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| 1. Introduction and Where You Can Get Help.mp4 |
28.60Мб |
| 1. Introduction and Where You Can Get Help.srt |
5.14Кб |
| 1. Introduction to Machine Learning Classification With PyTorch.mp4 |
84.58Мб |
| 1. Introduction to Machine Learning Classification With PyTorch.srt |
15.95Кб |
| 1. Introduction to PyTorch 2.0.mp4 |
82.16Мб |
| 1. Introduction to PyTorch 2.0.srt |
8.51Кб |
| 1. Introduction What is Transfer Learning and Why Use It.mp4 |
97.25Мб |
| 1. Introduction What is Transfer Learning and Why Use It.srt |
15.73Кб |
| 1. PyTorch for Deep Learning.mp4 |
75.35Мб |
| 1. PyTorch for Deep Learning.srt |
5.20Кб |
| 1. Special Bonus Lecture.html |
1.23Кб |
| 1. Thank You!.mp4 |
20.98Мб |
| 1. Thank You!.srt |
1.82Кб |
| 1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 |
113.66Мб |
| 1. What Is a Computer Vision Problem and What We Are Going to Cover.srt |
20.34Кб |
| 1. What Is a Custom Dataset and What We Are Going to Cover.mp4 |
92.59Мб |
| 1. What Is a Custom Dataset and What We Are Going to Cover.srt |
14.98Кб |
| 1. What Is a Machine Learning Research Paper.mp4 |
93.94Мб |
| 1. What Is a Machine Learning Research Paper.srt |
11.73Кб |
| 1. What Is Experiment Tracking and Why Track Experiments.mp4 |
61.85Мб |
| 1. What Is Experiment Tracking and Why Track Experiments.srt |
11.27Кб |
| 1. What Is Going Modular and What We Are Going to Cover.mp4 |
100.12Мб |
| 1. What Is Going Modular and What We Are Going to Cover.srt |
18.03Кб |
| 1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 |
73.83Мб |
| 1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.srt |
14.23Кб |
| 1. Why Use Machine Learning or Deep Learning.mp4 |
13.81Мб |
| 1. Why Use Machine Learning or Deep Learning.srt |
6.22Кб |
| 10. Breaking Down Figure 1 of the ViT Paper.mp4 |
87.11Мб |
| 10. Breaking Down Figure 1 of the ViT Paper.srt |
16.94Кб |
| 10. Creating a Function to Create SummaryWriter Instances.mp4 |
80.10Мб |
| 10. Creating a Function to Create SummaryWriter Instances.srt |
14.21Кб |
| 10. Creating a Function to Setup Our Model and Transforms.mp4 |
99.61Мб |
| 10. Creating a Function to Setup Our Model and Transforms.srt |
14.32Кб |
| 10. Creating a Loss Function an Optimizer for Model 0.mp4 |
110.53Мб |
| 10. Creating a Loss Function an Optimizer for Model 0.srt |
15.32Кб |
| 10. Creating an EffNetB2 Feature Extractor Model.mp4 |
92.12Мб |
| 10. Creating an EffNetB2 Feature Extractor Model.srt |
13.10Кб |
| 10. Different Kinds of Transfer Learning.mp4 |
56.96Мб |
| 10. Different Kinds of Transfer Learning.srt |
10.73Кб |
| 10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 |
80.67Мб |
| 10. Going Modular Summary, Exercises and Extra-Curriculum.srt |
8.86Кб |
| 10. How To and How Not To Approach This Course.mp4 |
37.74Мб |
| 10. How To and How Not To Approach This Course.srt |
8.56Кб |
| 10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 |
161.05Мб |
| 10. Loss Function Optimizer and Evaluation Function for Our Classification Network.srt |
23.12Кб |
| 10. Making Predictions With Our Random Model Using Inference Mode.mp4 |
107.03Мб |
| 10. Making Predictions With Our Random Model Using Inference Mode.srt |
15.98Кб |
| 10. Visualizing a Loaded Image From the Train Dataset.mp4 |
76.72Мб |
| 10. Visualizing a Loaded Image From the Train Dataset.srt |
10.26Кб |
| 11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 |
66.53Мб |
| 11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt |
6.59Кб |
| 11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 |
140.92Мб |
| 11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt |
16.17Кб |
| 11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 |
57.59Мб |
| 11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt |
8.81Кб |
| 11. Creating a Function to Time Our Modelling Code.mp4 |
45.61Мб |
| 11. Creating a Function to Time Our Modelling Code.srt |
8.06Кб |
| 11. Discussing How to Get Better Relative Speedups for Training Models.mp4 |
70.10Мб |
| 11. Discussing How to Get Better Relative Speedups for Training Models.srt |
10.34Кб |
| 11. Getting a Summary of the Different Layers of Our Model.mp4 |
76.03Мб |
| 11. Getting a Summary of the Different Layers of Our Model.srt |
10.01Кб |
| 11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 |
134.54Мб |
| 11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt |
22.63Кб |
| 11. Important Resources For This Course.mp4 |
58.32Мб |
| 11. Important Resources For This Course.srt |
8.73Кб |
| 11. Training a Model Intuition (The Things We Need).mp4 |
69.49Мб |
| 11. Training a Model Intuition (The Things We Need).srt |
12.50Кб |
| 11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 |
84.32Мб |
| 11. Turning Our Image Datasets into PyTorch Dataloaders.srt |
12.31Кб |
| 12. Breaking Down Equation 1.mp4 |
103.21Мб |
| 12. Breaking Down Equation 1.srt |
11.96Кб |
| 12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 |
126.75Мб |
| 12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt |
22.79Кб |
| 12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 |
74.70Мб |
| 12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt |
10.38Кб |
| 12. Creating DataLoaders for EffNetB2.mp4 |
31.38Мб |
| 12. Creating DataLoaders for EffNetB2.srt |
4.75Кб |
| 12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 |
160.67Мб |
| 12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt |
19.94Кб |
| 12. Getting Setup to Write PyTorch Code.mp4 |
69.99Мб |
| 12. Getting Setup to Write PyTorch Code.srt |
11.76Кб |
| 12. Setting the Batch Size and Data Size Programmatically.mp4 |
70.98Мб |
| 12. Setting the Batch Size and Data Size Programmatically.srt |
9.96Кб |
| 12. Setting Up an Optimizer and a Loss Function.mp4 |
116.00Мб |
| 12. Setting Up an Optimizer and a Loss Function.srt |
20.30Кб |
| 12. What Experiments Should You Try.mp4 |
46.91Мб |
| 12. What Experiments Should You Try.srt |
8.51Кб |
| 12. Writing Training and Testing Loops for Our Batched Data.mp4 |
157.56Мб |
| 12. Writing Training and Testing Loops for Our Batched Data.srt |
31.15Кб |
| 13. Breaking Down Equation 2 and 3.mp4 |
125.03Мб |
| 13. Breaking Down Equation 2 and 3.srt |
14.83Кб |
| 13. Creating a Helper Function to Get Class Names From a Directory.mp4 |
79.09Мб |
| 13. Creating a Helper Function to Get Class Names From a Directory.srt |
11.89Кб |
| 13. Discussing the Experiments We Are Going to Try.mp4 |
48.29Мб |
| 13. Discussing the Experiments We Are Going to Try.srt |
8.15Кб |
| 13. Getting More Potential Speedups with TensorFloat-32.mp4 |
83.85Мб |
| 13. Getting More Potential Speedups with TensorFloat-32.srt |
13.59Кб |
| 13. Introduction to PyTorch Tensors.mp4 |
93.99Мб |
| 13. Introduction to PyTorch Tensors.srt |
20.11Кб |
| 13. PyTorch Training Loop Steps and Intuition.mp4 |
128.78Мб |
| 13. PyTorch Training Loop Steps and Intuition.srt |
21.72Кб |
| 13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 |
97.04Мб |
| 13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt |
13.95Кб |
| 13. Training Our First Transfer Learning Feature Extractor Model.mp4 |
74.80Мб |
| 13. Training Our First Transfer Learning Feature Extractor Model.srt |
11.60Кб |
| 13. Writing an Evaluation Function to Get Our Models Results.mp4 |
106.78Мб |
| 13. Writing an Evaluation Function to Get Our Models Results.srt |
20.06Кб |
| 13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 |
149.99Мб |
| 13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt |
22.84Кб |
| 14. Breaking Down Equation 4.mp4 |
92.43Мб |
| 14. Breaking Down Equation 4.srt |
10.12Кб |
| 14. Creating Random Tensors in PyTorch.mp4 |
86.42Мб |
| 14. Creating Random Tensors in PyTorch.srt |
14.34Кб |
| 14. Discussing Options to Improve a Model.mp4 |
80.86Мб |
| 14. Discussing Options to Improve a Model.srt |
13.19Кб |
| 14. Downloading Datasets for Our Modelling Experiments.mp4 |
66.41Мб |
| 14. Downloading Datasets for Our Modelling Experiments.srt |
8.90Кб |
| 14. Downloading the CIFAR10 Dataset.mp4 |
67.55Мб |
| 14. Downloading the CIFAR10 Dataset.srt |
10.23Кб |
| 14. Plotting the Loss curves of Our Transfer Learning Model.mp4 |
58.93Мб |
| 14. Plotting the Loss curves of Our Transfer Learning Model.srt |
9.35Кб |
| 14. Saving Our EffNetB2 Model to File.mp4 |
26.70Мб |
| 14. Saving Our EffNetB2 Model to File.srt |
4.29Кб |
| 14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 |
44.32Мб |
| 14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt |
6.09Кб |
| 14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 |
176.27Мб |
| 14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt |
22.92Кб |
| 14. Writing Code for a PyTorch Training Loop.mp4 |
83.00Мб |
| 14. Writing Code for a PyTorch Training Loop.srt |
13.51Кб |
| 15. Breaking Down Table 1.mp4 |
122.09Мб |
| 15. Breaking Down Table 1.srt |
15.12Кб |
| 15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 |
69.50Мб |
| 15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.srt |
9.78Кб |
| 15. Creating a New Model with More Layers and Hidden Units.mp4 |
68.82Мб |
| 15. Creating a New Model with More Layers and Hidden Units.srt |
12.30Кб |
| 15. Creating Tensors With Zeros and Ones in PyTorch.mp4 |
24.56Мб |
| 15. Creating Tensors With Zeros and Ones in PyTorch.srt |
4.51Кб |
| 15. Creating Training and Test DataLoaders.mp4 |
67.81Мб |
| 15. Creating Training and Test DataLoaders.srt |
10.95Кб |
| 15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 |
55.47Мб |
| 15. Getting the Size of Our EffNetB2 Model in Megabytes.srt |
7.13Кб |
| 15. Model 1 Creating a Model with Non-Linear Functions.mp4 |
86.38Мб |
| 15. Model 1 Creating a Model with Non-Linear Functions.srt |
13.53Кб |
| 15. Outlining the Steps to Make Predictions on the Test Images.mp4 |
66.74Мб |
| 15. Outlining the Steps to Make Predictions on the Test Images.srt |
10.46Кб |
| 15. Reviewing the Steps in a Training Loop Step by Step.mp4 |
177.45Мб |
| 15. Reviewing the Steps in a Training Loop Step by Step.srt |
23.17Кб |
| 15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 |
78.06Мб |
| 15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt |
11.29Кб |
| 16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 |
160.59Мб |
| 16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt |
20.64Кб |
| 16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 |
63.27Мб |
| 16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt |
8.86Кб |
| 16. Creating a Function Predict On and Plot Images.mp4 |
101.66Мб |
| 16. Creating a Function Predict On and Plot Images.srt |
14.20Кб |
| 16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 |
32.59Мб |
| 16. Creating a Tensor Range and Tensors Like Other Tensors.srt |
7.13Кб |
| 16. Creating Functions to Prepare Our Feature Extractor Models.mp4 |
159.20Мб |
| 16. Creating Functions to Prepare Our Feature Extractor Models.srt |
22.71Кб |
| 16. Mode 1 Creating a Loss Function and Optimizer.mp4 |
31.33Мб |
| 16. Mode 1 Creating a Loss Function and Optimizer.srt |
4.59Кб |
| 16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp4 |
60.72Мб |
| 16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.srt |
7.07Кб |
| 16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 |
101.70Мб |
| 16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt |
15.61Кб |
| 16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 |
131.21Мб |
| 16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt |
19.32Кб |
| 16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 |
118.63Мб |
| 16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.srt |
19.10Кб |
| 17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 |
127.61Мб |
| 17. Coding Out the Steps to Run a Series of Modelling Experiments.srt |
19.64Кб |
| 17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 |
61.35Мб |
| 17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt |
11.85Кб |
| 17. Creating a Vision Transformer Feature Extractor Model.mp4 |
78.51Мб |
| 17. Creating a Vision Transformer Feature Extractor Model.srt |
10.49Кб |
| 17. Dealing With Tensor Data Types.mp4 |
81.41Мб |
| 17. Dealing With Tensor Data Types.srt |
12.65Кб |
| 17. Experiment 1 - Single Run without torch.compile.mp4 |
78.14Мб |
| 17. Experiment 1 - Single Run without torch.compile.srt |
12.83Кб |
| 17. Making and Plotting Predictions on Test Images.mp4 |
78.14Мб |
| 17. Making and Plotting Predictions on Test Images.srt |
10.73Кб |
| 17. Turing Our Training Loop into a Function.mp4 |
70.88Мб |
| 17. Turing Our Training Loop into a Function.srt |
12.10Кб |
| 17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 |
150.15Мб |
| 17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt |
20.27Кб |
| 17. Turning Our Custom Datasets Into DataLoaders.mp4 |
80.62Мб |
| 17. Turning Our Custom Datasets Into DataLoaders.srt |
9.69Кб |
| 17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 |
135.03Мб |
| 17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt |
19.58Кб |
| 18. Building and Training a Model to Fit on Straight Line Data.mp4 |
71.67Мб |
| 18. Building and Training a Model to Fit on Straight Line Data.srt |
15.73Кб |
| 18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 |
19.70Мб |
| 18. Creating DataLoaders for Our ViT Feature Extractor Model.srt |
3.78Кб |
| 18. Experiment 2 - Single Run with torch.compile.mp4 |
105.61Мб |
| 18. Experiment 2 - Single Run with torch.compile.srt |
15.12Кб |
| 18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 |
166.35Мб |
| 18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt |
20.75Кб |
| 18. Getting Tensor Attributes.mp4 |
66.44Мб |
| 18. Getting Tensor Attributes.srt |
11.64Кб |
| 18. Making a Prediction on a Custom Image.mp4 |
67.83Мб |
| 18. Making a Prediction on a Custom Image.srt |
9.40Кб |
| 18. Reviewing What Happens in a Testing Loop Step by Step.mp4 |
161.56Мб |
| 18. Reviewing What Happens in a Testing Loop Step by Step.srt |
22.89Кб |
| 18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 |
45.66Мб |
| 18. Running Eight Different Modelling Experiments in 5 Minutes.srt |
6.25Кб |
| 18. Turing Our Testing Loop into a Function.mp4 |
50.89Мб |
| 18. Turing Our Testing Loop into a Function.srt |
9.63Кб |
| 18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 |
130.65Мб |
| 18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt |
16.21Кб |
| 19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 |
77.93Мб |
| 19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt |
11.64Кб |
| 19. Comparing the Results of Experiment 1 and 2.mp4 |
120.57Мб |
| 19. Comparing the Results of Experiment 1 and 2.srt |
15.46Кб |
| 19. Creating Patch Embeddings with a Convolutional Layer.mp4 |
142.62Мб |
| 19. Creating Patch Embeddings with a Convolutional Layer.srt |
18.63Кб |
| 19. Evaluating Our Models Predictions on Straight Line Data.mp4 |
50.79Мб |
| 19. Evaluating Our Models Predictions on Straight Line Data.srt |
8.64Кб |
| 19. Main Takeaways, Exercises and Extra- Curriculum.mp4 |
44.43Мб |
| 19. Main Takeaways, Exercises and Extra- Curriculum.srt |
5.22Кб |
| 19. Manipulating Tensors (Tensor Operations).mp4 |
39.70Мб |
| 19. Manipulating Tensors (Tensor Operations).srt |
8.17Кб |
| 19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 |
108.43Мб |
| 19. Training and Testing Model 1 with Our Training and Testing Functions.srt |
17.85Кб |
| 19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 |
62.00Мб |
| 19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt |
9.43Кб |
| 19. Viewing Our Modelling Experiments in TensorBoard.mp4 |
140.29Мб |
| 19. Viewing Our Modelling Experiments in TensorBoard.srt |
19.65Кб |
| 19. Writing Code to Save a PyTorch Model.mp4 |
129.82Мб |
| 19. Writing Code to Save a PyTorch Model.srt |
21.63Кб |
| 2.1 PyTorch 2.0 tutorial on learnpytorch.io.html |
105б |
| 2. Become An Alumni.html |
921б |
| 2. Classification Problem Example Input and Output Shapes.mp4 |
49.96Мб |
| 2. Classification Problem Example Input and Output Shapes.srt |
14.54Кб |
| 2. Computer Vision Input and Output Shapes.mp4 |
85.01Мб |
| 2. Computer Vision Input and Output Shapes.srt |
16.51Кб |
| 2. Course Welcome and What Is Deep Learning.mp4 |
38.99Мб |
| 2. Course Welcome and What Is Deep Learning.srt |
8.57Кб |
| 2. Getting Setup and What We Are Covering.mp4 |
69.68Мб |
| 2. Getting Setup and What We Are Covering.srt |
11.32Кб |
| 2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 |
93.39Мб |
| 2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt |
12.45Кб |
| 2. Going Modular Notebook (Part 1) Running It End to End.mp4 |
104.92Мб |
| 2. Going Modular Notebook (Part 1) Running It End to End.srt |
11.49Кб |
| 2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 |
48.96Мб |
| 2. Importing PyTorch and Setting Up Device Agnostic Code.srt |
7.82Кб |
| 2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 |
35.33Мб |
| 2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt |
9.49Кб |
| 2. Three Questions to Ask for Machine Learning Model Deployment.mp4 |
46.93Мб |
| 2. Three Questions to Ask for Machine Learning Model Deployment.srt |
11.63Кб |
| 2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 |
15.08Мб |
| 2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt |
2.32Кб |
| 2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 |
55.86Мб |
| 2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt |
8.33Кб |
| 2. Why Replicate a Machine Learning Research Paper.mp4 |
23.26Мб |
| 2. Why Replicate a Machine Learning Research Paper.srt |
4.87Кб |
| 20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 |
117.22Мб |
| 20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt |
15.62Кб |
| 20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 |
129.06Мб |
| 20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt |
17.95Кб |
| 20. Getting a Results Dictionary for Model 1.mp4 |
41.35Мб |
| 20. Getting a Results Dictionary for Model 1.srt |
6.13Кб |
| 20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 |
96.52Мб |
| 20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt |
15.72Кб |
| 20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 |
99.19Мб |
| 20. Loading the Best Model and Making Predictions on Random Images from the Test Set.srt |
14.80Кб |
| 20. Matrix Multiplication (Part 1).mp4 |
77.80Мб |
| 20. Matrix Multiplication (Part 1).srt |
12.67Кб |
| 20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 |
43.77Мб |
| 20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt |
6.69Кб |
| 20. Saving the Results of Experiment 1 and 2.mp4 |
58.03Мб |
| 20. Saving the Results of Experiment 1 and 2.srt |
6.60Кб |
| 20. Writing Code to Load a PyTorch Model.mp4 |
79.57Мб |
| 20. Writing Code to Load a PyTorch Model.srt |
12.61Кб |
| 21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 |
96.49Мб |
| 21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.srt |
12.05Кб |
| 21. Building Our First Neural Network with Non-Linearity.mp4 |
92.59Мб |
| 21. Building Our First Neural Network with Non-Linearity.srt |
15.53Кб |
| 21. Collecting Stats About Our-ViT Feature Extractor.mp4 |
45.85Мб |
| 21. Collecting Stats About Our-ViT Feature Extractor.srt |
8.49Кб |
| 21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 |
89.61Мб |
| 21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt |
13.21Кб |
| 21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 |
39.71Мб |
| 21. Making a Prediction on Our Own Custom Image with the Best Model.srt |
5.85Кб |
| 21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 |
57.77Мб |
| 21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt |
11.44Кб |
| 21. Model 2 Convolutional Neural Networks High Level Overview.mp4 |
94.62Мб |
| 21. Model 2 Convolutional Neural Networks High Level Overview.srt |
13.29Кб |
| 21. Preparing Functions for Experiment 3 and 4.mp4 |
116.28Мб |
| 21. Preparing Functions for Experiment 3 and 4.srt |
17.66Кб |
| 21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 |
45.79Мб |
| 21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.srt |
9.43Кб |
| 22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 |
132.79Мб |
| 22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt |
16.57Кб |
| 22. Main Takeaways, Exercises and Extra- Curriculum.mp4 |
43.60Мб |
| 22. Main Takeaways, Exercises and Extra- Curriculum.srt |
6.59Кб |
| 22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 |
97.34Мб |
| 22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt |
17.62Кб |
| 22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 |
208.33Мб |
| 22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt |
30.94Кб |
| 22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 |
93.41Мб |
| 22. Outlining the Steps for Making and Timing Predictions for Our Models.srt |
14.01Кб |
| 22. Putting Everything Together (Part 1) Data.mp4 |
49.34Мб |
| 22. Putting Everything Together (Part 1) Data.srt |
9.28Кб |
| 22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 |
64.97Мб |
| 22. Using the Torchinfo Package to Get a Summary of Our Model.srt |
9.48Кб |
| 22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 |
50.37Мб |
| 22. Visualizing a Single Sequence Vector of Patch Embeddings.srt |
6.91Кб |
| 22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 |
150.56Мб |
| 22. Writing Training and Testing Code for Our First Non-Linear Model.srt |
22.88Кб |
| 23. Creating a Function to Make and Time Predictions with Our Models.mp4 |
185.77Мб |
| 23. Creating a Function to Make and Time Predictions with Our Models.srt |
24.14Кб |
| 23. Creating the Patch Embedding Layer with PyTorch.mp4 |
170.03Мб |
| 23. Creating the Patch Embedding Layer with PyTorch.srt |
22.79Кб |
| 23. Creating Training and Testing loop Functions.mp4 |
106.16Мб |
| 23. Creating Training and Testing loop Functions.srt |
17.45Кб |
| 23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 |
104.98Мб |
| 23. Experiment 4 - Training a Compiled Model for Multiple Runs.srt |
13.98Кб |
| 23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 |
48.15Мб |
| 23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt |
8.42Кб |
| 23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 |
53.04Мб |
| 23. Making Predictions with and Evaluating Our First Non-Linear Model.srt |
8.67Кб |
| 23. Model 2 Breaking Down Conv2D Step by Step.mp4 |
162.71Мб |
| 23. Model 2 Breaking Down Conv2D Step by Step.srt |
23.64Кб |
| 23. Putting Everything Together (Part 2) Building a Model.mp4 |
88.69Мб |
| 23. Putting Everything Together (Part 2) Building a Model.srt |
13.59Кб |
| 24. Comparing the Results of Experiment 3 and 4.mp4 |
62.82Мб |
| 24. Comparing the Results of Experiment 3 and 4.srt |
8.07Кб |
| 24. Creating a Train Function to Train and Evaluate Our Models.mp4 |
103.47Мб |
| 24. Creating a Train Function to Train and Evaluate Our Models.srt |
15.61Кб |
| 24. Creating the Class Token Embedding.mp4 |
131.98Мб |
| 24. Creating the Class Token Embedding.srt |
17.52Кб |
| 24. Finding The Positional Min and Max of Tensors.mp4 |
24.49Мб |
| 24. Finding The Positional Min and Max of Tensors.srt |
3.95Кб |
| 24. Making and Timing Predictions with EffNetB2.mp4 |
97.62Мб |
| 24. Making and Timing Predictions with EffNetB2.srt |
13.66Кб |
| 24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 |
158.10Мб |
| 24. Model 2 Breaking Down MaxPool2D Step by Step.srt |
22.73Кб |
| 24. Putting Everything Together (Part 3) Training a Model.mp4 |
102.99Мб |
| 24. Putting Everything Together (Part 3) Training a Model.srt |
19.87Кб |
| 24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 |
80.74Мб |
| 24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt |
14.66Кб |
| 25. Creating the Class Token Embedding - Less Birds.mp4 |
131.91Мб |
| 25. Creating the Class Token Embedding - Less Birds.srt |
17.66Кб |
| 25. Making and Timing Predictions with ViT.mp4 |
72.47Мб |
| 25. Making and Timing Predictions with ViT.srt |
9.67Кб |
| 25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 |
174.82Мб |
| 25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt |
20.13Кб |
| 25. Potential Extensions and Resources to Learn More.mp4 |
64.06Мб |
| 25. Potential Extensions and Resources to Learn More.srt |
8.89Кб |
| 25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 |
50.63Мб |
| 25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt |
8.13Кб |
| 25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 |
97.45Мб |
| 25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt |
17.88Кб |
| 25. Reshaping, Viewing and Stacking Tensors.mp4 |
103.95Мб |
| 25. Reshaping, Viewing and Stacking Tensors.srt |
20.27Кб |
| 25. Training and Evaluating Model 0 With Our Training Functions.mp4 |
89.27Мб |
| 25. Training and Evaluating Model 0 With Our Training Functions.srt |
14.66Кб |
| 26. Comparing EffNetB2 and ViT Model Statistics.mp4 |
89.62Мб |
| 26. Comparing EffNetB2 and ViT Model Statistics.srt |
14.40Кб |
| 26. Creating a Multi-Class Classification Model with PyTorch.mp4 |
107.43Мб |
| 26. Creating a Multi-Class Classification Model with PyTorch.srt |
18.27Кб |
| 26. Creating the Position Embedding.mp4 |
109.18Мб |
| 26. Creating the Position Embedding.srt |
16.70Кб |
| 26. Model 2 Setting Up a Loss Function and Optimizer.mp4 |
27.87Мб |
| 26. Model 2 Setting Up a Loss Function and Optimizer.srt |
3.58Кб |
| 26. Plotting the Loss Curves of Model 0.mp4 |
89.44Мб |
| 26. Plotting the Loss Curves of Model 0.srt |
12.46Кб |
| 26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 |
72.53Мб |
| 26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt |
13.89Кб |
| 26. Squeezing, Unsqueezing and Permuting Tensors.mp4 |
88.41Мб |
| 26. Squeezing, Unsqueezing and Permuting Tensors.srt |
16.79Кб |
| 27. Equation 1 Putting it All Together.mp4 |
134.81Мб |
| 27. Equation 1 Putting it All Together.srt |
18.49Кб |
| 27. Exercise Imposter Syndrome.mp4 |
39.25Мб |
| 27. Exercise Imposter Syndrome.srt |
4.50Кб |
| 27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 |
76.78Мб |
| 27. Model 2 Training Our First CNN and Evaluating Its Results.srt |
11.81Кб |
| 27. Selecting Data From Tensors (Indexing).mp4 |
56.95Мб |
| 27. Selecting Data From Tensors (Indexing).srt |
13.11Кб |
| 27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 |
65.06Мб |
| 27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt |
10.13Кб |
| 27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 |
131.81Мб |
| 27. The Balance Between Overfitting and Underfitting and How to Deal With Each.srt |
21.57Кб |
| 27. Visualizing the Performance vs Speed Trade-off.mp4 |
134.66Мб |
| 27. Visualizing the Performance vs Speed Trade-off.srt |
21.62Кб |
| 28. Comparing the Results of Our Modelling Experiments.mp4 |
61.75Мб |
| 28. Comparing the Results of Our Modelling Experiments.srt |
10.99Кб |
| 28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 |
98.83Мб |
| 28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt |
15.05Кб |
| 28. Equation 2 Multihead Attention Overview.mp4 |
144.10Мб |
| 28. Equation 2 Multihead Attention Overview.srt |
21.60Кб |
| 28. Gradio Overview and Installation.mp4 |
95.13Мб |
| 28. Gradio Overview and Installation.srt |
13.08Кб |
| 28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 |
97.04Мб |
| 28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt |
16.75Кб |
| 28. PyTorch Tensors and NumPy.mp4 |
59.77Мб |
| 28. PyTorch Tensors and NumPy.srt |
11.81Кб |
| 28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 |
49.31Мб |
| 28. PyTorch Workflow Exercises and Extra-Curriculum.srt |
6.38Кб |
| 29. Constructing and Training Model 1.mp4 |
60.64Мб |
| 29. Constructing and Training Model 1.srt |
9.48Кб |
| 29. Equation 2 Layernorm Overview.mp4 |
111.75Мб |
| 29. Equation 2 Layernorm Overview.srt |
12.79Кб |
| 29. Gradio Function Outline.mp4 |
79.89Мб |
| 29. Gradio Function Outline.srt |
11.50Кб |
| 29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 |
83.66Мб |
| 29. Making Predictions on Random Test Samples with the Best Trained Model.srt |
16.16Кб |
| 29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 |
95.11Мб |
| 29. PyTorch Reproducibility (Taking the Random Out of Random).srt |
14.93Кб |
| 29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 |
150.08Мб |
| 29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt |
24.98Кб |
| 3.1 PyTorch 2.0 tutorial on learnpytorch.io.html |
105б |
| 3. Creating a Function to Download Data.mp4 |
95.22Мб |
| 3. Creating a Function to Download Data.srt |
14.56Кб |
| 3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 |
68.66Мб |
| 3. Creating a Simple Dataset Using the Linear Regression Formula.srt |
13.93Кб |
| 3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 |
150.95Мб |
| 3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt |
19.13Кб |
| 3. Downloading a Dataset.mp4 |
67.63Мб |
| 3. Downloading a Dataset.srt |
7.15Кб |
| 3. Endorsements on LinkedIn.html |
1.37Кб |
| 3. Getting Started with PyTorch 2 in Google Colab.mp4 |
44.58Мб |
| 3. Getting Started with PyTorch 2 in Google Colab.srt |
6.46Кб |
| 3. Installing the Latest Versions of Torch and Torchvision.mp4 |
82.39Мб |
| 3. Installing the Latest Versions of Torch and Torchvision.srt |
11.13Кб |
| 3. Join Our Online Classroom!.mp4 |
75.34Мб |
| 3. Join Our Online Classroom!.srt |
5.95Кб |
| 3. Machine Learning vs. Deep Learning.mp4 |
55.29Мб |
| 3. Machine Learning vs. Deep Learning.srt |
9.67Кб |
| 3. Typical Architecture of a Classification Neural Network (Overview).mp4 |
67.04Мб |
| 3. Typical Architecture of a Classification Neural Network (Overview).srt |
10.00Кб |
| 3. What Is a Convolutional Neural Network (CNN).mp4 |
55.40Мб |
| 3. What Is a Convolutional Neural Network (CNN).srt |
8.12Кб |
| 3. Where Can You Find Machine Learning Research Papers and Code.mp4 |
110.76Мб |
| 3. Where Can You Find Machine Learning Research Papers and Code.srt |
13.31Кб |
| 3. Where Is My Model Going to Go.mp4 |
139.84Мб |
| 3. Where Is My Model Going to Go.srt |
21.38Кб |
| 30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 |
95.22Мб |
| 30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt |
13.77Кб |
| 30. Different Ways of Accessing a GPU in PyTorch.mp4 |
113.01Мб |
| 30. Different Ways of Accessing a GPU in PyTorch.srt |
14.54Кб |
| 30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 |
77.04Мб |
| 30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt |
13.17Кб |
| 30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 |
63.48Мб |
| 30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt |
12.24Кб |
| 30. Plotting the Loss Curves of Model 1.mp4 |
31.69Мб |
| 30. Plotting the Loss Curves of Model 1.srt |
5.13Кб |
| 30. Turning Equation 2 into Code.mp4 |
163.86Мб |
| 30. Turning Equation 2 into Code.srt |
20.77Кб |
| 31. Checking the Inputs and Outputs of Equation.mp4 |
53.69Мб |
| 31. Checking the Inputs and Outputs of Equation.srt |
7.77Кб |
| 31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 |
53.30Мб |
| 31. Creating a List of Examples to Pass to Our Gradio Demo.srt |
6.83Кб |
| 31. Discussing a Few More Classification Metrics.mp4 |
97.54Мб |
| 31. Discussing a Few More Classification Metrics.srt |
13.67Кб |
| 31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 |
160.84Мб |
| 31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.srt |
21.60Кб |
| 31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 |
89.26Мб |
| 31. Plotting the Loss Curves of All of Our Models Against Each Other.srt |
15.79Кб |
| 31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 |
64.51Мб |
| 31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.srt |
10.45Кб |
| 32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 |
135.38Мб |
| 32. Bringing Food Vision Mini to Life in a Live Web Application.srt |
18.71Кб |
| 32. Equation 3 Replication Overview.mp4 |
88.70Мб |
| 32. Equation 3 Replication Overview.srt |
12.23Кб |
| 32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 |
67.00Мб |
| 32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt |
9.97Кб |
| 32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 |
51.66Мб |
| 32. Predicting on Custom Data (Part 1) Downloading an Image.srt |
7.68Кб |
| 32. PyTorch Classification Exercises and Extra-Curriculum.mp4 |
41.46Мб |
| 32. PyTorch Classification Exercises and Extra-Curriculum.srt |
4.35Кб |
| 32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 |
56.76Мб |
| 32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt |
7.46Кб |
| 33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 |
64.81Мб |
| 33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt |
8.57Кб |
| 33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 |
68.00Мб |
| 33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.srt |
10.72Кб |
| 33. Saving and Loading Our Best Performing Model.mp4 |
98.15Мб |
| 33. Saving and Loading Our Best Performing Model.srt |
17.15Кб |
| 33. Turning Equation 3 into Code.mp4 |
107.07Мб |
| 33. Turning Equation 3 into Code.srt |
14.89Кб |
| 34. Outlining the File Structure of Our Deployed App.mp4 |
89.53Мб |
| 34. Outlining the File Structure of Our Deployed App.srt |
11.02Кб |
| 34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 |
127.05Мб |
| 34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.srt |
19.66Кб |
| 34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 |
81.89Мб |
| 34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt |
9.40Кб |
| 34. Transformer Encoder Overview.mp4 |
82.85Мб |
| 34. Transformer Encoder Overview.srt |
10.82Кб |
| 35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 |
84.87Мб |
| 35. Combining equation 2 and 3 to Create the Transformer Encoder.srt |
12.65Кб |
| 35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 |
39.14Мб |
| 35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt |
5.68Кб |
| 35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 |
36.06Мб |
| 35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.srt |
5.92Кб |
| 36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 |
92.40Мб |
| 36. Creating an Examples Directory with Example Food Vision Mini Images.srt |
12.88Кб |
| 36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 |
188.74Мб |
| 36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt |
21.27Кб |
| 36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 |
113.03Мб |
| 36. Predicting on Custom Data (Part 5) Putting It All Together.srt |
18.37Кб |
| 37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 |
190.81Мб |
| 37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.srt |
26.26Кб |
| 37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 |
73.32Мб |
| 37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt |
9.28Кб |
| 37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 |
71.91Мб |
| 37. Writing Code to Move Our Saved EffNetB2 Model File.srt |
10.06Кб |
| 38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 |
111.37Мб |
| 38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.srt |
14.88Кб |
| 38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 |
44.78Мб |
| 38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt |
5.33Кб |
| 39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 |
84.89Мб |
| 39. Getting a Visual Summary of Our Custom Vision Transformer.srt |
10.85Кб |
| 39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 |
137.62Мб |
| 39. Turning Our Food Vision Mini Demo App Into a Python Script.srt |
18.71Кб |
| 4. Anatomy of Neural Networks.mp4 |
70.32Мб |
| 4. Anatomy of Neural Networks.srt |
14.52Кб |
| 4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 |
87.61Мб |
| 4. Becoming One With the Data (Part 1) Exploring the Data Format.srt |
12.10Кб |
| 4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 |
89.19Мб |
| 4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt |
14.67Кб |
| 4. Downloading Our Previously Written Code from Going Modular.mp4 |
83.74Мб |
| 4. Downloading Our Previously Written Code from Going Modular.srt |
10.28Кб |
| 4. Exercise Meet Your Classmates + Instructor.html |
3.79Кб |
| 4. How Is My Model Going to Function.mp4 |
67.36Мб |
| 4. How Is My Model Going to Function.srt |
12.24Кб |
| 4. Learning Guideline.html |
353б |
| 4. Making a Toy Classification Dataset.mp4 |
91.48Мб |
| 4. Making a Toy Classification Dataset.srt |
17.99Кб |
| 4. PyTorch 2.0 - 30 Second Intro.mp4 |
22.40Мб |
| 4. PyTorch 2.0 - 30 Second Intro.srt |
4.91Кб |
| 4. Splitting Our Data Into Training and Test Sets.mp4 |
65.21Мб |
| 4. Splitting Our Data Into Training and Test Sets.srt |
11.91Кб |
| 4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 |
92.72Мб |
| 4. Turning Our Data into DataLoaders Using Manual Transforms.srt |
12.34Кб |
| 4. What We Are Going to Cover.mp4 |
87.76Мб |
| 4. What We Are Going to Cover.srt |
13.12Кб |
| 4. Writing the Outline for Our First Python Script to Setup the Data.mp4 |
156.79Мб |
| 4. Writing the Outline for Our First Python Script to Setup the Data.srt |
18.93Кб |
| 40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 |
118.33Мб |
| 40. Creating a Loss Function and Optimizer from the ViT Paper.srt |
16.20Кб |
| 40. Creating a Requirements File for Our Food Vision Mini App.mp4 |
37.50Мб |
| 40. Creating a Requirements File for Our Food Vision Mini App.srt |
6.21Кб |
| 41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 |
112.22Мб |
| 41. Downloading Our Food Vision Mini App Files from Google Colab.srt |
16.15Кб |
| 41. Training our Custom ViT on Food Vision Mini.mp4 |
53.47Мб |
| 41. Training our Custom ViT on Food Vision Mini.srt |
7.02Кб |
| 42. Discussing what Our Training Setup Is Missing.mp4 |
101.19Мб |
| 42. Discussing what Our Training Setup Is Missing.srt |
12.65Кб |
| 42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 |
143.59Мб |
| 42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt |
20.83Кб |
| 43. Plotting a Loss Curve for Our ViT Model.mp4 |
63.39Мб |
| 43. Plotting a Loss Curve for Our ViT Model.srt |
8.69Кб |
| 43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 |
91.60Мб |
| 43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt |
12.54Кб |
| 44. Food Vision Big Project Outline.mp4 |
39.14Мб |
| 44. Food Vision Big Project Outline.srt |
5.58Кб |
| 44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 |
164.75Мб |
| 44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt |
19.89Кб |
| 45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 |
96.52Мб |
| 45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt |
13.48Кб |
| 45. Preparing Data to Be Used with a Pretrained ViT.mp4 |
57.21Мб |
| 45. Preparing Data to Be Used with a Pretrained ViT.srt |
7.22Кб |
| 46. Downloading the Food 101 Dataset.mp4 |
71.66Мб |
| 46. Downloading the Food 101 Dataset.srt |
11.05Кб |
| 46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 |
76.28Мб |
| 46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt |
10.31Кб |
| 47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 |
119.73Мб |
| 47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt |
18.04Кб |
| 47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 |
40.36Мб |
| 47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt |
6.39Кб |
| 48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 |
41.81Мб |
| 48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt |
5.45Кб |
| 48. Turning Our Food 101 Datasets into DataLoaders.mp4 |
61.50Мб |
| 48. Turning Our Food 101 Datasets into DataLoaders.srt |
9.55Кб |
| 49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 |
37.11Мб |
| 49. Making Predictions on a Custom Image with Our Pretrained ViT.srt |
5.07Кб |
| 49. Training Food Vision Big Our Biggest Model Yet!.mp4 |
184.21Мб |
| 49. Training Food Vision Big Our Biggest Model Yet!.srt |
27.99Кб |
| 5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 |
115.33Мб |
| 5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt |
17.22Кб |
| 5. Building a function to Visualize Our Data.mp4 |
61.89Мб |
| 5. Building a function to Visualize Our Data.srt |
12.23Кб |
| 5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 |
135.14Мб |
| 5. Creating a Python Script to Create Our PyTorch DataLoaders.srt |
15.87Кб |
| 5. Different Types of Learning Paradigms.mp4 |
27.04Мб |
| 5. Different Types of Learning Paradigms.srt |
6.79Кб |
| 5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 |
72.16Мб |
| 5. Downloading Pizza, Steak, Sushi Image Data from Github.srt |
11.23Кб |
| 5. Free Course Book + Code Resources + Asking Questions + Getting Help.html |
2.37Кб |
| 5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 |
153.99Мб |
| 5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt |
23.78Кб |
| 5. Getting Setup for Coding in Google Colab.mp4 |
99.13Мб |
| 5. Getting Setup for Coding in Google Colab.srt |
11.91Кб |
| 5. Getting Setup for PyTorch 2.mp4 |
27.14Мб |
| 5. Getting Setup for PyTorch 2.srt |
3.35Кб |
| 5. Some Tools and Places to Deploy Machine Learning Models.mp4 |
65.36Мб |
| 5. Some Tools and Places to Deploy Machine Learning Models.srt |
8.85Кб |
| 5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 |
82.00Мб |
| 5. Turning Our Data into DataLoaders Using Automatic Transforms.srt |
11.10Кб |
| 5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 |
81.06Мб |
| 5. Turning Our Data into Tensors and Making a Training and Test Split.srt |
17.78Кб |
| 50. Outlining the File Structure for Our Food Vision Big.mp4 |
52.77Мб |
| 50. Outlining the File Structure for Our Food Vision Big.srt |
8.24Кб |
| 50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 |
85.48Мб |
| 50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt |
10.66Кб |
| 51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 |
36.59Мб |
| 51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt |
5.20Кб |
| 52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 |
66.82Мб |
| 52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt |
9.34Кб |
| 53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 |
23.90Мб |
| 53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt |
3.19Кб |
| 54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 |
104.81Мб |
| 54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt |
14.63Кб |
| 55. Zipping and Downloading Our Food Vision Big App Files.mp4 |
39.75Мб |
| 55. Zipping and Downloading Our Food Vision Big App Files.srt |
5.24Кб |
| 56. Deploying Food Vision Big to Hugging Face Spaces.mp4 |
162.52Мб |
| 56. Deploying Food Vision Big to Hugging Face Spaces.srt |
19.70Кб |
| 57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 |
81.75Мб |
| 57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt |
9.45Кб |
| 6.1 LinkedIn Group.html |
102б |
| 6.2 zerotomastery.io.html |
86б |
| 6.3 ZTM Youtube.html |
99б |
| 6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 |
51.91Мб |
| 6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt |
7.05Кб |
| 6. Creating Our First PyTorch Model for Linear Regression.mp4 |
130.08Мб |
| 6. Creating Our First PyTorch Model for Linear Regression.srt |
18.28Кб |
| 6. Downloading Data for Food Vision Mini.mp4 |
43.84Мб |
| 6. Downloading Data for Food Vision Mini.srt |
6.17Кб |
| 6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 |
77.55Мб |
| 6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.srt |
8.90Кб |
| 6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 |
31.91Мб |
| 6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt |
6.51Кб |
| 6. Preparing a Pretrained Model for Our Own Problem.mp4 |
113.16Мб |
| 6. Preparing a Pretrained Model for Our Own Problem.srt |
15.66Кб |
| 6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 |
141.48Мб |
| 6. Turning Our Data into DataLoaders with Manually Created Transforms.srt |
19.37Кб |
| 6. Turning Our Model Building Code into a Python Script.mp4 |
115.12Мб |
| 6. Turning Our Model Building Code into a Python Script.srt |
13.40Кб |
| 6. Visualizing Random Samples of Data.mp4 |
68.11Мб |
| 6. Visualizing Random Samples of Data.srt |
15.53Кб |
| 6. What Can Deep Learning Be Used For.mp4 |
43.19Мб |
| 6. What Can Deep Learning Be Used For.srt |
11.11Кб |
| 6. What We Are Going to Cover.mp4 |
40.82Мб |
| 6. What We Are Going to Cover.srt |
7.21Кб |
| 6. ZTM Resources.mp4 |
44.57Мб |
| 6. ZTM Resources.srt |
6.31Кб |
| 7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 |
62.18Мб |
| 7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt |
8.77Кб |
| 7. Coding a Small Neural Network to Handle Our Classification Data.mp4 |
86.84Мб |
| 7. Coding a Small Neural Network to Handle Our Classification Data.srt |
15.79Кб |
| 7. DataLoader Overview Understanding Mini-Batches.mp4 |
60.20Мб |
| 7. DataLoader Overview Understanding Mini-Batches.srt |
10.40Кб |
| 7. Getting Setup to Code.mp4 |
62.89Мб |
| 7. Getting Setup to Code.srt |
8.79Кб |
| 7. Machine Learning + Python Monthly Newsletters.html |
1.96Кб |
| 7. Setting the Default Device in PyTorch 2.mp4 |
102.96Мб |
| 7. Setting the Default Device in PyTorch 2.srt |
13.87Кб |
| 7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 |
150.28Мб |
| 7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.srt |
20.04Кб |
| 7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 |
81.71Мб |
| 7. Transforming Data (Part 1) Turning Images Into Tensors.srt |
11.70Кб |
| 7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 |
139.74Мб |
| 7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt |
18.42Кб |
| 7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 |
89.70Мб |
| 7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt |
13.69Кб |
| 7. Turning Our Model Training Code into a Python Script.mp4 |
80.00Мб |
| 7. Turning Our Model Training Code into a Python Script.srt |
8.65Кб |
| 7. What Is and Why PyTorch.mp4 |
113.55Мб |
| 7. What Is and Why PyTorch.srt |
15.63Кб |
| 8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 |
74.44Мб |
| 8. Discussing Some of the Most Important PyTorch Model Building Classes.srt |
8.67Кб |
| 8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 |
57.55Мб |
| 8. Discussing the Experiments We Are Going to Run for PyTorch 2.srt |
9.53Кб |
| 8. Downloading a Dataset for Food Vision Mini.mp4 |
39.25Мб |
| 8. Downloading a Dataset for Food Vision Mini.srt |
4.84Кб |
| 8. Making Our Neural Network Visual.mp4 |
91.28Мб |
| 8. Making Our Neural Network Visual.srt |
11.04Кб |
| 8. Training a Single Model and Saving the Results to TensorBoard.mp4 |
41.79Мб |
| 8. Training a Single Model and Saving the Results to TensorBoard.srt |
6.70Кб |
| 8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 |
127.58Мб |
| 8. Transforming Data (Part 2) Visualizing Transformed Images.srt |
16.68Кб |
| 8. Turning Our Datasets Into DataLoaders.mp4 |
100.23Мб |
| 8. Turning Our Datasets Into DataLoaders.srt |
19.36Кб |
| 8. Turning Our Utility Function to Save a Model into a Python Script.mp4 |
75.79Мб |
| 8. Turning Our Utility Function to Save a Model into a Python Script.srt |
9.04Кб |
| 8. Visualizing a Single Image.mp4 |
36.44Мб |
| 8. Visualizing a Single Image.srt |
5.34Кб |
| 8. What Are Tensors.mp4 |
24.98Мб |
| 8. What Are Tensors.srt |
6.72Кб |
| 8. Which Pretrained Model Should You Use.mp4 |
128.78Мб |
| 8. Which Pretrained Model Should You Use.srt |
17.67Кб |
| 9. Checking Out the Internals of Our PyTorch Model.mp4 |
102.71Мб |
| 9. Checking Out the Internals of Our PyTorch Model.srt |
14.75Кб |
| 9. Creating a Training Script to Train Our Model in One Line of Code.mp4 |
165.53Мб |
| 9. Creating a Training Script to Train Our Model in One Line of Code.srt |
21.90Кб |
| 9. Exploring Our Single Models Results with TensorBoard.mp4 |
116.27Мб |
| 9. Exploring Our Single Models Results with TensorBoard.srt |
16.70Кб |
| 9. Introduction to PyTorch 2.mp4 |
82.13Мб |
| 9. Introduction to PyTorch 2.srt |
8.51Кб |
| 9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 |
98.16Мб |
| 9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt |
13.29Кб |
| 9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 |
136.88Мб |
| 9. Model 0 Creating a Baseline Model with Two Linear Layers.srt |
21.65Кб |
| 9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 |
58.55Мб |
| 9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt |
10.84Кб |
| 9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 |
123.24Мб |
| 9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt |
20.67Кб |
| 9. Replicating a Vision Transformer - High Level Overview.mp4 |
77.83Мб |
| 9. Replicating a Vision Transformer - High Level Overview.srt |
13.55Кб |
| 9. Setting Up a Pretrained Model with Torchvision.mp4 |
113.14Мб |
| 9. Setting Up a Pretrained Model with Torchvision.srt |
16.59Кб |
| 9. What We Are Going To Cover With PyTorch.mp4 |
50.45Мб |
| 9. What We Are Going To Cover With PyTorch.srt |
10.63Кб |