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Understanding computer vision with Deep Learning
The document outlines CloudxLab's AI-enabled ed-tech platform designed to address the skills gap in emerging technologies through a gamified learning environment, collaborative learning, and real-time feedback. It highlights various features such as an online lab for building machine learning models, an auto-assessment engine, and user behavioral analytics, making deep tech accessible with minimal installation. Additionally, it emphasizes the importance of practice and adaptability in learning, supported by positive user feedback and engagement metrics.
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Understanding computer vision with Deep Learning
- 1.
- 2. Agenda ● About CloudxLab ●About Speaker ● Intro to Machine Learning ● Gradient Descent & Back Propagation ● Neural networks & CNN ● Computer Vision ● GANs ● Face Embedding
- 3. CloudxLab.com AI enabled Ed-Techplatform solving the skill-gap in emerging technologies.
- 4. CloudxLab.com Learning can behard without the right tools, especially Deep Tech The Problem Plethora of content but not engaging enough The instant feedback is not available Content alone in not sufficient. Need Practice. One course doesn’t fit all. Every user is different
- 5. CloudxLab.com How are wesolving it? Gamified Learning Environment Knowledge Graph To engage you and your peers for collaborative learning To identify gaps, and strengths, and to improve learning curve
- 6. CloudxLab.com Gamified Learning Environment BootMLTM - Buildmachine learning models on the fly. Auto Assessment Engine Online Lab - Playground Hooks - Concept Wise Discussion, XP Points, Forum, Leaderboard Auto Quiz, Slides Finder in Videos Best in class content User Behavioural Analytics
- 7. CloudxLab.com Online Lab -Playground Everything available on Cloud. Zero installation - Get started instantaneously. Real Experience - Real Learning Access from anywhere, anytime 24x7. Shared Datasets and Quick to Debug Very easy to integrate into any LMS Online Lab - PlaygroundAI Powered gamified environment
- 8. CloudxLab.com Online Lab -PlaygroundAI Powered gamified environment Technologies available on Cloud Every open source tool - AI & Big Data
- 9. CloudxLab.com Auto Assessment EngineAIPowered gamified environment
- 10. CloudxLab.com Auto Assessment EngineAIPowered gamified environment
- 11. CloudxLab.com Best In ClassContentAI Powered gamified environment
- 12. CloudxLab.com Social Hooks: JobsAIPowered gamified environment
- 13. CloudxLab.com Social Hooks: JobsAIPowered gamified environment
- 14. CloudxLab.com Social Hooks: ForumAIPowered gamified environment
- 15. CloudxLab.com BootML - buildmachine learning models on the fly.AI Powered gamified environment
- 16. CloudxLab.com How are wesolving it? With what minimal learning can users get maximum returns? Optimized Algorithm to help users figure out their current knowledge level Knowledge Graph Individual’s skill Graph Job skill graph ?
- 17. CloudxLab.com Real / Relavent/ Practical Learning Engagement / Completion Rate Competitors Map University Courses Classroom Learning
- 18. What our Users say ...Anda 100+ such reviews
- 19. Feedback on Labfrom Users Been working with a startup from another ex-Amazonian: CloudxLab. Provides a learning environment for big data processing on a real cluster, that you can access via a web browser. Neat stuff. Frank Kane , Top Udemy Instructor Am using cloudxlab for more than an year. The main advantage of using cloudxlab, a) Get 6 node production cluster with all installed components, just getting user and password, you can start working on it. b) You have almost all the access... continue reading Sachin Peedikakkandy , Sr Engineer at DXC Technology We took CloudxLab to train our team on big data analytics and when we saw how much easier it was to use it than set up our own infrastructure. Almost like plug-n-play. IBM Blue Team , IBM India (70,000+ Users)
- 20. Feedback on Coursefrom Users
- 21. Learners from topUS Universities (And many more...)
- 22.
- 23. About Me Sandeep Giri WorkedOn Large Scale Computing Graduated from IIT Roorkee Software Engineer Love Explaining Technologies Founder Feel free to add me on linkedin
- 24. Computer Vision iseverywhere
- 27. What is MachineLearning?
- 29. What is MachineLearning?
- 30.
- 32. Question What will youdo to make this program learn any other games such as PacMan ? Option 1 - Write new rules as per the game Option 2 - Just hook it to new game and let it play for a while
- 33. Question What will youdo to make this program learn any other games such as PacMan ? Option 1 - Write new rules as per the game Option 2 - Just hook it to new game and let it play for a while
- 34. What is DeepLearning?
- 35. Nature has inspiredmany inventions
- 36. Inspiration from animalbrains to achieve intelligence
- 37. How does machinelearn?
- 38. Typical ML Process> Training AlgorithmData Model
- 39. AlgorithmData Model Live Data Prediction TypicalML Process > Predictions
- 40. Typical ML Process> Model and Algorithm AlgorithmData Model Live Data Prediction
- 41. Predicting salary basedon the number of years of experience
- 42.
- 43.
- 44.
- 45. 2000, 4000, 6000,______ Predicting Salary
- 46. 2000, 4000, 6000,8000 Predicting Salary
- 47. Years of Experience Salary 1 2000 24000 3 6000 Historical Data Let's formulate the machine learning problem.. Predicting Salary
- 48. Years of Experience Salary 1 2000 24000 3 6000 Model Historical Data Let's formulate the machine learning problem.. Training Predicting Salary
- 49. Years of Experience Salary 1 2000 24000 3 6000 Years of Experience Salary 4 ??? Model Historical Data Unknown Salary Let's formulate the machine learning problem.. Training Predicting Salary
- 50. Years of Experience Salary 1 2000 24000 3 6000 Years of Experience Salary 4 ??? Model Years of Experience Salary 4 8000 Historical Data Unknown Salary Prediction Let's formulate the machine learning problem.. Training Predicting Salary
- 51. Predicting Salary 1 23 2000 4000 6000 How would a computer do it? - plot it → Years Salary
- 52. Predicting Salary 1 23 2000 4000 6000 How would a computer do it? - plot it → Years Salary
- 53. Predicting Salary 1 23 2000 4000 6000 How would a computer do it? - plot it → Years Salary
- 54. Predicting Salary 1 23 2000 4000 6000 How would a computer do it? - plot it → Years Salary
- 55. Predicting Salary 1 23 2000 4000 6000 How would a computer do it? - plot it → Years Salary
- 56. 1 2 3 2000 4000 6000 4 Askthe plot for the salary in 4th yr! Predicting Salary → Years Salary
- 57. Predicting Salary 1 23 2000 4000 6000 8000 4 It is correct! → Years Salary
- 58. 1 2 3 2010 3980 6020 Lifeisn't perfect! You never get straight line! Predicting Salary
- 59. 2010 3980 6020 Linear Regression -Find best fitting line 1 2 3Predicting Salary
- 60. 2010 3980 6020 Line 1 Line 2 Line3 Normal Equation - Mathematically Predicting Salary 1 2 3
- 61. Line 1Try multiplelines! Whichever is closer. 2010 3980 6020 Line 2 Line 3 Predicting Salary 1 2 3
- 62.
- 63. 1 2 34 2010 3980 6020 Initial Guess Gradient Descent - A little greedy approach. Gradient Descent
- 64. 1 2 34 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Gradient Descent - find impact of slight change Gradient Descent
- 65. 1 2 34 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Gradient Descent - guess next line! Next line Gradient Descent
- 66. Gradient Descent Increase inslope is proportional to Rate of decrease of error w.r.t change in slope Gradient Descent 1 2 3 4 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Next line
- 67. Gradient Descent Increase inslope = learning rate * Rate of decrease of error wrt change in slope Some constant. Gradient Descent 1 2 3 4 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Next line
- 68. 1 2 34 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Next line Gradient Descent New Slope = Old Slope + Learning Rate * Rate of decrease of error wrt change in slope Gradient Descent
- 69. 1 2 34 2010 3980 6020 Initial Guess Try Increasing the slope a little bit Next line What if error increased due to increasing slope? Gradient Descent
- 70. Start with randomline Gradient Descent Gradient Descent > Flowchart
- 71. Increase the slopea bit & Calculate change in error Start with random line Did the error decrease? Gradient Descent Gradient Descent > Flowchart
- 72. Increase the slopea bit & Calculate change in error Start with random line Did the error decrease? Increase the slope. Yes Gradient Descent Gradient Descent > Flowchart
- 73. Increase the slopea bit & Calculate change in error Start with random line Did the error decrease? Decrease the slope. No. Error increased Increase the slope. Yes Gradient Descent Gradient Descent > Flowchart
- 74. Increase the slopea bit & Calculate change in error Start with random line Did the error decrease? Decrease the slope. Stop, you have found the best line No. Error increased Increase the slope. Yes No, Error didn't change Gradient Descent Gradient Descent > Flowchart
- 75. Increase the slopea bit & Calculate change in error Start with random line Did the error decrease? Decrease the slope. Stop, you have found the best line No. Error increased Increase the slope. Yes No, Error didn't change Next epoch / iterationNext epoch / iteration Gradient Descent Gradient Descent > Flowchart
- 76. Gradient Descent Gradient Descent> Tap Analogy
- 77. Gradient Descent Gradient Descent> Tap Analogy
- 78. Gradient Descent Gradient Descent> Tap Analogy
- 79. Gradient Descent New Position GradientDescent > Tap Analogy
- 80. Gradient Descent Begin Again! GradientDescent > Tap Analogy
- 81. Gradient Descent New Position Slightright twist Gradient Descent > Tap Analogy
- 82. What if wehad two knob? Comfortable position Gradient Descent > Tap Analogy
- 83. What if wehad two knob? Gradient Descent > Two Knobs
- 84. Gradient Descent Gradient Descent> Two Knobs
- 85. Hot Cold Good Flow Right Temperature ModelInputOutput Gradient Descent > Model Weights
- 86. What if wehad more input features? Hot Cold Good Flow Right Temperature Country Gender Climate Gradient Descent > Model Model Weights Input Output
- 87. Deep Learning -Artificial Neural Network(ANN) Computing systems inspired by the biological neural networks that constitute animal brains. Deep Learning > Artificial Neural Network (ANN)
- 88. Multiple layers ofneurons Deep Learning > Simple Neural Network (ANN) > Deep Learning Neural Network (DNN)
- 89. Multiple layers ofneurons Deep Learning > Simple Neural Network (ANN) > Deep Learning Neural Network (DNN)
- 90. Multiple layers ofneurons Deep Learning > Simple Neural Network (ANN) > Deep Learning Neural Network (DNN)
- 91. Multiple layers ofneurons Deep Learning > Simple Neural Network (ANN) > Deep Learning Neural Network (DNN)
- 92. What if wehad more input features? Hot Cold Good Flow Right Temperature Model Weights Input Output Country Gender Climate Deep Learning > Simple Neural Network (ANN)
- 93. We could improvethe model further... Hot Cold Good Flow Right Temperature ModelInput Output Country Gender Climate Deep Learning > Deep Learning Neural Network (DNN)
- 94. And further…. Deep NeuralNetwork... Hot Cold Good Flow Right Temperature Neural NetworkInput layer Output layer Country Gender Climate Deep Learning > Deep Learning Neural Network (DNN)
- 95. Training Neural Networks- Single Neuron Hot Cold Right Temperature Deep Learning > Training Neural Network
- 96. Training Neural Networks- How? Hot Cold Right Temperature Deep Learning > Training Neural Network
- 97. Training Neural Networks- How? Backpropagation - Two neuron Hot Cold Right Temperature First Knob Second Knob Deep Learning > Training Neural Network > Backpropagation
- 98. Training Neural Networks- Backpropagation Hot Cold Right Temperature 1. Initialisation First Knob Second Knob Deep Learning > Training Neural Network > Backpropagation
- 99. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? 2. Forward Pass Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 100. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? 3. Reverse Pass - Tweak Second knob. Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 101. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? 3. Reverse Pass - Tweak first knob. Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 102. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? 4. Reverse Pass - Tweak first knob. Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 103. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? 4. Reverse Pass - Tweak first knob. Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 104. Training Neural Networks- Backpropagation Hot Cold Right Temperature ?? Back to 1. Forward Pass - Take next instance Interim temperature Deep Learning > Training Neural Network > Backpropagation
- 105. Artificial Neuron -Let's get real! Deep Learning > Artificial Neuron
- 106. Artificial Neuron -Let's get real! Input Neuron Connection weight Output Deep Learning > Artificial Neuron
- 107. Artificial Neuron -Let's get real! bias Deep Learning > Artificial Neuron Input Neuron Connection weight Output
- 108. Artificial Neuron -Let's get real! Output = Input * weight + bias Deep Learning > Artificial Neuron > Weight and Bias bias Input Neuron Connection weight Output
- 109. Artificial Neuron -Let's get real! Output = Input * weight + bias Deep Learning > Artificial Neuron > Weight and Bias bias Input Neuron Connection weight Output
- 110. Artificial Neuron -Let's get real! Input1 Neuron bias Output Input2 Connection Weight 2 Connection Weight 1 Deep Learning > Artificial Neuron
- 111. Artificial Neuron -Let's get real! Output = Input1 * weight1 + input2 * weight2 + bias Deep Learning > Artificial Neuron Input1 Neuron bias Output Input2 Connection Weight 2 Connection Weight 1
- 112. Artificial Neuron -Let's get real! Input1 Neuron bias Connection weight Input2 Connection weight Activation function If value is too low, gives 0. Output Deep Learning > Artificial Neuron
- 113. Artificial Neuron -Let's get real! Input1 Neuron bias Connection weight Output Activation function If value is too low, gives 0.Input2 Connection weight Deep Learning > Artificial Neuron
- 114. Artificial Neuron -To make it like a electric circuit Input1 Neuron bias Connection weight Output Input2 Connection weight Deep Learning > Artificial Neuron
- 115. Artificial Neuron -To make it like a electric circuit Input1 Neuron bias Connection weight Output Input2 Connection weight Deep Learning > Artificial Neuron
- 116. Backpropagation with example Traininga network based on years of work experience as input and Salary as output Deep Learning > Artificial Neuron > Backpropagation
- 117. EMP Work Ex Salary emp1 4 100 emp25 130 emp3 2 30 emp4 5 140 Deep Learning > Artificial Neuron > Backpropagation Training a network based on years of work experience as input and Salary as output
- 118. First Second ? ? ? ? actual value Epoch 1 Record1 EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Deep Learning > Artificial Neuron > Backpropagation
- 119. 1.0 4 Initialize with randomweights or knobs. 50 0.1 30 actual value 100 EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 120. Two Neurons -4 knobs. Take first record... ? 4 ? ? ? EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 actual value 100 Deep Learning > Artificial Neuron > Backpropagation
- 121. 1.0 4 Do the forwardpass 50 0.1 30 54 4*1.0 + 50 EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 actual value 100 Deep Learning > Artificial Neuron > Backpropagation
- 122. 1.0 4 Do the forwardpass 50 0.1 30 actual value 10035.454 Compute d value 54*0.1 + 30 EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 123. 1.0 4 Compute the error 50 0.1 30 actual value 10035.454 Computed value { ||100 - 35.4 || EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 124. 1.0 4 50 0.2 50 actual value 10035.454 Computed value Back Pass: Tweakthe knob of the second neuron EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 125. 2.0 4 40 0.2 50 actual value 10035.454 Computed value Back Pass: Freezethe second knob, tweak the knob of the first neuron EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 126. Next Record. Repeatthe process, Tweak the weights... 2.0 5 40 0.2 50 actual value 13050 55 Computed value EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 2 Deep Learning > Artificial Neuron > Backpropagation
- 127. Next Record. Repeatthe process, Tweak the weights... 3.0 2 45 0.25 55 actual value 3051 67.75 Computed value Epoch 1 Record 3 EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Deep Learning > Artificial Neuron > Backpropagation
- 128. Next Record. Repeatthe process, Tweak the weights... 3.5 5 47 0.2 40 actual value 14064.5 65.2 Computed value EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 1 Record 4 Deep Learning > Artificial Neuron > Backpropagation
- 129. Next Epoch. Beginagain from the first record ... 4.0 4 51 0.21 41 actual value 10067 54.07 Computed value EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 2 Record 1 Deep Learning > Artificial Neuron > Backpropagation
- 130. Epoch #2 ,Second Record, and so on. 5.0 5 50 0.1 45 actual value 13075 52.5 Computed value EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 Epoch 2 Record 2 Deep Learning > Artificial Neuron > Backpropagation
- 131. Once it hasbeen trained, it is ready to do the predictions 4.75 6 42.25 0.215 43.12 ??? Predicted value EMP Work Ex Salary emp1 4 100 emp2 5 130 emp3 2 30 emp4 5 140 emp5 6 ?? Deep Learning > Artificial Neuron > Backpropagation
- 132. MNIST - classifyinga handwritten image into 10 labels. ANN or Fully connected Neural Network 0 1 2 3 4 5 6 7 8 9 Labels Deep Learning > Artificial Neuron > ANN
- 133. It would basicallycompute probabilities of various digits Training Neural Network - MNIST .001 .001 0.04 0.06 0.03 0.1 0.07 0.01 0.6 0.07 Probabilities 0 1 2 3 4 5 6 7 8 9 Labels Deep Learning > Artificial Neuron > ANN
- 134. Training Neural Network- MNIST How to train it? .001 .001 0.04 0.06 0.03 0.1 0.07 0.01 0.6 0.07 Probabilities Deep Learning > Artificial Neuron > ANN
- 135. Step 1 -Initialize the weights ANN or Fully connected Neural Network Actual 0 1 2 3 4 5 6 7 8 9 Labels 0 0 0 0 0 0 0 0 1 0 Deep Learning > Artificial Neuron > ANN
- 136. Training Neural Network- MNIST Take an instance from training data. Actual Labels 0 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 1 0 Deep Learning > Artificial Neuron > ANN
- 137. Step 2 -Forward pass ANN or Fully connected Neural Network Labels Intermediate values Actual 0 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 1 0 Deep Learning > Artificial Neuron > ANN
- 138. ANN or Fullyconnected Neural Network Step 2 - Forward pass 0 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 1 0 Actual Labels Deep Learning > Artificial Neuron > ANN
- 139. Step 2 -Forward pass Actual Labels .1 .1 0.05 0.15 0.11 0.99 0.1 0.18 0.02 0.1 Computed ANN or Fully connected Neural Network 0 0 0 0 0 0 0 0 1 0 0 1 2 3 4 5 6 7 8 9 Deep Learning > Artificial Neuron > ANN
- 140. ANN or Fullyconnected Neural Network Step 2 - Forward pass Actual Labels .1 .1 0.05 0.15 0.11 0.99 0.1 0.18 0.02 Computed 0 0 0 0 0 0 0 0 1 0 0 1 2 3 4 5 6 7 8 90.1 Deep Learning > Artificial Neuron > ANN
- 141. ANN or Fullyconnected Neural Network Step 3 - Back Pass - Second Layer Frozen Layer 0.1 Actual Labels .1 .1 0.05 0.15 0.11 0.99 0.1 0.18 0.02 Computed 0 0 0 0 0 0 0 0 1 0 0 1 2 3 4 5 6 7 8 9 Deep Learning > Artificial Neuron > ANN
- 142. Training Neural Network- MNIST .1 .1 0.05 0.15 0.11 0.99 0.1 0.18 0.02 0.1 Predicted Frozen Layer Step 2 - Forward pass Actual Labels 0 0 0 0 0 0 0 0 1 0 0 1 2 3 4 5 6 7 8 9 Deep Learning > Artificial Neuron > ANN > Training
- 143. Pick next instance TrainingNeural Network - MNIST .45 .01 0.03 0.25 0.01 0.03 0.02 0.1 0.01 0.1 Predicted Actual Labels 1 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 Deep Learning > Artificial Neuron > ANN > Training
- 144. Next Iteration orepoch Training Neural Network - MNIST .1 .1 0.05 0.15 0.11 0.99 0.1 0.18 0.02 0.1 Predicted 0 0 0 0 0 0 0 0 1 0 Actual Deep Learning > Artificial Neuron > ANN > Training
- 145. Male Female This will requirea lot of computing power Layers of neurons 10,000 pixels Output layer 100x100 3 layers each with 10000 weights and 2 last Total connections: 10000x10000 + 10000x10000 + 10000x2 ~ 200 million CNN > Fully Connected Network vs CNN
- 146. Male Female This will requirea lot of computing power Layers of neurons 10,000 pixels Output layer 100x100 CNN > Fully Connected Network vs CNN Also notice that the adjacent pixels at 0,0 and 1,1 would go far away.
- 147. CNNs solve thisproblem by using partially connected layers called Convolutional Layers CNN > Fully Connected Network vs CNN
- 148. CNN > ConvolutionalLayer ● Neurons in the first convolutional layer are connected only to pixels in their receptive fields. Convolutional Layer
- 149. CNN > ConvolutionalLayer ● Neurons in the first convolutional layer are connected only to pixels in their receptive fields. Receptive Field Convolutional Layer
- 150. CNN > ConvolutionalLayer Convolutional Layer ● Neurons in the first convolutional layer are connected only to pixels in their receptive fields. ● Each neuron in the second convolutional layer is connected only to neurons located within a small rectangle in the first layer Receptive Field
- 151. CNN > ConvolutionalLayer Convolutional Layer ● The network concentrates on low-level features in the first hidden layer Receptive Field
- 152. CNN > ConvolutionalLayer Convolutional Layer ● The network concentrates on low-level features in the first hidden layer ● Then assemble them into higher-level features in the next hidden layer and so on. Receptive Field
- 153. CNN > ConvolutionalLayer Convolutional Layer ● The network concentrates on low-level features in the first hidden layer ● Then assemble them into higher-level features in the next hidden layer and so on. ● This hierarchical structure is common in real-world images Receptive Field
- 154. CNN > ConvolutionalLayer
- 155. CNN > ConvolutionalLayer
- 156. CNN > ConvolutionalLayer Input Layer This figure shows how the layers of CNN are formed
- 157. CNN > ConvolutionalLayer Input Layer Convolutional Layer This figure shows how the layers of CNN are formed
- 158. This figure showshow the layers of CNN are formed CNN > Convolutional Layer Input Layer Convolutional Layer Receptive Field
- 159. CNN > ConvolutionalLayer This figure shows how the layers of CNN are formed Input Layer Convolutional Layer
- 160. CNN > ConvolutionalLayer 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 Vertical Filter Horizontal Filter This figure shows how the layers of CNN are formed Convolutional Layer Input Layer
- 161. CNN > PoolingLayers Input Image Shrinked Image Pooling Layers
- 162. CNN > PoolingLayers Pooling Layers ● Computes the maximum value of the receptive field - Max pool
- 163. CNN > PoolingLayers Pooling Layers ● Computes the maximum value of the receptive field - Max pool ● Computes the average of all pixels - Average pool
- 164.
- 165. The Image ClassificationChallenge: 1,000 object classes 1,431,167 images
- 166. Progress so far Russakovskyet al. IJCV 2015
- 167. Problems related toimage classification ...
- 168.
- 169. How AI worksin it? Various Computer Vision Task
- 170. How AI worksin it? Sliding Window
- 171. R-CNN < FastR-CNN < Faster R-CNN < Mask R-CNN < YOLO < YOLO v3
- 172. How AI worksin it? YOLO v3 (You Only Look Once) Fastest object detection algorithm Deep CNN Intersection over Union Bounding Box Detection
- 173. Yolo v3 -Deep CNN More at: https://pjreddie.com/media/files/papers/YOLOv3.pdf
- 174.
- 175. What? ● In aconference like this ● we want to live stream and record it. ● But the presenter gets out of focus as she/he moves ● Automating the cameraman is the only option! ● For the humanity :) ● See https://cloudxlab.com/blog/creating-ai-based-cameraman/
- 176.
- 177. How does itwork? ● Read image from the USB camera ● Using OPENCV / YOLO identify the object in the image ● Keep only the persons ● For all the persons ○ Pick the one which has the biggest bounding box ● Rotate the motors to bring ○ Centre of the bounding box to centre of the main image, ● Sleep for some time, go back to step 1
- 178. Beyond 2D ObjectDetection... Object Detection + Captioning = Dense Captioning
- 179. Generative Adversarial Network(GANs) Image Generator Detector Real Fake By Ian J. Goodfellow, Yoshua Bengio and others in 2014. Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”
- 180.
- 181.
- 182.
- 183.
- 184. Example Face Embedding Facenet Facenet Facenet Facenet [0.3,-0.1, 0.3, …… 128 numbers] [0.2, 0.1, 0.8, …… 128 numbers] [0.1, 0.2, 0.6, …… 128 numbers] [0.8, 0.9, 0.3, …… 128 numbers] Facenet [0.2, -0.1, 0.3, …… 128 numbers]
- 185. Example Face Embedding Facenet Facenet Facenet Facenet [0.3,-0.1, 0.3, …… 128 numbers] [0.2, 0.1, 0.8, …… 128 numbers] [0.1, 0.2, 0.6, …… 128 numbers] [0.8, 0.9, 0.3, …… 128 numbers] Facenet [0.2, -0.1, 0.3, …… 128 numbers]
- 186.
- 188. DejaView.AI - Itis a plug and play solution to recognize and recall your users. Few of the use cases are: Giving loyalty points to your users, letting your employees enter the premises based on the face recognition. Application of Computer Vision
- 189. Upskill Yourself -Get Certification From E&ICT Academy, IIT Roorkee ● Big Data Engineering with Hadoop and Spark ● Self-paced ● 60+ hours of learning ● Starts from $59 ● Machine Learning Specialization - Includes Python, Machine Learning and Deep Learning ● Self-paced ● 100+ hours of learning ● Starts from $99 ● Python Foundations for Machine Learning ● Self-paced ● 40+ hours of learning ● Starts from $99
- 190. Upskill Yourself ● AIfor Managers ● For Technical product managers, project managers, business heads, senior managers and team leads ● Self-paced ● 60+ hours of learning ● Starts from $99
- 191.