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Премиум
  • Урок 1. 00:03:34
    PyTorch for Deep Learning
  • Урок 2. 00:05:54
    Course Welcome and What Is Deep Learning
  • Урок 3. 00:03:34
    Why Use Machine Learning or Deep Learning
  • Урок 4. 00:05:40
    The Number 1 Rule of Machine Learning and What Is Deep Learning Good For
  • Урок 5. 00:06:07
    Machine Learning vs. Deep Learning
  • Урок 6. 00:09:22
    Anatomy of Neural Networks
  • Урок 7. 00:04:31
    Different Types of Learning Paradigms
  • Урок 8. 00:06:22
    What Can Deep Learning Be Used For
  • Урок 9. 00:10:13
    What Is and Why PyTorch
  • Урок 10. 00:04:16
    What Are Tensors
  • Урок 11. 00:06:06
    What We Are Going To Cover With PyTorch
  • Урок 12. 00:05:10
    How To and How Not To Approach This Course
  • Урок 13. 00:05:22
    Important Resources For This Course
  • Урок 14. 00:07:40
    Getting Setup to Write PyTorch Code
  • Урок 15. 00:13:26
    Introduction to PyTorch Tensors
  • Урок 16. 00:09:59
    Creating Random Tensors in PyTorch
  • Урок 17. 00:03:09
    Creating Tensors With Zeros and Ones in PyTorch
  • Урок 18. 00:05:18
    Creating a Tensor Range and Tensors Like Other Tensors
  • Урок 19. 00:09:25
    Dealing With Tensor Data Types
  • Урок 20. 00:08:23
    Getting Tensor Attributes
  • Урок 21. 00:06:00
    Manipulating Tensors (Tensor Operations)
  • Урок 22. 00:09:35
    Matrix Multiplication (Part 1)
  • Урок 23. 00:07:52
    Matrix Multiplication (Part 2): The Two Main Rules of Matrix Multiplication
  • Урок 24. 00:12:58
    Matrix Multiplication (Part 3): Dealing With Tensor Shape Errors
  • Урок 25. 00:06:10
    Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation)
  • Урок 26. 00:03:17
    Finding The Positional Min and Max of Tensors
  • Урок 27. 00:13:41
    Reshaping, Viewing and Stacking Tensors
  • Урок 28. 00:11:56
    Squeezing, Unsqueezing and Permuting Tensors
  • Урок 29. 00:09:32
    Selecting Data From Tensors (Indexing)
  • Урок 30. 00:09:09
    PyTorch Tensors and NumPy
  • Урок 31. 00:10:47
    PyTorch Reproducibility (Taking the Random Out of Random)
  • Урок 32. 00:11:51
    Different Ways of Accessing a GPU in PyTorch
  • Урок 33. 00:07:44
    Setting up Device Agnostic Code and Putting Tensors On and Off the GPU
  • Урок 34. 00:04:50
    PyTorch Fundamentals: Exercises and Extra-Curriculum
  • Урок 35. 00:02:46
    Introduction and Where You Can Get Help
  • Урок 36. 00:07:15
    Getting Setup and What We Are Covering
  • Урок 37. 00:09:42
    Creating a Simple Dataset Using the Linear Regression Formula
  • Урок 38. 00:08:21
    Splitting Our Data Into Training and Test Sets
  • Урок 39. 00:07:46
    Building a function to Visualize Our Data
  • Урок 40. 00:14:10
    Creating Our First PyTorch Model for Linear Regression
  • Урок 41. 00:06:11
    Breaking Down What's Happening in Our PyTorch Linear regression Model
  • Урок 42. 00:06:27
    Discussing Some of the Most Important PyTorch Model Building Classes
  • Урок 43. 00:09:51
    Checking Out the Internals of Our PyTorch Model
  • Урок 44. 00:11:13
    Making Predictions With Our Random Model Using Inference Mode
  • Урок 45. 00:08:15
    Training a Model Intuition (The Things We Need)
  • Урок 46. 00:12:52
    Setting Up an Optimizer and a Loss Function
  • Урок 47. 00:13:54
    PyTorch Training Loop Steps and Intuition
  • Урок 48. 00:08:47
    Writing Code for a PyTorch Training Loop
  • Урок 49. 00:14:58
    Reviewing the Steps in a Training Loop Step by Step
  • Урок 50. 00:09:26
    Running Our Training Loop Epoch by Epoch and Seeing What Happens
  • Урок 51. 00:11:38
    Writing Testing Loop Code and Discussing What's Happening Step by Step
  • Урок 52. 00:14:43
    Reviewing What Happens in a Testing Loop Step by Step
  • Урок 53. 00:13:46
    Writing Code to Save a PyTorch Model
  • Урок 54. 00:08:45
    Writing Code to Load a PyTorch Model
  • Урок 55. 00:06:03
    Setting Up to Practice Everything We Have Done Using Device-Agnostic Code
  • Урок 56. 00:06:09
    Putting Everything Together (Part 1): Data
  • Урок 57. 00:10:08
    Putting Everything Together (Part 2): Building a Model
  • Урок 58. 00:12:41
    Putting Everything Together (Part 3): Training a Model
  • Урок 59. 00:05:18
    Putting Everything Together (Part 4): Making Predictions With a Trained Model
  • Урок 60. 00:09:11
    Putting Everything Together (Part 5): Saving and Loading a Trained Model
  • Урок 61. 00:02:57
    Exercise: Imposter Syndrome
  • Урок 62. 00:03:58
    PyTorch Workflow: Exercises and Extra-Curriculum
  • Урок 63. 00:09:42
    Introduction to Machine Learning Classification With PyTorch
  • Урок 64. 00:09:08
    Classification Problem Example: Input and Output Shapes
  • Урок 65. 00:06:32
    Typical Architecture of a Classification Neural Network (Overview)
  • Урок 66. 00:12:19
    Making a Toy Classification Dataset
  • Урок 67. 00:11:56
    Turning Our Data into Tensors and Making a Training and Test Split
  • Урок 68. 00:04:20
    Laying Out Steps for Modelling and Setting Up Device-Agnostic Code
  • Урок 69. 00:10:58
    Coding a Small Neural Network to Handle Our Classification Data
  • Урок 70. 00:06:58
    Making Our Neural Network Visual
  • Урок 71. 00:13:18
    Recreating and Exploring the Insides of Our Model Using nn.Sequential
  • Урок 72. 00:14:51
    Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network
  • Урок 73. 00:16:08
    Going from Model Logits to Prediction Probabilities to Prediction Labels
  • Урок 74. 00:15:27
    Coding a Training and Testing Optimization Loop for Our Classification Model
  • Урок 75. 00:14:14
    Writing Code to Download a Helper Function to Visualize Our Models Predictions
  • Урок 76. 00:08:03
    Discussing Options to Improve a Model
  • Урок 77. 00:09:07
    Creating a New Model with More Layers and Hidden Units
  • Урок 78. 00:12:46
    Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better
  • Урок 79. 00:08:08
    Creating a Straight Line Dataset to See if Our Model is Learning Anything
  • Урок 80. 00:10:02
    Building and Training a Model to Fit on Straight Line Data
  • Урок 81. 00:05:24
    Evaluating Our Models Predictions on Straight Line Data
  • Урок 82. 00:10:01
    Introducing the Missing Piece for Our Classification Model Non-Linearity
  • Урок 83. 00:10:26
    Building Our First Neural Network with Non-Linearity
  • Урок 84. 00:15:13
    Writing Training and Testing Code for Our First Non-Linear Model
  • Урок 85. 00:05:48
    Making Predictions with and Evaluating Our First Non-Linear Model
  • Урок 86. 00:09:35
    Replicating Non-Linear Activation Functions with Pure PyTorch
  • Урок 87. 00:11:25
    Putting It All Together (Part 1): Building a Multiclass Dataset
  • Урок 88. 00:12:28
    Creating a Multi-Class Classification Model with PyTorch
  • Урок 89. 00:06:41
    Setting Up a Loss Function and Optimizer for Our Multi-Class Model
  • Урок 90. 00:11:03
    Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model
  • Урок 91. 00:16:18
    Training a Multi-Class Classification Model and Troubleshooting Code on the Fly
  • Урок 92. 00:08:00
    Making Predictions with and Evaluating Our Multi-Class Classification Model
  • Урок 93. 00:09:18
    Discussing a Few More Classification Metrics
  • Урок 94. 00:02:59
    PyTorch Classification: Exercises and Extra-Curriculum
  • Урок 95. 00:11:48
    What Is a Computer Vision Problem and What We Are Going to Cover
  • Урок 96. 00:10:09
    Computer Vision Input and Output Shapes
  • Урок 97. 00:05:03
    What Is a Convolutional Neural Network (CNN)
  • Урок 98. 00:09:20
    Discussing and Importing the Base Computer Vision Libraries in PyTorch
  • Урок 99. 00:14:31
    Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes
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    Visualizing Random Samples of Data
  • Урок 101. 00:07:18
    DataLoader Overview Understanding Mini-Batch
  • Урок 102. 00:12:24
    Turning Our Datasets Into DataLoaders
  • Урок 103. 00:14:39
    Model 0: Creating a Baseline Model with Two Linear Layers
  • Урок 104. 00:10:30
    Creating a Loss Function: an Optimizer for Model 0
  • Урок 105. 00:05:35
    Creating a Function to Time Our Modelling Code
  • Урок 106. 00:21:26
    Writing Training and Testing Loops for Our Batched Data
  • Урок 107. 00:12:59
    Writing an Evaluation Function to Get Our Models Results
  • Урок 108. 00:03:47
    Setup Device-Agnostic Code for Running Experiments on the GPU
  • Урок 109. 00:09:04
    Model 1: Creating a Model with Non-Linear Functions
  • Урок 110. 00:03:05
    Mode 1: Creating a Loss Function and Optimizer
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    Turing Our Training Loop into a Function
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    Turing Our Testing Loop into a Function
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    Training and Testing Model 1 with Our Training and Testing Functions
  • Урок 114. 00:04:09
    Getting a Results Dictionary for Model 1
  • Урок 115. 00:08:25
    Model 2: Convolutional Neural Networks High Level Overview
  • Урок 116. 00:19:49
    Model 2: Coding Our First Convolutional Neural Network with PyTorch
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    Model 2: Breaking Down Conv2D Step by Step
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    Model 2: Breaking Down MaxPool2D Step by Step
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    Mode 2: Using a Trick to Find the Input and Output Shapes of Each of Our Layers
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    Model 2: Setting Up a Loss Function and Optimizer
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    Model 2: Training Our First CNN and Evaluating Its Results
  • Урок 122. 00:07:24
    Comparing the Results of Our Modelling Experiments
  • Урок 123. 00:11:40
    Making Predictions on Random Test Samples with the Best Trained Model
  • Урок 124. 00:08:11
    Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them
  • Урок 125. 00:15:21
    Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix
  • Урок 126. 00:06:55
    Evaluating Our Best Models Predictions with a Confusion Matrix
  • Урок 127. 00:11:28
    Saving and Loading Our Best Performing Model
  • Урок 128. 00:06:02
    Recapping What We Have Covered Plus Exercises and Extra-Curriculum
  • Урок 129. 00:09:54
    What Is a Custom Dataset and What We Are Going to Cover
  • Урок 130. 00:05:55
    Importing PyTorch and Setting Up Device-Agnostic Code
  • Урок 131. 00:14:05
    Downloading a Custom Dataset of Pizza, Steak and Sushi Images
  • Урок 132. 00:08:42
    Becoming One With the Data (Part 1): Exploring the Data Format
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    Becoming One With the Data (Part 2): Visualizing a Random Image
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    Becoming One With the Data (Part 3): Visualizing a Random Image with Matplotlib
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    Transforming Data (Part 1): Turning Images Into Tensors
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    Transforming Data (Part 2): Visualizing Transformed Images
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    Loading All of Our Images and Turning Them Into Tensors With ImageFolder
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    Visualizing a Loaded Image From the Train Dataset
  • Урок 139. 00:09:04
    Turning Our Image Datasets into PyTorch DataLoaders
  • Урок 140. 00:08:01
    Creating a Custom Dataset Class in PyTorch High Level Overview
  • Урок 141. 00:09:07
    Creating a Helper Function to Get Class Names From a Directory
  • Урок 142. 00:17:47
    Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images
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    Compare Our Custom Dataset Class to the Original ImageFolder Class
  • Урок 144. 00:14:19
    Writing a Helper Function to Visualize Random Images from Our Custom Dataset
  • Урок 145. 00:07:00
    Turning Our Custom Datasets Into DataLoaders
  • Урок 146. 00:14:24
    Exploring State of the Art Data Augmentation With Torchvision Transforms
  • Урок 147. 00:08:16
    Building a Baseline Model (Part 1): Loading and Transforming Data
  • Урок 148. 00:11:25
    Building a Baseline Model (Part 2): Replicating Tiny VGG from Scratch
  • Урок 149. 00:08:10
    Building a Baseline Model (Part 3): Doing a Forward Pass to Test Our Model Shapes
  • Урок 150. 00:06:39
    Using the Torchinfo Package to Get a Summary of Our Model
  • Урок 151. 00:13:04
    Creating Training and Testing loop Functions
  • Урок 152. 00:10:15
    Creating a Train Function to Train and Evaluate Our Models
  • Урок 153. 00:09:54
    Training and Evaluating Model 0 With Our Training Functions
  • Урок 154. 00:09:03
    Plotting the Loss Curves of Model 0
  • Урок 155. 00:14:14
    Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each
  • Урок 156. 00:11:04
    Creating Augmented Training Datasets and DataLoaders for Model 1
  • Урок 157. 00:07:11
    Constructing and Training Model 1
  • Урок 158. 00:03:23
    Plotting the Loss Curves of Model 1
  • Урок 159. 00:10:56
    Plotting the Loss Curves of All of Our Models Against Each Other
  • Урок 160. 00:05:33
    Predicting on Custom Data (Part 1): Downloading an Image
  • Урок 161. 00:07:01
    Predicting on Custom Data (Part2): Loading In a Custom Image With PyTorch
  • Урок 162. 00:14:08
    Predicting on Custom Data (Part 3): Getting Our Custom Image Into the Right Format
  • Урок 163. 00:04:25
    Predicting on Custom Data (Part 4): Turning Our Models Raw Outputs Into Prediction Labels
  • Урок 164. 00:12:48
    Predicting on Custom Data (Part 5): Putting It All Together
  • Урок 165. 00:06:05
    Summary of What We Have Covered Plus Exercises and Extra-Curriculum
  • Урок 166. 00:11:35
    What Is Going Modular and What We Are Going to Cover
  • Урок 167. 00:07:41
    Going Modular Notebook (Part 1): Running It End to End
  • Урок 168. 00:04:51
    Downloading a Dataset
  • Урок 169. 00:13:51
    Writing the Outline for Our First Python Script to Setup the Data
  • Урок 170. 00:10:36
    Creating a Python Script to Create Our PyTorch DataLoaders
  • Урок 171. 00:09:19
    Turning Our Model Building Code into a Python Script
  • Урок 172. 00:06:17
    Turning Our Model Training Code into a Python Script
  • Урок 173. 00:06:08
    Turning Our Utility Function to Save a Model into a Python Script
  • Урок 174. 00:15:47
    Creating a Training Script to Train Our Model in One Line of Code
  • Урок 175. 00:06:00
    Going Modular: Summary, Exercises and Extra-Curriculum
  • Урок 176. 00:10:06
    Introduction: What is Transfer Learning and Why Use It
  • Урок 177. 00:05:13
    Where Can You Find Pretrained Models and What We Are Going to Cover
  • Урок 178. 00:08:06
    Installing the Latest Versions of Torch and Torchvision
  • Урок 179. 00:06:42
    Downloading Our Previously Written Code from Going Modular
  • Урок 180. 00:08:01
    Downloading Pizza, Steak, Sushi Image Data from Github
  • Урок 181. 00:14:41
    Turning Our Data into DataLoaders with Manually Created Transforms
  • Урок 182. 00:13:07
    Turning Our Data into DataLoaders with Automatic Created Transforms
  • Урок 183. 00:12:16
    Which Pretrained Model Should You Use
  • Урок 184. 00:10:57
    Setting Up a Pretrained Model with Torchvision
  • Урок 185. 00:07:12
    Different Kinds of Transfer Learning
  • Урок 186. 00:06:50
    Getting a Summary of the Different Layers of Our Model
  • Урок 187. 00:13:27
    Freezing the Base Layers of Our Model and Updating the Classifier Head
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    Training Our First Transfer Learning Feature Extractor Model
  • Урок 189. 00:06:27
    Plotting the Loss Curves of Our Transfer Learning Model
  • Урок 190. 00:07:58
    Outlining the Steps to Make Predictions on the Test Images
  • Урок 191. 00:10:01
    Creating a Function Predict On and Plot Images
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    Making and Plotting Predictions on Test Images
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    Making a Prediction on a Custom Image
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    Main Takeaways, Exercises and Extra Curriculum
  • Урок 195. 00:07:07
    What Is Experiment Tracking and Why Track Experiments
  • Урок 196. 00:08:14
    Getting Setup by Importing Torch Libraries and Going Modular Code
  • Урок 197. 00:10:24
    Creating a Function to Download Data
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    Turning Our Data into DataLoaders Using Manual Transforms
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    Turning Our Data into DataLoaders Using Automatic Transforms
  • Урок 200. 00:10:29
    Preparing a Pretrained Model for Our Own Problem
  • Урок 201. 00:13:36
    Setting Up a Way to Track a Single Model Experiment with TensorBoard
  • Урок 202. 00:04:39
    Training a Single Model and Saving the Results to TensorBoard
  • Урок 203. 00:10:18
    Exploring Our Single Models Results with TensorBoard
  • Урок 204. 00:10:45
    Creating a Function to Create SummaryWriter Instances
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    Adapting Our Train Function to Be Able to Track Multiple Experiments
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    What Experiments Should You Try
  • Урок 207. 00:06:02
    Discussing the Experiments We Are Going to Try
  • Урок 208. 00:06:32
    Downloading Datasets for Our Modelling Experiments
  • Урок 209. 00:08:29
    Turning Our Datasets into DataLoaders Ready for Experimentation
  • Урок 210. 00:15:55
    Creating Functions to Prepare Our Feature Extractor Models
  • Урок 211. 00:14:28
    Coding Out the Steps to Run a Series of Modelling Experiments
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    Running Eight Different Modelling Experiments in 5 Minutes
  • Урок 213. 00:13:39
    Viewing Our Modelling Experiments in TensorBoard
  • Урок 214. 00:10:33
    Loading In the Best Model and Making Predictions on Random Images from the Test Set
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    Making a Prediction on Our Own Custom Image with the Best Model
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    Main Takeaways, Exercises and Extra Curriculum
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    What Is a Machine Learning Research Paper?
  • Урок 218. 00:03:14
    Why Replicate a Machine Learning Research Paper?
  • Урок 219. 00:08:19
    Where Can You Find Machine Learning Research Papers and Code?
  • Урок 220. 00:08:22
    What We Are Going to Cover
  • Урок 221. 00:08:22
    Getting Setup for Coding in Google Colab
  • Урок 222. 00:04:03
    Downloading Data for Food Vision Mini
  • Урок 223. 00:09:48
    Turning Our Food Vision Mini Images into PyTorch DataLoaders
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    Visualizing a Single Image
  • Урок 225. 00:09:54
    Replicating a Vision Transformer - High Level Overview
  • Урок 226. 00:11:13
    Breaking Down Figure 1 of the ViT Paper
  • Урок 227. 00:10:56
    Breaking Down the Four Equations Overview and a Trick for Reading Papers
  • Урок 228. 00:08:15
    Breaking Down Equation 1
  • Урок 229. 00:10:04
    Breaking Down Equations 2 and 3
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    Breaking Down Equation 4
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    Breaking Down Table 1
  • Урок 232. 00:15:42
    Calculating the Input and Output Shape of the Embedding Layer by Hand
  • Урок 233. 00:15:04
    Turning a Single Image into Patches (Part 1: Patching the Top Row)
  • Урок 234. 00:12:34
    Turning a Single Image into Patches (Part 2: Patching the Entire Image)
  • Урок 235. 00:13:34
    Creating Patch Embeddings with a Convolutional Layer
  • Урок 236. 00:12:55
    Exploring the Outputs of Our Convolutional Patch Embedding Layer
  • Урок 237. 00:10:00
    Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings
  • Урок 238. 00:05:04
    Visualizing a Single Sequence Vector of Patch Embeddings
  • Урок 239. 00:17:02
    Creating the Patch Embedding Layer with PyTorch
  • Урок 240. 00:13:25
    Creating the Class Token Embedding
  • Урок 241. 00:13:25
    Creating the Class Token Embedding - Less Birds
  • Урок 242. 00:11:26
    Creating the Position Embedding
  • Урок 243. 00:13:26
    Equation 1: Putting it All Together
  • Урок 244. 00:14:31
    Equation 2: Multihead Attention Overview
  • Урок 245. 00:09:04
    Equation 2: Layernorm Overview
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    Turning Equation 2 into Code
  • Урок 247. 00:05:41
    Checking the Inputs and Outputs of Equation
  • Урок 248. 00:09:12
    Equation 3: Replication Overview
  • Урок 249. 00:11:26
    Turning Equation 3 into Code
  • Урок 250. 00:08:51
    Transformer Encoder Overview
  • Урок 251. 00:09:17
    Combining Equation 2 and 3 to Create the Transformer Encoder
  • Урок 252. 00:15:55
    Creating a Transformer Encoder Layer with In-Built PyTorch Layer
  • Урок 253. 00:18:20
    Bringing Our Own Vision Transformer to Life - Part 1: Gathering the Pieces of the Puzzle
  • Урок 254. 00:10:42
    Bringing Our Own Vision Transformer to Life - Part 2: Putting Together the Forward Method
  • Урок 255. 00:07:14
    Getting a Visual Summary of Our Custom Vision Transformer
  • Урок 256. 00:11:27
    Creating a Loss Function and Optimizer from the ViT Paper
  • Урок 257. 00:04:30
    Training our Custom ViT on Food Vision Mini
  • Урок 258. 00:09:09
    Discussing what Our Training Setup Is Missing
  • Урок 259. 00:06:14
    Plotting a Loss Curve for Our ViT Model
  • Урок 260. 00:14:38
    Getting a Pretrained Vision Transformer from Torchvision and Setting it Up
  • Урок 261. 00:05:54
    Preparing Data to Be Used with a Pretrained ViT
  • Урок 262. 00:07:16
    Training a Pretrained ViT Feature Extractor Model for Food Vision Mini
  • Урок 263. 00:05:14
    Saving Our Pretrained ViT Model to File and Inspecting Its Size
  • Урок 264. 00:03:47
    Discussing the Trade-Offs Between Using a Larger Model for Deployments
  • Урок 265. 00:03:31
    Making Predictions on a Custom Image with Our Pretrained ViT
  • Урок 266. 00:06:51
    PyTorch Paper Replicating: Main Takeaways, Exercises and Extra-Curriculum
  • Урок 267. 00:09:36
    What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model
  • Урок 268. 00:07:14
    Three Questions to Ask for Machine Learning Model Deployment
  • Урок 269. 00:13:35
    Where Is My Model Going to Go?
  • Урок 270. 00:08:00
    How Is My Model Going to Function?
  • Урок 271. 00:05:50
    Some Tools and Places to Deploy Machine Learning Models
  • Урок 272. 00:04:02
    What We Are Going to Cover
  • Урок 273. 00:06:16
    Getting Setup to Code
  • Урок 274. 00:03:24
    Downloading a Dataset for Food Vision Mini
  • Урок 275. 00:08:00
    Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments
  • Урок 276. 00:09:46
    Creating an EffNetB2 Feature Extractor Model
  • Урок 277. 00:06:30
    Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms
  • Урок 278. 00:03:32
    Creating DataLoaders for EffNetB2
  • Урок 279. 00:09:16
    Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves
  • Урок 280. 00:03:25
    Saving Our EffNetB2 Model to File
  • Урок 281. 00:05:52
    Getting the Size of Our EffNetB2 Model in Megabytes
  • Урок 282. 00:06:35
    Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model
  • Урок 283. 00:07:52
    Creating a Vision Transformer Feature Extractor Model
  • Урок 284. 00:02:31
    Creating DataLoaders for Our ViT Feature Extractor Model
  • Урок 285. 00:06:20
    Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves
  • Урок 286. 00:05:09
    Saving Our ViT Feature Extractor and Inspecting Its Size
  • Урок 287. 00:05:52
    Collecting Stats About Our ViT Feature Extractor
  • Урок 288. 00:11:16
    Outlining the Steps for Making and Timing Predictions for Our Models
  • Урок 289. 00:16:21
    Creating a Function to Make and Time Predictions with Our Models
  • Урок 290. 00:10:44
    Making and Timing Predictions with EffNetB2
  • Урок 291. 00:07:35
    Making and Timing Predictions with ViT
  • Урок 292. 00:11:32
    Comparing EffNetB2 and ViT Model Statistics
  • Урок 293. 00:15:55
    Visualizing the Performance vs Speed Trade-off
  • Урок 294. 00:08:40
    Gradio Overview and Installation
  • Урок 295. 00:08:50
    Gradio Function Outline
  • Урок 296. 00:09:52
    Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs
  • Урок 297. 00:05:27
    Creating a List of Examples to Pass to Our Gradio Demo
  • Урок 298. 00:12:13
    Bringing Food Vision Mini to Life in a Live Web Application
  • Урок 299. 00:06:27
    Getting Ready to Deploy Our App Hugging Face Spaces Overview
  • Урок 300. 00:08:12
    Outlining the File Structure of Our Deployed App
  • Урок 301. 00:04:12
    Creating a Food Vision Mini Demo Directory to House Our App Files
  • Урок 302. 00:09:14
    Creating an Examples Directory with Example Food Vision Mini Images
  • Урок 303. 00:07:43
    Writing Code to Move Our Saved EffNetB2 Model File
  • Урок 304. 00:04:02
    Turning Our EffNetB2 Model Creation Function Into a Python Script
  • Урок 305. 00:13:28
    Turning Our Food Vision Mini Demo App Into a Python Script
  • Урок 306. 00:04:12
    Creating a Requirements File for Our Food Vision Mini App
  • Урок 307. 00:11:31
    Downloading Our Food Vision Mini App Files from Google Colab
  • Урок 308. 00:13:37
    Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically
  • Урок 309. 00:07:45
    Running Food Vision Mini on Hugging Face Spaces and Trying it Out
  • Урок 310. 00:04:18
    Food Vision Big Project Outline
  • Урок 311. 00:09:39
    Preparing an EffNetB2 Feature Extractor Model for Food Vision Big
  • Урок 312. 00:07:46
    Downloading the Food 101 Dataset
  • Урок 313. 00:13:37
    Creating a Function to Split Our Food 101 Dataset into Smaller Portions
  • Урок 314. 00:07:24
    Turning Our Food 101 Datasets into DataLoaders
  • Урок 315. 00:20:16
    Training Food Vision Big: Our Biggest Model Yet!
  • Урок 316. 00:05:49
    Outlining the File Structure for Our Food Vision Big
  • Урок 317. 00:03:34
    Downloading an Example Image and Moving Our Food Vision Big Model File
  • Урок 318. 00:06:57
    Saving Food 101 Class Names to a Text File and Reading them Back In
  • Урок 319. 00:02:21
    Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script
  • Урок 320. 00:10:42
    Creating an App Script for Our Food Vision Big Model Gradio Demo
  • Урок 321. 00:03:46
    Zipping and Downloading Our Food Vision Big App Files
  • Урок 322. 00:13:35
    Deploying Food Vision Big to Hugging Face Spaces
  • Урок 323. 00:06:14
    PyTorch Mode Deployment: Main Takeaways, Extra-Curriculum and Exercises
  • Урок 324. 00:01:18
    Thank You!