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Genre:eLearning | Language: English | Size:29.69 GB

Files Included :

1  PyTorch for Deep Learning.mp4 (75.35 MB)
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2  Course Welcome and What Is Deep Learning.mp4 (38.99 MB)
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3  Join Our Online Classroom!.mp4 (75.34 MB)
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6  ZTM Resources.mp4 (44.57 MB)
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1  What Is a Machine Learning Research Paper.mp4 (93.94 MB)
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10  Breaking Down Figure 1 of the ViT Paper.mp4 (87.11 MB)
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11  Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 (140.92 MB)
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12  Breaking Down Equation 1.mp4 (103.21 MB)
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13  Breaking Down Equation 2 and 3.mp4 (125.03 MB)
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14  Breaking Down Equation 4.mp4 (92.43 MB)
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15  Breaking Down Table 1.mp4 (122.09 MB)
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16  Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 (160.59 MB)
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17  Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 (150.15 MB)
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18  Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 (130.65 MB)
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19  Creating Patch Embeddings with a Convolutional Layer.mp4 (142.62 MB)
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2  Why Replicate a Machine Learning Research Paper.mp4 (23.26 MB)
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20  Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 (129.06 MB)
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21  Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 (89.61 MB)
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22  Visualizing a Single Sequence Vector of Patch Embeddings.mp4 (50.37 MB)
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23  Creating the Patch Embedding Layer with PyTorch.mp4 (170.03 MB)
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24  Creating the Class Token Embedding.mp4 (131.98 MB)
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25  Creating the Class Token Embedding - Less Birds.mp4 (131.91 MB)
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26  Creating the Position Embedding.mp4 (109.18 MB)
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27  Equation 1 Putting it All Together.mp4 (134.81 MB)
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28  Equation 2 Multihead Attention Overview.mp4 (144.1 MB)
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29  Equation 2 Layernorm Overview.mp4 (111.75 MB)
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3  Where Can You Find Machine Learning Research Papers and Code.mp4 (110.76 MB)
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30  Turning Equation 2 into Code.mp4 (163.86 MB)
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31  Checking the Inputs and Outputs of Equation.mp4 (53.69 MB)
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32  Equation 3 Replication Overview.mp4 (88.7 MB)
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33  Turning Equation 3 into Code.mp4 (107.07 MB)
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34  Transformer  Encoder Overview.mp4 (82.85 MB)
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35  Combining equation 2 and 3 to Create the Transformer  Encoder.mp4 (84.87 MB)
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36  Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 (188.74 MB)
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37  Bringing Our Own Vision Transformer to Life  - Part 1 Gathering the Pieces.mp4 (190.81 MB)
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38  Bringing Our Own Vision Transformer to Life  - Part 2 The Forward Method.mp4 (111.37 MB)
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39  Getting a Visual Summary of Our Custom Vision Transformer.mp4 (84.89 MB)
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4  What We Are Going to Cover.mp4 (87.76 MB)
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40  Creating a Loss Function and Optimizer from the ViT Paper.mp4 (118.33 MB)
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41  Training our Custom ViT on Food Vision Mini.mp4 (53.47 MB)
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42  Discussing what Our Training Setup Is Missing.mp4 (101.19 MB)
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43  Plotting a Loss Curve for Our ViT Model.mp4 (63.39 MB)
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44  Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 (164.75 MB)
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45  Preparing Data to Be Used with a Pretrained ViT.mp4 (57.21 MB)
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46  Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 (76.28 MB)
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47  Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 (40.36 MB)
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48  Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 (41.81 MB)
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49  Making Predictions on a Custom Image with Our Pretrained ViT.mp4 (37.11 MB)
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5  Getting Setup for Coding in Google Colab.mp4 (99.13 MB)
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50  PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 (85.48 MB)
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6  Downloading Data for Food Vision Mini.mp4 (43.84 MB)
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7  Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 (89.7 MB)
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8  Visualizing a Single Image.mp4 (36.44 MB)
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9  Replicating a Vision Transformer - High Level Overview.mp4 (77.83 MB)
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1  What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 (73.83 MB)
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10  Creating an EffNetB2 Feature Extractor Model.mp4 (92.12 MB)
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11  Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 (57.59 MB)
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12  Creating DataLoaders for EffNetB2.mp4 (31.38 MB)
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13  Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 (97.04 MB)
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14  Saving Our EffNetB2 Model to File.mp4 (26.7 MB)
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15  Getting the Size of Our EffNetB2 Model in Megabytes.mp4 (55.47 MB)
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16  Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 (63.27 MB)
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17  Creating a Vision Transformer Feature Extractor Model.mp4 (78.51 MB)
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18  Creating DataLoaders for Our ViT Feature Extractor Model.mp4 (19.7 MB)
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19  Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 (62 MB)
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2  Three Questions to Ask for Machine Learning Model Deployment.mp4 (46.93 MB)
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20  Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 (43.77 MB)
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21  Collecting Stats About Our-ViT Feature Extractor.mp4 (45.85 MB)
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22  Outlining the Steps for Making and Timing Predictions for Our Models.mp4 (93.41 MB)
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23  Creating a Function to Make and Time Predictions with Our Models.mp4 (185.77 MB)
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24  Making and Timing Predictions with EffNetB2.mp4 (97.62 MB)
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25  Making and Timing Predictions with ViT.mp4 (72.47 MB)
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26  Comparing EffNetB2 and ViT Model Statistics.mp4 (89.62 MB)
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27  Visualizing the  Performance vs Speed Trade-off.mp4 (134.66 MB)
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28  Gradio Overview and Installation.mp4 (95.13 MB)
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29  Gradio Function Outline.mp4 (79.89 MB)
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3  Where Is My Model Going to Go.mp4 (139.84 MB)
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30  Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 (95.22 MB)
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31  Creating a List of Examples to Pass to Our Gradio Demo.mp4 (53.3 MB)
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32  Bringing Food Vision Mini to Life in a Live Web Application.mp4 (135.38 MB)
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33  Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 (64.81 MB)
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34  Outlining the File Structure of Our Deployed App.mp4 (89.53 MB)
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35  Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 (39.14 MB)
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36  Creating an Examples Directory with Example Food Vision Mini Images.mp4 (92.4 MB)
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37  Writing Code to Move Our Saved EffNetB2 Model File.mp4 (71.91 MB)
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38  Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 (44.78 MB)
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39  Turning Our Food Vision Mini Demo App Into a Python Script.mp4 (137.62 MB)
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4  How Is My Model Going to Function.mp4 (67.36 MB)
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40  Creating a Requirements File for Our Food Vision Mini App.mp4 (37.5 MB)
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41  Downloading Our Food Vision Mini App Files from Google Colab.mp4 (112.22 MB)
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42  Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 (143.59 MB)
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43  Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 (91.6 MB)
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44  Food Vision Big Project Outline.mp4 (39.14 MB)
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45  Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 (96.52 MB)
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46  Downloading the Food 101 Dataset.mp4 (71.66 MB)
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47  Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 (119.73 MB)
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48  Turning Our Food 101 Datasets into DataLoaders.mp4 (61.5 MB)
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49  Training Food Vision Big Our Biggest Model Yet!.mp4 (184.21 MB)
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5  Some Tools and Places to Deploy Machine Learning Models.mp4 (65.36 MB)
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50  Outlining the File Structure for Our Food Vision Big.mp4 (52.77 MB)
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51  Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 (36.59 MB)
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52  Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 (66.82 MB)
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53  Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 (23.9 MB)
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54  Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 (104.81 MB)
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55  Zipping and Downloading Our Food Vision Big App Files.mp4 (39.75 MB)
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56  Deploying Food Vision Big to Hugging Face Spaces.mp4 (162.52 MB)
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57  PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 (81.75 MB)
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6  What We Are Going to Cover.mp4 (40.82 MB)
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7  Getting Setup to Code.mp4 (62.89 MB)
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8  Downloading a Dataset for Food Vision Mini.mp4 (39.25 MB)
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9  Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 (58.55 MB)
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1  Introduction to PyTorch 2 0.mp4 (82.16 MB)
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10  Creating a Function to Setup Our Model and Transforms.mp4 (99.61 MB)
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11  Discussing How to Get Better Relative Speedups for Training Models.mp4 (70.1 MB)
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12  Setting the Batch Size and Data Size Programmatically.mp4 (70.98 MB)
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13  Getting More Potential Speedups with TensorFloat-32.mp4 (83.85 MB)
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14  Downloading the CIFAR10 Dataset.mp4 (67.55 MB)
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15  Creating Training and Test DataLoaders.mp4 (67.81 MB)
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16  Preparing Training and Testing Loops with Timing Steps for PyTorch 2 0 timing.mp4 (60.72 MB)
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17  Experiment 1 - Single Run without torch compile.mp4 (78.14 MB)
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18  Experiment 2  - Single Run with torch compile.mp4 (105.61 MB)
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19  Comparing the Results of Experiment 1 and 2.mp4 (120.57 MB)
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2  What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 (15.08 MB)
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20  Saving the Results of Experiment 1 and 2.mp4 (58.03 MB)
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21  Preparing Functions for Experiment 3 and 4.mp4 (116.28 MB)
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22  Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 (132.79 MB)
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23  Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 (104.98 MB)
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24  Comparing the Results of Experiment 3 and 4.mp4 (62.82 MB)
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25  Potential Extensions and Resources to Learn More.mp4 (64.06 MB)
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3  Getting Started with PyTorch 2 in Google Colab.mp4 (44.58 MB)
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4  PyTorch 2 0 - 30 Second Intro.mp4 (22.4 MB)
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5  Getting Setup for PyTorch 2.mp4 (27.14 MB)
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6  Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 (77.55 MB)
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7  Setting the Default Device in PyTorch 2.mp4 (102.96 MB)
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8  Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 (57.55 MB)
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9  Introduction to PyTorch 2.mp4 (82.13 MB)
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1  Thank You!.mp4 (20.98 MB)
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1  Why Use Machine Learning or Deep Learning.mp4 (13.81 MB)
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10  How To and How Not To Approach This Course.mp4 (37.74 MB)
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11  Important Resources For This Course.mp4 (58.32 MB)
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12  Getting Setup to Write PyTorch Code.mp4 (69.99 MB)
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13  Introduction to PyTorch Tensors.mp4 (93.99 MB)
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14  Creating Random Tensors in PyTorch.mp4 (86.42 MB)
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15  Creating Tensors With  Zeros and Ones in PyTorch.mp4 (24.56 MB)
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16  Creating a Tensor Range and Tensors Like Other Tensors.mp4 (32.59 MB)
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17  Dealing With Tensor Data Types.mp4 (81.41 MB)
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18  Getting Tensor Attributes.mp4 (66.44 MB)
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19  Manipulating Tensors (Tensor Operations).mp4 (39.7 MB)
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2  The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 (35.33 MB)
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20  Matrix Multiplication (Part 1).mp4 (77.8 MB)
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21  Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 (57.77 MB)
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22  Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 (97.34 MB)
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23  Finding the Min Max Mean and  Sum of Tensors (Tensor Aggregation).mp4 (48.15 MB)
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24  Finding The Positional Min and Max of Tensors.mp4 (24.49 MB)
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25  Reshaping, Viewing and Stacking Tensors.mp4 (103.95 MB)
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26  Squeezing, Unsqueezing and Permuting Tensors.mp4 (88.41 MB)
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27  Selecting Data From Tensors (Indexing).mp4 (56.95 MB)
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28  PyTorch Tensors and NumPy.mp4 (59.77 MB)
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29  PyTorch Reproducibility (Taking the Random Out of Random).mp4 (95.11 MB)
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3  Machine Learning vs  Deep Learning.mp4 (55.29 MB)
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30  Different Ways of Accessing a GPU in PyTorch.mp4 (113.01 MB)
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31  Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 (64.51 MB)
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32  PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 (56.76 MB)
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4  Anatomy of Neural Networks.mp4 (70.32 MB)
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5  Different Types of Learning Paradigms.mp4 (27.04 MB)
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6  What Can Deep Learning Be Used For.mp4 (43.19 MB)
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7  What Is and Why PyTorch.mp4 (113.55 MB)
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8  What Are Tensors.mp4 (24.98 MB)
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9  What We Are Going To Cover With PyTorch.mp4 (50.45 MB)
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1  Introduction and Where You Can Get Help.mp4 (28.6 MB)
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10  Making Predictions With Our Random Model Using Inference Mode.mp4 (107.03 MB)
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11  Training a Model Intuition (The Things We Need).mp4 (69.49 MB)
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12  Setting Up an Optimizer and a Loss Function.mp4 (116 MB)
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13  PyTorch Training Loop Steps and Intuition.mp4 (128.78 MB)
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14  Writing Code for a PyTorch Training Loop.mp4 (83 MB)
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15  Reviewing the Steps in a Training Loop Step by Step.mp4 (177.45 MB)
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16  Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 (101.7 MB)
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17  Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 (135.03 MB)
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18  Reviewing What Happens in a Testing Loop Step by Step.mp4 (161.56 MB)
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19  Writing Code to Save a PyTorch Model.mp4 (129.82 MB)
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2  Getting Setup and What We Are Covering.mp4 (69.68 MB)
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20  Writing Code  to Load a PyTorch Model.mp4 (79.57 MB)
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21  Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 (45.79 MB)
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22  Putting Everything Together (Part 1) Data.mp4 (49.34 MB)
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23  Putting Everything Together (Part 2) Building a Model.mp4 (88.69 MB)
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24  Putting Everything Together (Part 3)  Training a Model.mp4 (102.99 MB)
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25  Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 (50.63 MB)
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26  Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 (72.53 MB)
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27  Exercise Imposter Syndrome.mp4 (39.25 MB)
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28  PyTorch Workflow Exercises and Extra-Curriculum.mp4 (49.31 MB)
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3  Creating a Simple Dataset Using the Linear Regression Formula.mp4 (68.66 MB)
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4  Splitting Our Data Into Training and Test Sets.mp4 (65.21 MB)
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5  Building a function to Visualize Our Data.mp4 (61.89 MB)
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6  Creating Our First PyTorch Model for Linear Regression.mp4 (130.08 MB)
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7  Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 (62.18 MB)
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8  Discussing Some of the Most Important PyTorch Model Building Classes.mp4 (74.44 MB)
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9  Checking Out the Internals of Our PyTorch Model.mp4 (102.71 MB)
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1  Introduction to Machine Learning Classification With PyTorch.mp4 (84.58 MB)
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10  Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 (161.05 MB)
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11  Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 (134.54 MB)
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12  Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 (126.75 MB)
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13  Writing Code to download скачать a Helper Function to Visualize Our Models Predictions.mp4 (149.99 MB)
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14  Discussing Options to Improve a Model.mp4 (80.86 MB)
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15  Creating a New Model with More Layers and Hidden Units.mp4 (68.82 MB)
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16  Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 (118.63 MB)
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17  Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 (61.35 MB)
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18  Building and Training a Model to Fit on Straight Line Data.mp4 (71.67 MB)
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19  Evaluating Our Models Predictions on Straight Line Data.mp4 (50.79 MB)
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2  Classification Problem Example Input and Output Shapes.mp4 (49.96 MB)
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20  Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 (96.52 MB)
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21  Building Our First Neural Network with Non-Linearity.mp4 (92.59 MB)
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22  Writing Training and Testing Code for Our First Non-Linear Model.mp4 (150.56 MB)
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23  Making Predictions with and Evaluating Our First Non-Linear Model.mp4 (53.04 MB)
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24  Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 (80.74 MB)
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25  Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 (97.45 MB)
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26  Creating a Multi-Class Classification Model with PyTorch.mp4 (107.43 MB)
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27  Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 (65.06 MB)
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28  Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 (97.04 MB)
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29  Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 (150.08 MB)
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3  Typical Architecture of a Classification Neural Network (Overview).mp4 (67.04 MB)
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30  Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 (77.04 MB)
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31  Discussing a Few More Classification Metrics.mp4 (97.54 MB)
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32  PyTorch Classification Exercises and Extra-Curriculum.mp4 (41.46 MB)
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4  Making a Toy Classification Dataset.mp4 (91.48 MB)
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5  Turning Our Data into Tensors and Making a Training and Test Split.mp4 (81.06 MB)
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6  Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 (31.91 MB)
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7  Coding a Small Neural Network to Handle Our Classification Data.mp4 (86.84 MB)
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8  Making Our Neural Network Visual.mp4 (91.28 MB)
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9  Recreating and Exploring the Insides of Our Model Using nn Sequential.mp4 (123.24 MB)
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1  What Is a Computer Vision Problem and What We Are Going to Cover.mp4 (113.66 MB)
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10  Creating a Loss Function an Optimizer for Model 0.mp4 (110.53 MB)
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11  Creating a Function to Time Our Modelling Code.mp4 (45.61 MB)
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12  Writing Training and Testing Loops for Our Batched Data.mp4 (157.56 MB)
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13  Writing an Evaluation Function to Get Our Models Results.mp4 (106.78 MB)
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14  Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 (44.32 MB)
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15  Model 1 Creating a Model with Non-Linear Functions.mp4 (86.38 MB)
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16  Mode 1 Creating a Loss Function and Optimizer.mp4 (31.33 MB)
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17  Turing Our Training Loop into a Function.mp4 (70.88 MB)
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18  Turing Our Testing Loop into a Function.mp4 (50.89 MB)
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19  Training and Testing Model 1 with Our Training and Testing Functions.mp4 (108.43 MB)
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2  Computer Vision Input and Output Shapes.mp4 (85.01 MB)
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20  Getting a Results Dictionary for Model 1.mp4 (41.35 MB)
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21  Model 2 Convolutional Neural Networks High Level Overview.mp4 (94.62 MB)
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22  Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 (208.33 MB)
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23  Model 2 Breaking Down Conv2D Step by Step.mp4 (162.71 MB)
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24  Model 2 Breaking Down MaxPool2D Step by Step.mp4 (158.1 MB)
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25  Mode 2  Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 (174.82 MB)
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26  Model 2 Setting Up a Loss Function and Optimizer.mp4 (27.87 MB)
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27  Model 2 Training Our First CNN and Evaluating Its Results.mp4 (76.78 MB)
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28  Comparing the Results of Our Modelling Experiments.mp4 (61.75 MB)
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29  Making Predictions on Random Test Samples with the Best Trained Model.mp4 (83.66 MB)
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3  What Is a Convolutional Neural Network (CNN).mp4 (55.4 MB)
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30  Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 (63.48 MB)
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31  Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 (160.84 MB)
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32  Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 (67 MB)
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33  Saving and Loading Our Best Performing Model.mp4 (98.15 MB)
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34  Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 (81.89 MB)
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4  Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 (89.19 MB)
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5  Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 (153.99 MB)
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6  Visualizing Random Samples of Data.mp4 (68.11 MB)
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7  DataLoader Overview Understanding Mini-Batches.mp4 (60.2 MB)
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8  Turning Our Datasets Into DataLoaders.mp4 (100.23 MB)
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9  Model 0 Creating a Baseline Model with Two Linear Layers.mp4 (136.88 MB)
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1  What Is a Custom Dataset and What We Are Going to Cover.mp4 (92.59 MB)
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10  Visualizing a Loaded Image From the Train Dataset.mp4 (76.72 MB)
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11  Turning Our Image Datasets into PyTorch Dataloaders.mp4 (84.32 MB)
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12  Creating a Custom Dataset Class in PyTorch High  Level Overview.mp4 (74.7 MB)
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13  Creating a Helper Function to Get Class Names From a Directory.mp4 (79.09 MB)
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14  Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 (176.27 MB)
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15  Compare Our Custom Dataset Class  to the Original Imagefolder Class.mp4 (69.5 MB)
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16  Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 (131.21 MB)
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17  Turning Our Custom Datasets Into DataLoaders.mp4 (80.62 MB)
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18  Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 (166.35 MB)
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19  Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 (77.93 MB)
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2  Importing PyTorch and Setting Up Device Agnostic Code.mp4 (48.96 MB)
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20  Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 (117.22 MB)
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21  Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 (96.49 MB)
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22  Using the Torchinfo Package to Get a Summary of Our Model.mp4 (64.97 MB)
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23  Creating Training and Testing loop Functions.mp4 (106.16 MB)
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24  Creating a Train Function to Train and Evaluate Our Models.mp4 (103.47 MB)
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25  Training and Evaluating Model 0 With Our Training Functions.mp4 (89.27 MB)
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26  Plotting the Loss Curves of Model 0.mp4 (89.44 MB)
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27  The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 (131.81 MB)
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28  Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 (98.83 MB)
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29  Constructing and Training Model 1.mp4 (60.64 MB)
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3  Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 (150.95 MB)
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30  Plotting the Loss Curves of Model 1.mp4 (31.69 MB)
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31  Plotting the Loss Curves of All of Our Models Against Each Other.mp4 (89.26 MB)
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32  Predicting on Custom Data (Part 1) Downloading an Image.mp4 (51.66 MB)
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33  Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 (68 MB)
MP4
34  Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 (127.05 MB)
MP4
35  Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 (36.06 MB)
MP4
36  Predicting on Custom Data (Part 5) Putting It All Together.mp4 (113.03 MB)
MP4
37  Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 (73.32 MB)
MP4
4  Becoming One With the Data (Part 1) Exploring the Data Format.mp4 (87.61 MB)
MP4
5  Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 (115.33 MB)
MP4
6  Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 (51.91 MB)
MP4
7  Transforming Data (Part 1) Turning Images Into Tensors.mp4 (81.71 MB)
MP4
8  Transforming Data (Part 2) Visualizing Transformed Images.mp4 (127.58 MB)
MP4
9  Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 (98.16 MB)
MP4
1  What Is Going Modular and What We Are Going to Cover.mp4 (100.12 MB)
MP4
10  Going Modular Summary, Exercises and Extra-Curriculum.mp4 (80.67 MB)
MP4
2  Going Modular Notebook (Part 1) Running It End to End.mp4 (104.92 MB)
MP4
3  Downloading a Dataset.mp4 (67.63 MB)
MP4
4  Writing the Outline for Our First Python Script to Setup the Data.mp4 (156.79 MB)
MP4
5  Creating a Python Script to Create Our PyTorch DataLoaders.mp4 (135.14 MB)
MP4
6  Turning Our Model Building Code into a Python Script.mp4 (115.12 MB)
MP4
7  Turning Our Model Training Code into a Python Script.mp4 (80 MB)
MP4
8  Turning Our Utility Function to Save a Model into a Python Script.mp4 (75.79 MB)
MP4
9  Creating a Training Script to Train Our Model in One Line of Code.mp4 (165.53 MB)
MP4
1  Introduction What is  Transfer Learning and Why Use It.mp4 (97.25 MB)
MP4
10  Different Kinds of Transfer Learning.mp4 (56.96 MB)
MP4
11  Getting a Summary of the Different Layers of  Our Model.mp4 (76.03 MB)
MP4
12  Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 (160.67 MB)
MP4
13  Training Our First Transfer Learning Feature Extractor Model.mp4 (74.8 MB)
MP4
14  Plotting the Loss curves of Our Transfer Learning Model.mp4 (58.93 MB)
MP4
15  Outlining the Steps to Make Predictions on the Test Images.mp4 (66.74 MB)
MP4
16  Creating a Function Predict On and Plot Images.mp4 (101.66 MB)
MP4
17  Making and Plotting Predictions on Test Images.mp4 (78.14 MB)
MP4
18  Making a Prediction on a Custom Image.mp4 (67.83 MB)
MP4
19  Main Takeaways, Exercises and Extra- Curriculum.mp4 (44.43 MB)
MP4
2  Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 (55.86 MB)
MP4
3  Installing the Latest Versions of Torch and Torchvision.mp4 (82.39 MB)
MP4
4  Downloading Our Previously Written Code from Going Modular.mp4 (83.74 MB)
MP4
5  Downloading Pizza, Steak, Sushi Image Data from Github.mp4 (72.16 MB)
MP4
6  Turning Our Data into DataLoaders with Manually Created Transforms.mp4 (141.48 MB)
MP4
7  Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 (139.74 MB)
MP4
8  Which Pretrained Model Should You Use.mp4 (128.78 MB)
MP4
9  Setting Up a Pretrained Model with Torchvision.mp4 (113.14 MB)
MP4
1  What Is Experiment Tracking and Why Track Experiments.mp4 (61.85 MB)
MP4
10  Creating a Function to Create SummaryWriter Instances.mp4 (80.1 MB)
MP4
11  Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 (66.53 MB)
MP4
12  What Experiments Should You Try.mp4 (46.91 MB)
MP4
13  Discussing the Experiments We Are Going to Try.mp4 (48.29 MB)
MP4
14  Downloading Datasets for Our Modelling Experiments.mp4 (66.41 MB)
MP4
15  Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 (78.06 MB)
MP4
16  Creating Functions to Prepare Our Feature Extractor Models.mp4 (159.2 MB)
MP4
17  Coding Out the Steps to Run a Series of Modelling Experiments.mp4 (127.61 MB)
MP4
18  Running Eight Different Modelling Experiments in 5 Minutes.mp4 (45.66 MB)
MP4
19  Viewing Our Modelling Experiments in TensorBoard.mp4 (140.29 MB)
MP4
2  Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 (93.39 MB)
MP4
20  Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 (99.19 MB)
MP4
21  Making a Prediction on Our Own Custom Image with the Best Model.mp4 (39.71 MB)
MP4
22  Main Takeaways, Exercises and Extra- Curriculum.mp4 (43.6 MB)
MP4
3  Creating a Function to download скачать Data.mp4 (95.22 MB)
MP4
4  Turning Our Data into DataLoaders Using Manual Transforms.mp4 (92.72 MB)
MP4
5  Turning Our Data into DataLoaders Using Automatic Transforms.mp4 (82 MB)
MP4
6  Preparing a Pretrained Model for Our Own Problem.mp4 (113.16 MB)
MP4
7  Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 (150.28 MB)
MP4
8  Training a Single Model and Saving the Results to TensorBoard.mp4 (41.79 MB)
MP4
9  Exploring Our Single Models Results with TensorBoard.mp4 (116.27 MB)
MP4

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