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  1. 1. Introduction/1. PyTorch for Deep Learning.mp4 75.3 MB
  2. 1. Introduction/2. Course Welcome and What Is Deep Learning.mp4 39.0 MB
  3. 1. Introduction/3. Join Our Online Classroom!.mp4 75.3 MB
  4. 1. Introduction/4. Exercise Meet Your Classmates + Instructor.html 3.8 KB
  5. 1. Introduction/5. Course Companion Book + Code + More.html 1.1 KB
  6. 1. Introduction/6. Machine Learning + Python Monthly Newsletters.html 870 bytes
  7. 10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp4 93.9 MB
  8. 10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp4 87.1 MB
  9. 10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 140.9 MB
  10. 10. PyTorch Paper Replicating/12. Breaking Down Equation 1.mp4 103.2 MB
  11. 10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.mp4 125.0 MB
  12. 10. PyTorch Paper Replicating/14. Breaking Down Equation 4.mp4 92.4 MB
  13. 10. PyTorch Paper Replicating/15. Breaking Down Table 1.mp4 122.1 MB
  14. 10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 160.6 MB
  15. 10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 150.2 MB
  16. 10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 130.6 MB
  17. 10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp4 142.6 MB
  18. 10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp4 23.3 MB
  19. 10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 129.1 MB
  20. 10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 89.6 MB
  21. 10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 50.4 MB
  22. 10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4 170.0 MB
  23. 10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp4 132.0 MB
  24. 10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp4 131.9 MB
  25. 10. PyTorch Paper Replicating/26. Creating the Position Embedding.mp4 109.2 MB
  26. 10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp4 134.8 MB
  27. 10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp4 144.1 MB
  28. 10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp4 111.8 MB
  29. 10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp4 110.7 MB
  30. 10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4 163.9 MB
  31. 10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp4 53.7 MB
  32. 10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp4 88.7 MB
  33. 10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp4 107.1 MB
  34. 10. PyTorch Paper Replicating/34. Transformer Encoder Overview.mp4 82.9 MB
  35. 10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 84.9 MB
  36. 10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 188.7 MB
  37. 10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 190.8 MB
  38. 10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 111.4 MB
  39. 10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 84.9 MB
  40. 10. PyTorch Paper Replicating/4. What We Are Going to Cover.mp4 87.8 MB
  41. 10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 118.3 MB
  42. 10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp4 53.5 MB
  43. 10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp4 101.2 MB
  44. 10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp4 63.4 MB
  45. 10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 164.7 MB
  46. 10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp4 57.2 MB
  47. 10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 76.3 MB
  48. 10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 40.4 MB
  49. 10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 41.8 MB
  50. 10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 37.1 MB
  51. 10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp4 99.1 MB
  52. 10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 85.5 MB
  53. 10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp4 43.8 MB
  54. 10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 89.7 MB
  55. 10. PyTorch Paper Replicating/8. Visualizing a Single Image.mp4 36.4 MB
  56. 10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp4 77.8 MB
  57. 11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 73.8 MB
  58. 11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp4 92.1 MB
  59. 11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 57.6 MB
  60. 11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp4 31.4 MB
  61. 11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 97.0 MB
  62. 11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp4 26.7 MB
  63. 11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 55.5 MB
  64. 11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 63.3 MB
  65. 11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp4 78.5 MB
  66. 11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 19.7 MB
  67. 11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 62.0 MB
  68. 11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp4 46.9 MB
  69. 11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 43.8 MB
  70. 11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.mp4 45.9 MB
  71. 11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 93.4 MB
  72. 11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4 185.8 MB
  73. 11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp4 97.6 MB
  74. 11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp4 72.5 MB
  75. 11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp4 89.6 MB
  76. 11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp4 134.7 MB
  77. 11. PyTorch Model Deployment/28. Gradio Overview and Installation.mp4 95.1 MB
  78. 11. PyTorch Model Deployment/29. Gradio Function Outline.mp4 79.9 MB
  79. 11. PyTorch Model Deployment/3. Where Is My Model Going to Go.mp4 139.8 MB
  80. 11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 95.2 MB
  81. 11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 53.3 MB
  82. 11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 135.4 MB
  83. 11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 64.8 MB
  84. 11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp4 89.5 MB
  85. 11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 39.1 MB
  86. 11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 92.4 MB
  87. 11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 71.9 MB
  88. 11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 44.8 MB
  89. 11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 137.6 MB
  90. 11. PyTorch Model Deployment/4. How Is My Model Going to Function.mp4 67.4 MB
  91. 11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp4 37.5 MB
  92. 11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 112.2 MB
  93. 11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 143.6 MB
  94. 11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 91.6 MB
  95. 11. PyTorch Model Deployment/44. Food Vision Big Project Outline.mp4 39.1 MB
  96. 11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 96.5 MB
  97. 11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp4 71.7 MB
  98. 11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 119.7 MB
  99. 11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp4 61.5 MB
  100. 11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4 184.2 MB
  101. 11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp4 65.4 MB
  102. 11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp4 52.8 MB
  103. 11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 36.6 MB
  104. 11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 66.8 MB
  105. 11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 23.9 MB
  106. 11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 104.8 MB
  107. 11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp4 39.8 MB
  108. 11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp4 162.5 MB
  109. 11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 81.8 MB
  110. 11. PyTorch Model Deployment/6. What We Are Going to Cover.mp4 40.8 MB
  111. 11. PyTorch Model Deployment/7. Getting Setup to Code.mp4 62.9 MB
  112. 11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp4 39.3 MB
  113. 11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 58.6 MB
  114. 11. PyTorch Model Deployment/Download Paid Udemy Courses For Free.url 116 bytes
  115. 11. PyTorch Model Deployment/GetFreeCourses.Co.url 116 bytes
  116. 12. Where To Go From Here/1. Thank You!.mp4 21.0 MB
  117. 2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp4 13.8 MB
  118. 2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp4 37.7 MB
  119. 2. PyTorch Fundamentals/11. Important Resources For This Course.mp4 58.3 MB
  120. 2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp4 70.0 MB
  121. 2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp4 94.0 MB
  122. 2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp4 86.4 MB
  123. 2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp4 24.6 MB
  124. 2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 32.6 MB
  125. 2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp4 81.4 MB
  126. 2. PyTorch Fundamentals/18. Getting Tensor Attributes.mp4 66.4 MB
  127. 2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp4 39.7 MB
  128. 2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 35.3 MB
  129. 2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp4 77.8 MB
  130. 2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 57.8 MB
  131. 2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 97.4 MB
  132. 2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 48.1 MB
  133. 2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp4 24.5 MB
  134. 2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp4 104.0 MB
  135. 2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp4 88.4 MB
  136. 2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp4 57.0 MB
  137. 2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp4 59.8 MB
  138. 2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 95.1 MB
  139. 2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp4 55.3 MB
  140. 2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp4 113.0 MB
  141. 2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 64.5 MB
  142. 2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 56.8 MB
  143. 2. PyTorch Fundamentals/33. Unlimited Updates.html 1.7 KB
  144. 2. PyTorch Fundamentals/4. Anatomy of Neural Networks.mp4 70.3 MB
  145. 2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp4 27.1 MB
  146. 2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp4 43.2 MB
  147. 2. PyTorch Fundamentals/7. What Is and Why PyTorch.mp4 113.6 MB
  148. 2. PyTorch Fundamentals/8. What Are Tensors.mp4 25.0 MB
  149. 2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp4 50.4 MB
  150. 2. PyTorch Fundamentals/Download Paid Udemy Courses For Free.url 116 bytes
  151. 2. PyTorch Fundamentals/GetFreeCourses.Co.url 116 bytes
  152. 3. PyTorch Workflow/1. Introduction and Where You Can Get Help.mp4 28.6 MB
  153. 3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp4 107.0 MB
  154. 3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp4 69.5 MB
  155. 3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp4 116.0 MB
  156. 3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp4 128.8 MB
  157. 3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp4 83.0 MB
  158. 3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4 177.5 MB
  159. 3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 101.7 MB
  160. 3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 135.0 MB
  161. 3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4 161.6 MB
  162. 3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp4 129.8 MB
  163. 3. PyTorch Workflow/2. Getting Setup and What We Are Covering.mp4 69.7 MB
  164. 3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp4 79.6 MB
  165. 3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 45.8 MB
  166. 3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp4 49.3 MB
  167. 3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp4 88.7 MB
  168. 3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp4 103.0 MB
  169. 3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 50.6 MB
  170. 3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 72.5 MB
  171. 3. PyTorch Workflow/27. Exercise Imposter Syndrome.mp4 39.3 MB
  172. 3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 49.3 MB
  173. 3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 68.6 MB
  174. 3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp4 65.2 MB
  175. 3. PyTorch Workflow/5. Building a function to Visualize Our Data.mp4 61.9 MB
  176. 3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp4 130.1 MB
  177. 3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 62.2 MB
  178. 3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 74.4 MB
  179. 3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp4 102.7 MB
  180. 4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp4 84.6 MB
  181. 4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 161.1 MB
  182. 4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 134.5 MB
  183. 4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 126.8 MB
  184. 4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 150.0 MB
  185. 4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp4 80.9 MB
  186. 4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp4 68.8 MB
  187. 4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 118.6 MB
  188. 4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 61.4 MB
  189. 4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp4 71.7 MB
  190. 4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp4 50.8 MB
  191. 4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp4 50.0 MB
  192. 4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 96.5 MB
  193. 4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp4 92.6 MB
  194. 4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 150.6 MB
  195. 4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 53.0 MB
  196. 4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 80.7 MB
  197. 4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 97.5 MB
  198. 4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp4 107.4 MB
  199. 4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 65.1 MB
  200. 4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 97.0 MB
  201. 4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 150.1 MB
  202. 4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp4 67.0 MB
  203. 4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 77.0 MB
  204. 4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp4 97.5 MB
  205. 4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp4 41.5 MB
  206. 4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp4 91.5 MB
  207. 4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 81.1 MB
  208. 4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 31.9 MB
  209. 4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp4 86.9 MB
  210. 4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp4 91.3 MB
  211. 4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 123.2 MB
  212. 5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 113.7 MB
  213. 5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp4 110.5 MB
  214. 5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp4 45.6 MB
  215. 5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp4 157.6 MB
  216. 5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp4 106.8 MB
  217. 5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 44.3 MB
  218. 5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp4 86.4 MB
  219. 5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.mp4 31.3 MB
  220. 5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp4 70.9 MB
  221. 5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp4 50.9 MB
  222. 5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 108.4 MB
  223. 5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp4 85.0 MB
  224. 5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp4 41.3 MB
  225. 5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp4 94.6 MB
  226. 5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 208.3 MB
  227. 5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp4 162.7 MB
  228. 5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 158.1 MB
  229. 5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 174.8 MB
  230. 5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp4 27.9 MB
  231. 5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 76.8 MB
  232. 5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.mp4 61.8 MB
  233. 5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 83.7 MB
  234. 5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).mp4 55.4 MB
  235. 5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 63.5 MB
  236. 5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 160.8 MB
  237. 5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 67.0 MB
  238. 5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.mp4 98.2 MB
  239. 5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 81.9 MB
  240. 5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 89.2 MB
  241. 5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 154.0 MB
  242. 5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.mp4 68.1 MB
  243. 5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.mp4 60.2 MB
  244. 5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.mp4 100.2 MB
  245. 5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 136.9 MB
  246. 6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.mp4 92.6 MB
  247. 6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.mp4 76.7 MB
  248. 6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 84.3 MB
  249. 6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 74.7 MB
  250. 6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.mp4 79.1 MB
  251. 6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 176.3 MB
  252. 6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 69.5 MB
  253. 6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 131.2 MB
  254. 6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.mp4 80.6 MB
  255. 6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 166.4 MB
  256. 6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 77.9 MB
  257. 6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 49.0 MB
  258. 6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 117.2 MB
  259. 6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 96.5 MB
  260. 6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 65.0 MB
  261. 6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.mp4 106.2 MB
  262. 6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.mp4 103.5 MB
  263. 6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.mp4 89.3 MB
  264. 6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.mp4 89.4 MB
  265. 6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 131.8 MB
  266. 6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 98.8 MB
  267. 6. PyTorch Custom Datasets/29. Constructing and Training Model 1.mp4 60.7 MB
  268. 6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 151.0 MB
  269. 6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.mp4 31.7 MB
  270. 6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 89.3 MB
  271. 6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 51.7 MB
  272. 6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 68.0 MB
  273. 6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 127.1 MB
  274. 6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 36.1 MB
  275. 6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 113.0 MB
  276. 6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 73.3 MB
  277. 6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 87.6 MB
  278. 6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 115.3 MB
  279. 6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 51.9 MB
  280. 6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 81.7 MB
  281. 6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 127.6 MB
  282. 6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 98.2 MB
  283. 6. PyTorch Custom Datasets/Download Paid Udemy Courses For Free.url 116 bytes
  284. 6. PyTorch Custom Datasets/GetFreeCourses.Co.url 116 bytes
  285. 7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.mp4 100.1 MB
  286. 7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 80.7 MB
  287. 7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.mp4 104.9 MB
  288. 7. PyTorch Going Modular/3. Downloading a Dataset.mp4 67.6 MB
  289. 7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.mp4 156.8 MB
  290. 7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 135.1 MB
  291. 7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.mp4 115.1 MB
  292. 7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.mp4 80.0 MB
  293. 7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.mp4 75.8 MB
  294. 7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.mp4 165.5 MB
  295. 8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.mp4 97.3 MB
  296. 8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.mp4 57.0 MB
  297. 8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.mp4 76.0 MB
  298. 8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 160.7 MB
  299. 8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.mp4 74.8 MB
  300. 8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.mp4 58.9 MB
  301. 8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.mp4 66.7 MB
  302. 8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.mp4 101.7 MB
  303. 8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.mp4 78.1 MB
  304. 8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.mp4 67.8 MB
  305. 8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.mp4 44.4 MB
  306. 8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 55.9 MB
  307. 8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.mp4 82.4 MB
  308. 8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.mp4 83.8 MB
  309. 8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 72.2 MB
  310. 8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 141.5 MB
  311. 8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 139.7 MB
  312. 8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.mp4 128.8 MB
  313. 8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.mp4 113.1 MB
  314. 9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.mp4 61.9 MB
  315. 9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.mp4 80.1 MB
  316. 9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 66.5 MB
  317. 9. PyTorch Experiment Tracking/12. What Experiments Should You Try.mp4 46.9 MB
  318. 9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.mp4 48.3 MB
  319. 9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.mp4 66.4 MB
  320. 9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 78.1 MB
  321. 9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.mp4 159.2 MB
  322. 9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 127.6 MB
  323. 9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 45.7 MB
  324. 9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.mp4 140.3 MB
  325. 9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 93.4 MB
  326. 9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 99.2 MB
  327. 9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 39.7 MB
  328. 9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.mp4 43.6 MB
  329. 9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.mp4 95.2 MB
  330. 9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 92.7 MB
  331. 9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 82.0 MB
  332. 9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.mp4 113.2 MB
  333. 9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 150.3 MB
  334. 9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.mp4 41.8 MB
  335. 9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.mp4 116.3 MB
  336. Download Paid Udemy Courses For Free.url 116 bytes
  337. GetFreeCourses.Co.url 116 bytes

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