using read_csv() method
 Syntax: data_frame = pd. read_csv(‘ file
name.csv’)
28

Define Model
 Import model from keras library
 Import layers from keras library
 Define model as model=Sequential()
29

Add Layers and Activation Functions
 model.add(Any_net_Inception_model(Number of
layers), activation Function=‘function_name’)
 Inception Models are Dense,Imagenet,Residual
Net V1, V2
30

$Compile Model  model.compile(loss,optimizer,metrics)  Loss={binary_crossentropy (multi Label ) or categorical_crossentropy (multi classes))  Optimizers={adam or adagrad or sgd etc..}  metrics={accuracy ,count etc..} 31$

Fit a Model
 model.fit(x,y,epochs,batch_size)
32

A Deep Dream of Neural
Network
https://www.youtube.com/watch?v=sh-MQboWJug
45

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Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology. It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics. Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion. Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid. Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic. Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow. Fundamentally, every fluid mechanical system is assumed to obey the basic laws : Conservation of mass Conservation of energy Conservation of momentum The continuum assumption For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume. The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale

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A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation. ⚙️ How a Boiler Feed Pump Works Water Collection: Feedwater is collected from the deaerator or feedwater tank. Pressurization: The pump increases water pressure using multiple impellers/stages in centrifugal types. Discharge to Boiler: Pressurized water is then supplied to the boiler drum or economizer section, depending on design. 🌀 Types of Boiler Feed Pumps Centrifugal Pumps (most common): Multistage for higher pressure. Used in large thermal power stations. Positive Displacement Pumps (less common): For smaller or specific applications. Precise flow control but less efficient for large volumes. 🛠️ Key Operations and Controls Recirculation Line: Protects the pump from overheating at low flow. Throttle Valve: Regulates flow based on boiler demand. Control System: Often automated via DCS/PLC for variable load conditions. Sealing & Cooling Systems: Prevent leakage and maintain pump health. ⚠️ Common BFP Issues Cavitation due to low NPSH (Net Positive Suction Head). Seal or bearing failure. Overheating from improper flow or recirculation.

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In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in the further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further, this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi-angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array, and then optimization is done in data analysis software Minitab 17. The results of ANOVA shows that 15 degrees die semi-angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degrees die semi-angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally, the results of experimentation are validated with Finite Element Analysis technique using ANSYS.

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Smart Storage Solutions.pptx for production engineeringrushikeshnavghare94

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Deep learning ppt

1. 1 DEEP LEARNING by Mrs.Balne Sridevi M.Tech(Ph.D),MISTE,MCSI Asst. Professor Dept. of CSE Balaji Institute of Technology and Science

2. Contents 2  AI, ML and DL?  Why Deep Learning  Neural Network Models  Schematic Diagram of Inception Model  Activation Functions  Pooling Techniques  Demo

3. AI,ML & DL 3

4. 4

5. Why Deep Learning? 5

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7. Neural Network Models 7  Artificial Neural Network (ANN)  Convolution Neural Network (CNN)  Recurrent Neural Network (RNN)

8. Neural Network Model 8

9. ANN Architecture 9

10. CNN Architecture 10

11. RCNN 11

12. Schematic Diagram of Inception Model

13. Activation Functions 13  Sigmoid Activation Function  ReLu Activation Function

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16. Max Pooling 16

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19. Average Pooling with stride 2

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23. Basic Steps to Fit a Model  Linear Regression  NN Model  Load Data  Define a model  Add Layers, Activation Function  Compile Model  Fit Model 23

24. Linear Regression 24

25. Scatter() And Show() Methods  plt.scatter(x,y,color=‘r’) Where r is red in color  plt.show() is used to display the x and y coordinates 25

26. How Neural Network Works? 26

27. How Neural Network Works? 27

28. Load Data  Import pandas Library  Create CSV File  Load Data by using read_csv() method  Syntax: data_frame = pd. read_csv(‘ file name.csv’) 28

29. Define Model  Import model from keras library  Import layers from keras library  Define model as model=Sequential() 29

30. Add Layers and Activation Functions  model.add(Any_net_Inception_model(Number of layers), activation Function=‘function_name’)  Inception Models are Dense,Imagenet,Residual Net V1, V2 30

31. Compile Model  model.compile(loss,optimizer,metrics)  Loss={binary_crossentropy (multi Label ) or categorical_crossentropy (multi classes))  Optimizers={adam or adagrad or sgd etc..}  metrics={accuracy ,count etc..} 31

32. Fit a Model  model.fit(x,y,epochs,batch_size) 32

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