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Please note that this page does not hosts or makes available any of the listed filenames. You
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| 1. Introduction and Where You Can Get Help.mp4 |
28.60MB |
| 1. Introduction to Machine Learning Classification With PyTorch.mp4 |
84.58MB |
| 1. Introduction What is Transfer Learning and Why Use It.mp4 |
97.26MB |
| 1. PyTorch for Deep Learning.mp4 |
75.35MB |
| 1. Thank You!.mp4 |
20.99MB |
| 1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 |
113.67MB |
| 1. What Is a Custom Dataset and What We Are Going to Cover.mp4 |
92.59MB |
| 1. What Is a Machine Learning Research Paper.mp4 |
93.94MB |
| 1. What Is Experiment Tracking and Why Track Experiments.mp4 |
61.86MB |
| 1. What Is Going Modular and What We Are Going to Cover.mp4 |
100.12MB |
| 1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 |
73.84MB |
| 1. Why Use Machine Learning or Deep Learning.mp4 |
13.80MB |
| 10. Breaking Down Figure 1 of the ViT Paper.mp4 |
87.12MB |
| 10. Creating a Function to Create SummaryWriter Instances.mp4 |
80.10MB |
| 10. Creating a Loss Function an Optimizer for Model 0.mp4 |
110.54MB |
| 10. Creating an EffNetB2 Feature Extractor Model.mp4 |
92.12MB |
| 10. Different Kinds of Transfer Learning.mp4 |
56.96MB |
| 10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 |
80.67MB |
| 10. How To and How Not To Approach This Course.mp4 |
37.74MB |
| 10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 |
161.06MB |
| 10. Making Predictions With Our Random Model Using Inference Mode.mp4 |
107.03MB |
| 10. Visualizing a Loaded Image From the Train Dataset.mp4 |
76.73MB |
| 11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 |
66.54MB |
| 11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 |
140.93MB |
| 11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 |
57.60MB |
| 11. Creating a Function to Time Our Modelling Code.mp4 |
45.61MB |
| 11. Getting a Summary of the Different Layers of Our Model.mp4 |
76.04MB |
| 11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 |
134.54MB |
| 11. Important Resources For This Course.mp4 |
58.31MB |
| 11. Training a Model Intuition (The Things We Need).mp4 |
69.50MB |
| 11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 |
84.33MB |
| 12. Breaking Down Equation 1.mp4 |
103.22MB |
| 12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 |
126.75MB |
| 12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 |
74.70MB |
| 12. Creating DataLoaders for EffNetB2.mp4 |
31.38MB |
| 12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 |
160.67MB |
| 12. Getting Setup to Write PyTorch Code.mp4 |
70.00MB |
| 12. Setting Up an Optimizer and a Loss Function.mp4 |
116.00MB |
| 12. What Experiments Should You Try.mp4 |
46.92MB |
| 12. Writing Training and Testing Loops for Our Batched Data.mp4 |
157.56MB |
| 13. Breaking Down Equation 2 and 3.mp4 |
125.04MB |
| 13. Creating a Helper Function to Get Class Names From a Directory.mp4 |
79.09MB |
| 13. Discussing the Experiments We Are Going to Try.mp4 |
48.30MB |
| 13. Introduction to PyTorch Tensors.mp4 |
94.00MB |
| 13. PyTorch Training Loop Steps and Intuition.mp4 |
128.78MB |
| 13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 |
97.04MB |
| 13. Training Our First Transfer Learning Feature Extractor Model.mp4 |
74.81MB |
| 13. Writing an Evaluation Function to Get Our Models Results.mp4 |
106.79MB |
| 13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 |
149.99MB |
| 14. Breaking Down Equation 4.mp4 |
92.44MB |
| 14. Creating Random Tensors in PyTorch.mp4 |
86.42MB |
| 14. Discussing Options to Improve a Model.mp4 |
80.87MB |
| 14. Downloading Datasets for Our Modelling Experiments.mp4 |
66.42MB |
| 14. Plotting the Loss curves of Our Transfer Learning Model.mp4 |
58.93MB |
| 14. Saving Our EffNetB2 Model to File.mp4 |
26.71MB |
| 14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 |
44.32MB |
| 14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 |
176.28MB |
| 14. Writing Code for a PyTorch Training Loop.mp4 |
83.00MB |
| 15. Breaking Down Table 1.mp4 |
122.08MB |
| 15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 |
69.50MB |
| 15. Creating a New Model with More Layers and Hidden Units.mp4 |
68.81MB |
| 15. Creating Tensors With Zeros and Ones in PyTorch.mp4 |
24.56MB |
| 15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 |
55.48MB |
| 15. Model 1 Creating a Model with Non-Linear Functions.mp4 |
86.39MB |
| 15. Outlining the Steps to Make Predictions on the Test Images.mp4 |
66.74MB |
| 15. Reviewing the Steps in a Training Loop Step by Step.mp4 |
177.46MB |
| 15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 |
78.07MB |
| 16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 |
160.60MB |
| 16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 |
63.27MB |
| 16. Creating a Function Predict On and Plot Images.mp4 |
101.67MB |
| 16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 |
32.59MB |
| 16. Creating Functions to Prepare Our Feature Extractor Models.mp4 |
159.21MB |
| 16. Mode 1 Creating a Loss Function and Optimizer.mp4 |
31.34MB |
| 16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 |
101.70MB |
| 16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 |
131.22MB |
| 16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 |
118.64MB |
| 17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 |
127.62MB |
| 17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 |
61.36MB |
| 17. Creating a Vision Transformer Feature Extractor Model.mp4 |
78.51MB |
| 17. Dealing With Tensor Data Types.mp4 |
81.40MB |
| 17. Making and Plotting Predictions on Test Images.mp4 |
78.14MB |
| 17. Turing Our Training Loop into a Function.mp4 |
70.89MB |
| 17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 |
150.16MB |
| 17. Turning Our Custom Datasets Into DataLoaders.mp4 |
80.62MB |
| 17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 |
135.03MB |
| 18. Building and Training a Model to Fit on Straight Line Data.mp4 |
71.67MB |
| 18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 |
19.70MB |
| 18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 |
166.35MB |
| 18. Getting Tensor Attributes.mp4 |
66.44MB |
| 18. Making a Prediction on a Custom Image.mp4 |
67.83MB |
| 18. Reviewing What Happens in a Testing Loop Step by Step.mp4 |
161.56MB |
| 18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 |
45.66MB |
| 18. Turing Our Testing Loop into a Function.mp4 |
50.89MB |
| 18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 |
130.64MB |
| 19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 |
77.93MB |
| 19. Creating Patch Embeddings with a Convolutional Layer.mp4 |
142.63MB |
| 19. Evaluating Our Models Predictions on Straight Line Data.mp4 |
50.80MB |
| 19. Main Takeaways, Exercises and Extra- Curriculum.mp4 |
44.43MB |
| 19. Manipulating Tensors (Tensor Operations).mp4 |
39.70MB |
| 19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 |
108.44MB |
| 19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 |
62.00MB |
| 19. Viewing Our Modelling Experiments in TensorBoard.mp4 |
140.30MB |
| 19. Writing Code to Save a PyTorch Model.mp4 |
129.82MB |
| 2. Classification Problem Example Input and Output Shapes.mp4 |
49.97MB |
| 2. Computer Vision Input and Output Shapes.mp4 |
85.02MB |
| 2. Course Welcome and What Is Deep Learning.mp4 |
38.99MB |
| 2. Getting Setup and What We Are Covering.mp4 |
69.67MB |
| 2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 |
93.39MB |
| 2. Going Modular Notebook (Part 1) Running It End to End.mp4 |
104.92MB |
| 2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 |
48.97MB |
| 2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 |
35.34MB |
| 2. Three Questions to Ask for Machine Learning Model Deployment.mp4 |
46.93MB |
| 2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 |
55.85MB |
| 2. Why Replicate a Machine Learning Research Paper.mp4 |
23.26MB |
| 20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 |
117.23MB |
| 20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 |
129.06MB |
| 20. Getting a Results Dictionary for Model 1.mp4 |
41.35MB |
| 20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 |
96.51MB |
| 20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 |
99.19MB |
| 20. Matrix Multiplication (Part 1).mp4 |
77.80MB |
| 20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 |
43.77MB |
| 20. Writing Code to Load a PyTorch Model.mp4 |
79.58MB |
| 21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 |
96.50MB |
| 21. Building Our First Neural Network with Non-Linearity.mp4 |
92.59MB |
| 21. Collecting Stats About Our-ViT Feature Extractor.mp4 |
45.86MB |
| 21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 |
89.61MB |
| 21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 |
39.71MB |
| 21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 |
57.78MB |
| 21. Model 2 Convolutional Neural Networks High Level Overview.mp4 |
94.63MB |
| 21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 |
45.80MB |
| 22. Main Takeaways, Exercises and Extra- Curriculum.mp4 |
43.59MB |
| 22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 |
97.35MB |
| 22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 |
208.33MB |
| 22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 |
93.42MB |
| 22. Putting Everything Together (Part 1) Data.mp4 |
49.35MB |
| 22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 |
64.97MB |
| 22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 |
50.37MB |
| 22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 |
150.57MB |
| 23. Creating a Function to Make and Time Predictions with Our Models.mp4 |
185.78MB |
| 23. Creating the Patch Embedding Layer with PyTorch.mp4 |
170.03MB |
| 23. Creating Training and Testing loop Functions.mp4 |
106.17MB |
| 23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 |
48.14MB |
| 23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 |
53.05MB |
| 23. Model 2 Breaking Down Conv2D Step by Step.mp4 |
162.72MB |
| 23. Putting Everything Together (Part 2) Building a Model.mp4 |
88.70MB |
| 24. Creating a Train Function to Train and Evaluate Our Models.mp4 |
103.47MB |
| 24. Creating the Class Token Embedding.mp4 |
131.99MB |
| 24. Finding The Positional Min and Max of Tensors.mp4 |
24.50MB |
| 24. Making and Timing Predictions with EffNetB2.mp4 |
97.63MB |
| 24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 |
158.11MB |
| 24. Putting Everything Together (Part 3) Training a Model.mp4 |
103.00MB |
| 24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 |
80.74MB |
| 25. Creating the Class Token Embedding - Less Birds.mp4 |
131.91MB |
| 25. Making and Timing Predictions with ViT.mp4 |
72.47MB |
| 25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 |
174.82MB |
| 25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 |
50.63MB |
| 25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 |
97.46MB |
| 25. Reshaping, Viewing and Stacking Tensors.mp4 |
103.95MB |
| 25. Training and Evaluating Model 0 With Our Training Functions.mp4 |
89.28MB |
| 26. Comparing EffNetB2 and ViT Model Statistics.mp4 |
89.62MB |
| 26. Creating a Multi-Class Classification Model with PyTorch.mp4 |
107.44MB |
| 26. Creating the Position Embedding.mp4 |
109.18MB |
| 26. Model 2 Setting Up a Loss Function and Optimizer.mp4 |
27.88MB |
| 26. Plotting the Loss Curves of Model 0.mp4 |
89.45MB |
| 26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 |
72.52MB |
| 26. Squeezing, Unsqueezing and Permuting Tensors.mp4 |
88.41MB |
| 27. Equation 1 Putting it All Together.mp4 |
134.82MB |
| 27. Exercise Imposter Syndrome.mp4 |
39.25MB |
| 27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 |
76.79MB |
| 27. Selecting Data From Tensors (Indexing).mp4 |
56.96MB |
| 27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 |
65.06MB |
| 27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 |
131.82MB |
| 27. Visualizing the Performance vs Speed Trade-off.mp4 |
134.67MB |
| 28. Comparing the Results of Our Modelling Experiments.mp4 |
61.76MB |
| 28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 |
98.83MB |
| 28. Equation 2 Multihead Attention Overview.mp4 |
144.11MB |
| 28. Gradio Overview and Installation.mp4 |
95.13MB |
| 28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 |
97.05MB |
| 28. PyTorch Tensors and NumPy.mp4 |
59.78MB |
| 28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 |
49.32MB |
| 29. Constructing and Training Model 1.mp4 |
60.65MB |
| 29. Equation 2 Layernorm Overview.mp4 |
111.76MB |
| 29. Gradio Function Outline.mp4 |
79.90MB |
| 29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 |
83.66MB |
| 29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 |
95.11MB |
| 29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 |
150.09MB |
| 3. Creating a Function to Download Data.mp4 |
95.23MB |
| 3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 |
68.65MB |
| 3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 |
150.96MB |
| 3. Downloading a Dataset.mp4 |
67.64MB |
| 3. Installing the Latest Versions of Torch and Torchvision.mp4 |
82.39MB |
| 3. Join Our Online Classroom!.mp4 |
75.35MB |
| 3. Machine Learning vs. Deep Learning.mp4 |
55.30MB |
| 3. Typical Architecture of a Classification Neural Network (Overview).mp4 |
67.05MB |
| 3. What Is a Convolutional Neural Network (CNN).mp4 |
55.40MB |
| 3. Where Can You Find Machine Learning Research Papers and Code.mp4 |
110.75MB |
| 3. Where Is My Model Going to Go.mp4 |
139.84MB |
| 30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 |
95.22MB |
| 30. Different Ways of Accessing a GPU in PyTorch.mp4 |
113.01MB |
| 30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 |
77.05MB |
| 30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 |
63.49MB |
| 30. Plotting the Loss Curves of Model 1.mp4 |
31.69MB |
| 30. Turning Equation 2 into Code.mp4 |
163.87MB |
| 31. Checking the Inputs and Outputs of Equation.mp4 |
53.69MB |
| 31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 |
53.31MB |
| 31. Discussing a Few More Classification Metrics.mp4 |
97.54MB |
| 31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 |
160.84MB |
| 31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 |
89.27MB |
| 31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 |
64.51MB |
| 32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 |
135.39MB |
| 32. Equation 3 Replication Overview.mp4 |
88.70MB |
| 32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 |
67.01MB |
| 32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 |
51.66MB |
| 32. PyTorch Classification Exercises and Extra-Curriculum.mp4 |
41.47MB |
| 32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 |
56.76MB |
| 33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 |
64.81MB |
| 33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 |
67.99MB |
| 33. Saving and Loading Our Best Performing Model.mp4 |
98.16MB |
| 33. Turning Equation 3 into Code.mp4 |
107.07MB |
| 33. Unlimited Updates.html |
1.68KB |
| 34. Outlining the File Structure of Our Deployed App.mp4 |
89.54MB |
| 34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 |
127.06MB |
| 34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 |
81.90MB |
| 34. Transformer Encoder Overview.mp4 |
82.85MB |
| 35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 |
84.87MB |
| 35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 |
39.14MB |
| 35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 |
36.07MB |
| 36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 |
92.41MB |
| 36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 |
188.75MB |
| 36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 |
113.03MB |
| 37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 |
190.82MB |
| 37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 |
73.32MB |
| 37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 |
71.91MB |
| 38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 |
111.37MB |
| 38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 |
44.78MB |
| 39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 |
84.89MB |
| 39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 |
137.63MB |
| 4. Anatomy of Neural Networks.mp4 |
70.32MB |
| 4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 |
87.61MB |
| 4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 |
89.20MB |
| 4. Downloading Our Previously Written Code from Going Modular.mp4 |
83.75MB |
| 4. Exercise Meet Your Classmates + Instructor.html |
3.79KB |
| 4. How Is My Model Going to Function.mp4 |
67.36MB |
| 4. Making a Toy Classification Dataset.mp4 |
91.48MB |
| 4. Splitting Our Data Into Training and Test Sets.mp4 |
65.22MB |
| 4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 |
92.72MB |
| 4. What We Are Going to Cover.mp4 |
87.76MB |
| 4. Writing the Outline for Our First Python Script to Setup the Data.mp4 |
156.79MB |
| 40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 |
118.33MB |
| 40. Creating a Requirements File for Our Food Vision Mini App.mp4 |
37.50MB |
| 41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 |
112.22MB |
| 41. Training our Custom ViT on Food Vision Mini.mp4 |
53.48MB |
| 42. Discussing what Our Training Setup Is Missing.mp4 |
101.20MB |
| 42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 |
143.59MB |
| 43. Plotting a Loss Curve for Our ViT Model.mp4 |
63.40MB |
| 43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 |
91.61MB |
| 44. Food Vision Big Project Outline.mp4 |
39.15MB |
| 44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 |
164.75MB |
| 45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 |
96.53MB |
| 45. Preparing Data to Be Used with a Pretrained ViT.mp4 |
57.22MB |
| 46. Downloading the Food 101 Dataset.mp4 |
71.67MB |
| 46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 |
76.29MB |
| 47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 |
119.74MB |
| 47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 |
40.36MB |
| 48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 |
41.81MB |
| 48. Turning Our Food 101 Datasets into DataLoaders.mp4 |
61.50MB |
| 49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 |
37.11MB |
| 49. Training Food Vision Big Our Biggest Model Yet!.mp4 |
184.22MB |
| 5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 |
115.34MB |
| 5. Building a function to Visualize Our Data.mp4 |
61.89MB |
| 5. Course Companion Book + Code + More.html |
1.10KB |
| 5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 |
135.14MB |
| 5. Different Types of Learning Paradigms.mp4 |
27.05MB |
| 5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 |
72.17MB |
| 5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 |
154.00MB |
| 5. Getting Setup for Coding in Google Colab.mp4 |
99.14MB |
| 5. Some Tools and Places to Deploy Machine Learning Models.mp4 |
65.36MB |
| 5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 |
82.01MB |
| 5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 |
81.06MB |
| 50. Outlining the File Structure for Our Food Vision Big.mp4 |
52.78MB |
| 50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 |
85.49MB |
| 51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 |
36.59MB |
| 52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 |
66.81MB |
| 53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 |
23.90MB |
| 54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 |
104.81MB |
| 55. Zipping and Downloading Our Food Vision Big App Files.mp4 |
39.76MB |
| 56. Deploying Food Vision Big to Hugging Face Spaces.mp4 |
162.53MB |
| 57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 |
81.75MB |
| 6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 |
51.91MB |
| 6. Creating Our First PyTorch Model for Linear Regression.mp4 |
130.08MB |
| 6. Downloading Data for Food Vision Mini.mp4 |
43.83MB |
| 6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 |
31.92MB |
| 6. Machine Learning + Python Monthly Newsletters.html |
870B |
| 6. Preparing a Pretrained Model for Our Own Problem.mp4 |
113.16MB |
| 6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 |
141.48MB |
| 6. Turning Our Model Building Code into a Python Script.mp4 |
115.13MB |
| 6. Visualizing Random Samples of Data.mp4 |
68.11MB |
| 6. What Can Deep Learning Be Used For.mp4 |
43.20MB |
| 6. What We Are Going to Cover.mp4 |
40.83MB |
| 7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 |
62.18MB |
| 7. Coding a Small Neural Network to Handle Our Classification Data.mp4 |
86.85MB |
| 7. DataLoader Overview Understanding Mini-Batches.mp4 |
60.21MB |
| 7. Getting Setup to Code.mp4 |
62.88MB |
| 7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 |
150.28MB |
| 7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 |
81.72MB |
| 7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 |
139.74MB |
| 7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 |
89.70MB |
| 7. Turning Our Model Training Code into a Python Script.mp4 |
80.00MB |
| 7. What Is and Why PyTorch.mp4 |
113.56MB |
| 8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 |
74.44MB |
| 8. Downloading a Dataset for Food Vision Mini.mp4 |
39.25MB |
| 8. Making Our Neural Network Visual.mp4 |
91.27MB |
| 8. Training a Single Model and Saving the Results to TensorBoard.mp4 |
41.79MB |
| 8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 |
127.58MB |
| 8. Turning Our Datasets Into DataLoaders.mp4 |
100.24MB |
| 8. Turning Our Utility Function to Save a Model into a Python Script.mp4 |
75.79MB |
| 8. Visualizing a Single Image.mp4 |
36.44MB |
| 8. What Are Tensors.mp4 |
24.99MB |
| 8. Which Pretrained Model Should You Use.mp4 |
128.78MB |
| 9. Checking Out the Internals of Our PyTorch Model.mp4 |
102.71MB |
| 9. Creating a Training Script to Train Our Model in One Line of Code.mp4 |
165.52MB |
| 9. Exploring Our Single Models Results with TensorBoard.mp4 |
116.26MB |
| 9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 |
98.17MB |
| 9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 |
136.88MB |
| 9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 |
58.56MB |
| 9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 |
123.24MB |
| 9. Replicating a Vision Transformer - High Level Overview.mp4 |
77.84MB |
| 9. Setting Up a Pretrained Model with Torchvision.mp4 |
113.15MB |
| 9. What We Are Going To Cover With PyTorch.mp4 |
50.45MB |
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