FreeCourseSite com Udemy - PyTorch for Deep Learning in 2023 Zero to Mastery

1. 1. Introduction/1. PyTorch for Deep Learning.mp475.35MB
2. 1. Introduction/2. Course Welcome and What Is Deep Learning.mp438.99MB
3. 1. Introduction/3. Join Our Online Classroom!.mp475.34MB
4. 1. Introduction/6. ZTM Resources.mp444.57MB
5. 10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp493.94MB
6. 10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp487.11MB
7. 10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4140.92MB
8. 10. PyTorch Paper Replicating/12. Breaking Down Equation 1.mp4103.21MB
9. 10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.mp4125.03MB
10. 10. PyTorch Paper Replicating/14. Breaking Down Equation 4.mp492.43MB
11. 10. PyTorch Paper Replicating/15. Breaking Down Table 1.mp4122.09MB
12. 10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4160.59MB
13. 10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4150.15MB
14. 10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4130.65MB
15. 10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp4142.62MB
16. 10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp423.26MB
17. 10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4129.06MB
18. 10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp489.61MB
19. 10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp450.37MB
20. 10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4170.03MB
21. 10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp4131.98MB
22. 10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp4131.91MB
23. 10. PyTorch Paper Replicating/26. Creating the Position Embedding.mp4109.18MB
24. 10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp4134.81MB
25. 10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp4144.1MB
26. 10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp4111.75MB
27. 10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp4110.76MB
28. 10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4163.86MB
29. 10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp453.69MB
30. 10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp488.7MB
31. 10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp4107.07MB
32. 10. PyTorch Paper Replicating/34. Transformer Encoder Overview.mp482.85MB
33. 10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.mp484.87MB
34. 10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4188.74MB
35. 10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4190.81MB
36. 10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4111.37MB
37. 10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp484.89MB
38. 10. PyTorch Paper Replicating/4. What We Are Going to Cover.mp487.76MB
39. 10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp4118.33MB
40. 10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp453.47MB
41. 10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp4101.19MB
42. 10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp463.39MB
43. 10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4164.75MB
44. 10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp457.21MB
45. 10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp476.28MB
46. 10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp440.36MB
47. 10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp441.81MB
48. 10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp437.11MB
49. 10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp499.13MB
50. 10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp485.48MB
51. 10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp443.84MB
52. 10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp489.7MB
53. 10. PyTorch Paper Replicating/8. Visualizing a Single Image.mp436.44MB
54. 10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp477.83MB
55. 11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp473.83MB
56. 11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp492.12MB
57. 11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp457.59MB
58. 11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp431.38MB
59. 11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp497.04MB
60. 11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp426.7MB
61. 11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp455.47MB
62. 11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp463.27MB
63. 11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp478.51MB
64. 11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp419.7MB
65. 11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp462MB
66. 11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp446.93MB
67. 11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp443.77MB
68. 11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.mp445.85MB
69. 11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp493.41MB
70. 11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4185.77MB
71. 11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp497.62MB
72. 11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp472.47MB
73. 11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp489.62MB
74. 11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp4134.66MB
75. 11. PyTorch Model Deployment/28. Gradio Overview and Installation.mp495.13MB
76. 11. PyTorch Model Deployment/29. Gradio Function Outline.mp479.89MB
77. 11. PyTorch Model Deployment/3. Where Is My Model Going to Go.mp4139.84MB
78. 11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp495.22MB
79. 11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp453.3MB
80. 11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp4135.38MB
81. 11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp464.81MB
82. 11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp489.53MB
83. 11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp439.14MB
84. 11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp492.4MB
85. 11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp471.91MB
86. 11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp444.78MB
87. 11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4137.62MB
88. 11. PyTorch Model Deployment/4. How Is My Model Going to Function.mp467.36MB
89. 11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp437.5MB
90. 11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp4112.22MB
91. 11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4143.59MB
92. 11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp491.6MB
93. 11. PyTorch Model Deployment/44. Food Vision Big Project Outline.mp439.14MB
94. 11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp496.52MB
95. 11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp471.66MB
96. 11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4119.73MB
97. 11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp461.5MB
98. 11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4184.21MB
99. 11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp465.36MB
100. 11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp452.77MB
101. 11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp436.59MB
102. 11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp466.82MB
103. 11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp423.9MB
104. 11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4104.81MB
105. 11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp439.75MB
106. 11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp4162.52MB
107. 11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp481.75MB
108. 11. PyTorch Model Deployment/6. What We Are Going to Cover.mp440.82MB
109. 11. PyTorch Model Deployment/7. Getting Setup to Code.mp462.89MB
110. 11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp439.25MB
111. 11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp458.55MB
112. 12. Introduction to PyTorch 2.0 and torch.compile/1. Introduction to PyTorch 2.0.mp482.16MB
113. 12. Introduction to PyTorch 2.0 and torch.compile/10. Creating a Function to Setup Our Model and Transforms.mp499.61MB
114. 12. Introduction to PyTorch 2.0 and torch.compile/11. Discussing How to Get Better Relative Speedups for Training Models.mp470.1MB
115. 12. Introduction to PyTorch 2.0 and torch.compile/12. Setting the Batch Size and Data Size Programmatically.mp470.98MB
116. 12. Introduction to PyTorch 2.0 and torch.compile/13. Getting More Potential Speedups with TensorFloat-32.mp483.85MB
117. 12. Introduction to PyTorch 2.0 and torch.compile/14. Downloading the CIFAR10 Dataset.mp467.55MB
118. 12. Introduction to PyTorch 2.0 and torch.compile/15. Creating Training and Test DataLoaders.mp467.81MB
119. 12. Introduction to PyTorch 2.0 and torch.compile/16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp460.72MB
120. 12. Introduction to PyTorch 2.0 and torch.compile/17. Experiment 1 - Single Run without torch.compile.mp478.14MB
121. 12. Introduction to PyTorch 2.0 and torch.compile/18. Experiment 2 - Single Run with torch.compile.mp4105.61MB
122. 12. Introduction to PyTorch 2.0 and torch.compile/19. Comparing the Results of Experiment 1 and 2.mp4120.57MB
123. 12. Introduction to PyTorch 2.0 and torch.compile/2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp415.08MB
124. 12. Introduction to PyTorch 2.0 and torch.compile/20. Saving the Results of Experiment 1 and 2.mp458.03MB
125. 12. Introduction to PyTorch 2.0 and torch.compile/21. Preparing Functions for Experiment 3 and 4.mp4116.28MB
126. 12. Introduction to PyTorch 2.0 and torch.compile/22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4132.79MB
127. 12. Introduction to PyTorch 2.0 and torch.compile/23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4104.98MB
128. 12. Introduction to PyTorch 2.0 and torch.compile/24. Comparing the Results of Experiment 3 and 4.mp462.82MB
129. 12. Introduction to PyTorch 2.0 and torch.compile/25. Potential Extensions and Resources to Learn More.mp464.06MB
130. 12. Introduction to PyTorch 2.0 and torch.compile/3. Getting Started with PyTorch 2 in Google Colab.mp444.58MB
131. 12. Introduction to PyTorch 2.0 and torch.compile/4. PyTorch 2.0 - 30 Second Intro.mp422.4MB
132. 12. Introduction to PyTorch 2.0 and torch.compile/5. Getting Setup for PyTorch 2.mp427.14MB
133. 12. Introduction to PyTorch 2.0 and torch.compile/6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp477.55MB
134. 12. Introduction to PyTorch 2.0 and torch.compile/7. Setting the Default Device in PyTorch 2.mp4102.96MB
135. 12. Introduction to PyTorch 2.0 and torch.compile/8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp457.55MB
136. 12. Introduction to PyTorch 2.0 and torch.compile/9. Introduction to PyTorch 2.mp482.13MB
137. 14. Where To Go From Here/1. Thank You!.mp420.98MB
138. 2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp413.81MB
139. 2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp437.74MB
140. 2. PyTorch Fundamentals/11. Important Resources For This Course.mp458.32MB
141. 2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp469.99MB
142. 2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp493.99MB
143. 2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp486.42MB
144. 2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp424.56MB
145. 2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp432.59MB
146. 2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp481.41MB
147. 2. PyTorch Fundamentals/18. Getting Tensor Attributes.mp466.44MB
148. 2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp439.7MB
149. 2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp435.33MB
150. 2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp477.8MB
151. 2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp457.77MB
152. 2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp497.34MB
153. 2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp448.15MB
154. 2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp424.49MB
155. 2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp4103.95MB
156. 2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp488.41MB
157. 2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp456.95MB
158. 2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp459.77MB
159. 2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp495.11MB
160. 2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp455.29MB
161. 2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp4113.01MB
162. 2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp464.51MB
163. 2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp456.76MB
164. 2. PyTorch Fundamentals/4. Anatomy of Neural Networks.mp470.32MB
165. 2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp427.04MB
166. 2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp443.19MB
167. 2. PyTorch Fundamentals/7. What Is and Why PyTorch.mp4113.55MB
168. 2. PyTorch Fundamentals/8. What Are Tensors.mp424.98MB
169. 2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp450.45MB
170. 3. PyTorch Workflow/1. Introduction and Where You Can Get Help.mp428.6MB
171. 3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp4107.03MB
172. 3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp469.49MB
173. 3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp4116MB
174. 3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp4128.78MB
175. 3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp483MB
176. 3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4177.45MB
177. 3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4101.7MB
178. 3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4135.03MB
179. 3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4161.56MB
180. 3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp4129.82MB
181. 3. PyTorch Workflow/2. Getting Setup and What We Are Covering.mp469.68MB
182. 3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp479.57MB
183. 3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp445.79MB
184. 3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp449.34MB
185. 3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp488.69MB
186. 3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp4102.99MB
187. 3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp450.63MB
188. 3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp472.53MB
189. 3. PyTorch Workflow/27. Exercise Imposter Syndrome.mp439.25MB
190. 3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.mp449.31MB
191. 3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp468.66MB
192. 3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp465.21MB
193. 3. PyTorch Workflow/5. Building a function to Visualize Our Data.mp461.89MB
194. 3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp4130.08MB
195. 3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp462.18MB
196. 3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp474.44MB
197. 3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp4102.71MB
198. 4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp484.58MB
199. 4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4161.05MB
200. 4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4134.54MB
201. 4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4126.75MB
202. 4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4149.99MB
203. 4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp480.86MB
204. 4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp468.82MB
205. 4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4118.63MB
206. 4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp461.35MB
207. 4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp471.67MB
208. 4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp450.79MB
209. 4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp449.96MB
210. 4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp496.52MB
211. 4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp492.59MB
212. 4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp4150.56MB
213. 4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp453.04MB
214. 4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp480.74MB
215. 4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp497.45MB
216. 4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp4107.43MB
217. 4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp465.06MB
218. 4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp497.04MB
219. 4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4150.08MB
220. 4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp467.04MB
221. 4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp477.04MB
222. 4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp497.54MB
223. 4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp441.46MB
224. 4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp491.48MB
225. 4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp481.06MB
226. 4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp431.91MB
227. 4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp486.84MB
228. 4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp491.28MB
229. 4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4123.24MB
230. 5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4113.66MB
231. 5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp4110.53MB
232. 5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp445.61MB
233. 5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp4157.56MB
234. 5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp4106.78MB
235. 5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp444.32MB
236. 5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp486.38MB
237. 5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.mp431.33MB
238. 5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp470.88MB
239. 5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp450.89MB
240. 5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp4108.43MB
241. 5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp485.01MB
242. 5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp441.35MB
243. 5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp494.62MB
244. 5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4208.33MB
245. 5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp4162.71MB
246. 5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp4158.1MB
247. 5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4174.82MB
248. 5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp427.87MB
249. 5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.mp476.78MB
250. 5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.mp461.75MB
251. 5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.mp483.66MB
252. 5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).mp455.4MB
253. 5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp463.48MB
254. 5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4160.84MB
255. 5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp467MB
256. 5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.mp498.15MB
257. 5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp481.89MB
258. 5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp489.19MB
259. 5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4153.99MB
260. 5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.mp468.11MB
261. 5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.mp460.2MB
262. 5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.mp4100.23MB
263. 5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4136.88MB
264. 6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.mp492.59MB
265. 6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.mp476.72MB
266. 6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.mp484.32MB
267. 6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp474.7MB
268. 6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.mp479.09MB
269. 6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4176.27MB
270. 6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp469.5MB
271. 6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4131.21MB
272. 6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.mp480.62MB
273. 6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4166.35MB
274. 6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp477.93MB
275. 6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.mp448.96MB
276. 6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4117.22MB
277. 6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp496.49MB
278. 6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.mp464.97MB
279. 6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.mp4106.16MB
280. 6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.mp4103.47MB
281. 6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.mp489.27MB
282. 6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.mp489.44MB
283. 6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4131.81MB
284. 6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp498.83MB
285. 6. PyTorch Custom Datasets/29. Constructing and Training Model 1.mp460.64MB
286. 6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4150.95MB
287. 6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.mp431.69MB
288. 6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.mp489.26MB
289. 6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.mp451.66MB
290. 6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp468MB
291. 6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4127.05MB
292. 6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp436.06MB
293. 6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.mp4113.03MB
294. 6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp473.32MB
295. 6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.mp487.61MB
296. 6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4115.33MB
297. 6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp451.91MB
298. 6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.mp481.71MB
299. 6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.mp4127.58MB
300. 6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp498.16MB
301. 7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.mp4100.12MB
302. 7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.mp480.67MB
303. 7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.mp4104.92MB
304. 7. PyTorch Going Modular/3. Downloading a Dataset.mp467.63MB
305. 7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.mp4156.79MB
306. 7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4135.14MB
307. 7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.mp4115.12MB
308. 7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.mp480MB
309. 7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.mp475.79MB
310. 7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.mp4165.53MB
311. 8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.mp497.25MB
312. 8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.mp456.96MB
313. 8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.mp476.03MB
314. 8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4160.67MB
315. 8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.mp474.8MB
316. 8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.mp458.93MB
317. 8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.mp466.74MB
318. 8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.mp4101.66MB
319. 8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.mp478.14MB
320. 8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.mp467.83MB
321. 8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.mp444.43MB
322. 8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp455.86MB
323. 8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.mp482.39MB
324. 8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.mp483.74MB
325. 8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.mp472.16MB
326. 8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4141.48MB
327. 8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4139.74MB
328. 8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.mp4128.78MB
329. 8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.mp4113.14MB
330. 9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.mp461.85MB
331. 9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.mp480.1MB
332. 9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp466.53MB
333. 9. PyTorch Experiment Tracking/12. What Experiments Should You Try.mp446.91MB
334. 9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.mp448.29MB
335. 9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.mp466.41MB
336. 9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp478.06MB
337. 9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.mp4159.2MB
338. 9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4127.61MB
339. 9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.mp445.66MB
340. 9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.mp4140.29MB
341. 9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp493.39MB
342. 9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp499.19MB
343. 9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.mp439.71MB
344. 9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.mp443.6MB
345. 9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.mp495.22MB
346. 9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.mp492.72MB
347. 9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.mp482MB
348. 9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.mp4113.16MB
349. 9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4150.28MB
350. 9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.mp441.79MB
351. 9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.mp4116.27MB