GetFreeCourses Co Udemy PyTorch for Deep Learning in 2023 Zero to Mastery

GetFreeCourses Co Udemy PyTorch for Deep Learning in 2023 Zero to Mastery

Name	GetFreeCourses.Co-Udemy-PyTorch for Deep Learning in 2023 Zero to Mastery	DOWNLOAD Copy Link
Total Size	29.9 GB
Total Files	337
Last Seen	2024-07-23 23:42
Hash	B0C637B060695FFDAC314EDC544B35FB6747C18E

/1. Introduction/
1. PyTorch for Deep Learning.mp4	79.0 MB
2. Course Welcome and What Is Deep Learning.mp4	40.9 MB
3. Join Our Online Classroom!.mp4	79.0 MB
4. Exercise Meet Your Classmates + Instructor.html	3.9 KB
5. Course Companion Book + Code + More.html	1.1 KB
6. Machine Learning + Python Monthly Newsletters.html	0.9 KB
/10. PyTorch Paper Replicating/
1. What Is a Machine Learning Research Paper.mp4	98.5 MB
10. Breaking Down Figure 1 of the ViT Paper.mp4	91.4 MB
11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4	147.8 MB
12. Breaking Down Equation 1.mp4	108.2 MB
13. Breaking Down Equation 2 and 3.mp4	131.1 MB
14. Breaking Down Equation 4.mp4	96.9 MB
15. Breaking Down Table 1.mp4	128.0 MB
16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4	168.4 MB
17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4	157.5 MB
18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4	137.0 MB
19. Creating Patch Embeddings with a Convolutional Layer.mp4	149.6 MB
2. Why Replicate a Machine Learning Research Paper.mp4	24.4 MB
20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4	135.3 MB
21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4	94.0 MB
22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4	52.8 MB
23. Creating the Patch Embedding Layer with PyTorch.mp4	178.3 MB
24. Creating the Class Token Embedding.mp4	138.4 MB
25. Creating the Class Token Embedding - Less Birds.mp4	138.3 MB
26. Creating the Position Embedding.mp4	114.5 MB
27. Equation 1 Putting it All Together.mp4	141.4 MB
28. Equation 2 Multihead Attention Overview.mp4	151.1 MB
29. Equation 2 Layernorm Overview.mp4	117.2 MB
3. Where Can You Find Machine Learning Research Papers and Code.mp4	116.1 MB
30. Turning Equation 2 into Code.mp4	171.8 MB
31. Checking the Inputs and Outputs of Equation.mp4	56.3 MB
32. Equation 3 Replication Overview.mp4	93.0 MB
33. Turning Equation 3 into Code.mp4	112.3 MB
34. Transformer Encoder Overview.mp4	86.9 MB
35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4	89.0 MB
36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4	197.9 MB
37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4	200.1 MB
38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4	116.8 MB
39. Getting a Visual Summary of Our Custom Vision Transformer.mp4	89.0 MB
4. What We Are Going to Cover.mp4	92.0 MB
40. Creating a Loss Function and Optimizer from the ViT Paper.mp4	124.1 MB
41. Training our Custom ViT on Food Vision Mini.mp4	56.1 MB
42. Discussing what Our Training Setup Is Missing.mp4	106.1 MB
43. Plotting a Loss Curve for Our ViT Model.mp4	66.5 MB
44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4	172.8 MB
45. Preparing Data to Be Used with a Pretrained ViT.mp4	60.0 MB
46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4	80.0 MB
47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4	42.3 MB
48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4	43.8 MB
49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4	38.9 MB
5. Getting Setup for Coding in Google Colab.mp4	104.0 MB
50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4	89.6 MB
6. Downloading Data for Food Vision Mini.mp4	46.0 MB
7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4	94.1 MB
8. Visualizing a Single Image.mp4	38.2 MB
9. Replicating a Vision Transformer - High Level Overview.mp4	81.6 MB
/11. PyTorch Model Deployment/
1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4	77.4 MB
10. Creating an EffNetB2 Feature Extractor Model.mp4	96.6 MB
11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4	60.4 MB
12. Creating DataLoaders for EffNetB2.mp4	32.9 MB
13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4	101.7 MB
14. Saving Our EffNetB2 Model to File.mp4	28.0 MB
15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4	58.2 MB
16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4	66.3 MB
17. Creating a Vision Transformer Feature Extractor Model.mp4	82.3 MB
18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4	20.7 MB
19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4	65.0 MB
2. Three Questions to Ask for Machine Learning Model Deployment.mp4	49.2 MB
20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4	45.9 MB
21. Collecting Stats About Our-ViT Feature Extractor.mp4	48.1 MB
22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4	98.0 MB
23. Creating a Function to Make and Time Predictions with Our Models.mp4	194.8 MB
24. Making and Timing Predictions with EffNetB2.mp4	102.4 MB
25. Making and Timing Predictions with ViT.mp4	76.0 MB
26. Comparing EffNetB2 and ViT Model Statistics.mp4	94.0 MB
27. Visualizing the Performance vs Speed Trade-off.mp4	141.2 MB
28. Gradio Overview and Installation.mp4	99.8 MB
29. Gradio Function Outline.mp4	83.8 MB
3. Where Is My Model Going to Go.mp4	146.6 MB
30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4	99.8 MB
31. Creating a List of Examples to Pass to Our Gradio Demo.mp4	55.9 MB
32. Bringing Food Vision Mini to Life in a Live Web Application.mp4	142.0 MB
33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4	68.0 MB
34. Outlining the File Structure of Our Deployed App.mp4	93.9 MB
35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4	41.0 MB
36. Creating an Examples Directory with Example Food Vision Mini Images.mp4	96.9 MB
37. Writing Code to Move Our Saved EffNetB2 Model File.mp4	75.4 MB
38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4	47.0 MB
39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4	144.3 MB
4. How Is My Model Going to Function.mp4	70.6 MB
40. Creating a Requirements File for Our Food Vision Mini App.mp4	39.3 MB
41. Downloading Our Food Vision Mini App Files from Google Colab.mp4	117.7 MB
42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4	150.6 MB
43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4	96.1 MB
44. Food Vision Big Project Outline.mp4	41.1 MB
45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4	101.2 MB
46. Downloading the Food 101 Dataset.mp4	75.1 MB
47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4	125.6 MB
48. Turning Our Food 101 Datasets into DataLoaders.mp4	64.5 MB
49. Training Food Vision Big Our Biggest Model Yet!.mp4	193.2 MB
5. Some Tools and Places to Deploy Machine Learning Models.mp4	68.5 MB
50. Outlining the File Structure for Our Food Vision Big.mp4	55.3 MB
51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4	38.4 MB
52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4	70.1 MB
53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4	25.1 MB
54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4	109.9 MB
55. Zipping and Downloading Our Food Vision Big App Files.mp4	41.7 MB
56. Deploying Food Vision Big to Hugging Face Spaces.mp4	170.4 MB
57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4	85.7 MB
6. What We Are Going to Cover.mp4	42.8 MB
7. Getting Setup to Code.mp4	65.9 MB
8. Downloading a Dataset for Food Vision Mini.mp4	41.2 MB
9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4	61.4 MB
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/12. Where To Go From Here/
1. Thank You!.mp4	22.0 MB
/2. PyTorch Fundamentals/
1. Why Use Machine Learning or Deep Learning.mp4	14.5 MB
10. How To and How Not To Approach This Course.mp4	39.6 MB
11. Important Resources For This Course.mp4	61.1 MB
12. Getting Setup to Write PyTorch Code.mp4	73.4 MB
13. Introduction to PyTorch Tensors.mp4	98.6 MB
14. Creating Random Tensors in PyTorch.mp4	90.6 MB
15. Creating Tensors With Zeros and Ones in PyTorch.mp4	25.8 MB
16. Creating a Tensor Range and Tensors Like Other Tensors.mp4	34.2 MB
17. Dealing With Tensor Data Types.mp4	85.4 MB
18. Getting Tensor Attributes.mp4	69.7 MB
19. Manipulating Tensors (Tensor Operations).mp4	41.6 MB
2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4	37.1 MB
20. Matrix Multiplication (Part 1).mp4	81.6 MB
21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4	60.6 MB
22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4	102.1 MB
23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4	50.5 MB
24. Finding The Positional Min and Max of Tensors.mp4	25.7 MB
25. Reshaping, Viewing and Stacking Tensors.mp4	109.0 MB
26. Squeezing, Unsqueezing and Permuting Tensors.mp4	92.7 MB
27. Selecting Data From Tensors (Indexing).mp4	59.7 MB
28. PyTorch Tensors and NumPy.mp4	62.7 MB
29. PyTorch Reproducibility (Taking the Random Out of Random).mp4	99.7 MB
3. Machine Learning vs. Deep Learning.mp4	58.0 MB
30. Different Ways of Accessing a GPU in PyTorch.mp4	118.5 MB
31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4	67.6 MB
32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4	59.5 MB
33. Unlimited Updates.html	1.7 KB
4. Anatomy of Neural Networks.mp4	73.7 MB
5. Different Types of Learning Paradigms.mp4	28.4 MB
6. What Can Deep Learning Be Used For.mp4	45.3 MB
7. What Is and Why PyTorch.mp4	119.1 MB
8. What Are Tensors.mp4	26.2 MB
9. What We Are Going To Cover With PyTorch.mp4	52.9 MB
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/3. PyTorch Workflow/
1. Introduction and Where You Can Get Help.mp4	30.0 MB
10. Making Predictions With Our Random Model Using Inference Mode.mp4	112.2 MB
11. Training a Model Intuition (The Things We Need).mp4	72.9 MB
12. Setting Up an Optimizer and a Loss Function.mp4	121.6 MB
13. PyTorch Training Loop Steps and Intuition.mp4	135.0 MB
14. Writing Code for a PyTorch Training Loop.mp4	87.0 MB
15. Reviewing the Steps in a Training Loop Step by Step.mp4	186.1 MB
16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4	106.6 MB
17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4	141.6 MB
18. Reviewing What Happens in a Testing Loop Step by Step.mp4	169.4 MB
19. Writing Code to Save a PyTorch Model.mp4	136.1 MB
2. Getting Setup and What We Are Covering.mp4	73.1 MB
20. Writing Code to Load a PyTorch Model.mp4	83.4 MB
21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4	48.0 MB
22. Putting Everything Together (Part 1) Data.mp4	51.7 MB
23. Putting Everything Together (Part 2) Building a Model.mp4	93.0 MB
24. Putting Everything Together (Part 3) Training a Model.mp4	108.0 MB
25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4	53.1 MB
26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4	76.0 MB
27. Exercise Imposter Syndrome.mp4	41.2 MB
28. PyTorch Workflow Exercises and Extra-Curriculum.mp4	51.7 MB
3. Creating a Simple Dataset Using the Linear Regression Formula.mp4	72.0 MB
4. Splitting Our Data Into Training and Test Sets.mp4	68.4 MB
5. Building a function to Visualize Our Data.mp4	64.9 MB
6. Creating Our First PyTorch Model for Linear Regression.mp4	136.4 MB
7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4	65.2 MB
8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4	78.1 MB
9. Checking Out the Internals of Our PyTorch Model.mp4	107.7 MB
/4. PyTorch Neural Network Classification/
1. Introduction to Machine Learning Classification With PyTorch.mp4	88.7 MB
10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4	168.9 MB
11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4	141.1 MB
12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4	132.9 MB
13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4	157.3 MB
14. Discussing Options to Improve a Model.mp4	84.8 MB
15. Creating a New Model with More Layers and Hidden Units.mp4	72.2 MB
16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4	124.4 MB
17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4	64.3 MB
18. Building and Training a Model to Fit on Straight Line Data.mp4	75.2 MB
19. Evaluating Our Models Predictions on Straight Line Data.mp4	53.3 MB
2. Classification Problem Example Input and Output Shapes.mp4	52.4 MB
20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4	101.2 MB
21. Building Our First Neural Network with Non-Linearity.mp4	97.1 MB
22. Writing Training and Testing Code for Our First Non-Linear Model.mp4	157.9 MB
23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4	55.6 MB
24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4	84.7 MB
25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4	102.2 MB
26. Creating a Multi-Class Classification Model with PyTorch.mp4	112.7 MB
27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4	68.2 MB
28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4	101.8 MB
29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4	157.4 MB
3. Typical Architecture of a Classification Neural Network (Overview).mp4	70.3 MB
30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4	80.8 MB
31. Discussing a Few More Classification Metrics.mp4	102.3 MB
32. PyTorch Classification Exercises and Extra-Curriculum.mp4	43.5 MB
4. Making a Toy Classification Dataset.mp4	95.9 MB
5. Turning Our Data into Tensors and Making a Training and Test Split.mp4	85.0 MB
6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4	33.5 MB
7. Coding a Small Neural Network to Handle Our Classification Data.mp4	91.1 MB
8. Making Our Neural Network Visual.mp4	95.7 MB
9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4	129.2 MB
/5. PyTorch Computer Vision/
1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4	119.2 MB
10. Creating a Loss Function an Optimizer for Model 0.mp4	115.9 MB
11. Creating a Function to Time Our Modelling Code.mp4	47.8 MB
12. Writing Training and Testing Loops for Our Batched Data.mp4	165.2 MB
13. Writing an Evaluation Function to Get Our Models Results.mp4	112.0 MB
14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4	46.5 MB
15. Model 1 Creating a Model with Non-Linear Functions.mp4	90.6 MB
16. Mode 1 Creating a Loss Function and Optimizer.mp4	32.9 MB
17. Turing Our Training Loop into a Function.mp4	74.3 MB
18. Turing Our Testing Loop into a Function.mp4	53.4 MB
19. Training and Testing Model 1 with Our Training and Testing Functions.mp4	113.7 MB
2. Computer Vision Input and Output Shapes.mp4	89.1 MB
20. Getting a Results Dictionary for Model 1.mp4	43.4 MB
21. Model 2 Convolutional Neural Networks High Level Overview.mp4	99.2 MB
22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4	218.5 MB
23. Model 2 Breaking Down Conv2D Step by Step.mp4	170.6 MB
24. Model 2 Breaking Down MaxPool2D Step by Step.mp4	165.8 MB
25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4	183.3 MB
26. Model 2 Setting Up a Loss Function and Optimizer.mp4	29.2 MB
27. Model 2 Training Our First CNN and Evaluating Its Results.mp4	80.5 MB
28. Comparing the Results of Our Modelling Experiments.mp4	64.8 MB
29. Making Predictions on Random Test Samples with the Best Trained Model.mp4	87.7 MB
3. What Is a Convolutional Neural Network (CNN).mp4	58.1 MB
30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4	66.6 MB
31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4	168.6 MB
32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4	70.3 MB
33. Saving and Loading Our Best Performing Model.mp4	102.9 MB
34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4	85.9 MB
4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4	93.5 MB
5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4	161.5 MB
6. Visualizing Random Samples of Data.mp4	71.4 MB
7. DataLoader Overview Understanding Mini-Batches.mp4	63.1 MB
8. Turning Our Datasets Into DataLoaders.mp4	105.1 MB
9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4	143.5 MB
/6. PyTorch Custom Datasets/
1. What Is a Custom Dataset and What We Are Going to Cover.mp4	97.1 MB
10. Visualizing a Loaded Image From the Train Dataset.mp4	80.5 MB
11. Turning Our Image Datasets into PyTorch Dataloaders.mp4	88.4 MB
12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4	78.3 MB
13. Creating a Helper Function to Get Class Names From a Directory.mp4	82.9 MB
14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4	184.8 MB
15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4	72.9 MB
16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4	137.6 MB
17. Turning Our Custom Datasets Into DataLoaders.mp4	84.5 MB
18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4	174.4 MB
19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4	81.7 MB
2. Importing PyTorch and Setting Up Device Agnostic Code.mp4	51.3 MB
20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4	122.9 MB
21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4	101.2 MB
22. Using the Torchinfo Package to Get a Summary of Our Model.mp4	68.1 MB
23. Creating Training and Testing loop Functions.mp4	111.3 MB
24. Creating a Train Function to Train and Evaluate Our Models.mp4	108.5 MB
25. Training and Evaluating Model 0 With Our Training Functions.mp4	93.6 MB
26. Plotting the Loss Curves of Model 0.mp4	93.8 MB
27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4	138.2 MB
28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4	103.6 MB
29. Constructing and Training Model 1.mp4	63.6 MB
3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4	158.3 MB
30. Plotting the Loss Curves of Model 1.mp4	33.2 MB
31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4	93.6 MB
32. Predicting on Custom Data (Part 1) Downloading an Image.mp4	54.2 MB
33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4	71.3 MB
34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4	133.2 MB
35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4	37.8 MB
36. Predicting on Custom Data (Part 5) Putting It All Together.mp4	118.5 MB
37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4	76.9 MB
4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4	91.9 MB
5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4	120.9 MB
6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4	54.4 MB
7. Transforming Data (Part 1) Turning Images Into Tensors.mp4	85.7 MB
8. Transforming Data (Part 2) Visualizing Transformed Images.mp4	133.8 MB
9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4	102.9 MB
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/7. PyTorch Going Modular/
1. What Is Going Modular and What We Are Going to Cover.mp4	105.0 MB
10. Going Modular Summary, Exercises and Extra-Curriculum.mp4	84.6 MB
2. Going Modular Notebook (Part 1) Running It End to End.mp4	110.0 MB
3. Downloading a Dataset.mp4	70.9 MB
4. Writing the Outline for Our First Python Script to Setup the Data.mp4	164.4 MB
5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4	141.7 MB
6. Turning Our Model Building Code into a Python Script.mp4	120.7 MB
7. Turning Our Model Training Code into a Python Script.mp4	83.9 MB
8. Turning Our Utility Function to Save a Model into a Python Script.mp4	79.5 MB
9. Creating a Training Script to Train Our Model in One Line of Code.mp4	173.6 MB
/8. PyTorch Transfer Learning/
1. Introduction What is Transfer Learning and Why Use It.mp4	102.0 MB
10. Different Kinds of Transfer Learning.mp4	59.7 MB
11. Getting a Summary of the Different Layers of Our Model.mp4	79.7 MB
12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4	168.5 MB
13. Training Our First Transfer Learning Feature Extractor Model.mp4	78.4 MB
14. Plotting the Loss curves of Our Transfer Learning Model.mp4	61.8 MB
15. Outlining the Steps to Make Predictions on the Test Images.mp4	70.0 MB
16. Creating a Function Predict On and Plot Images.mp4	106.6 MB
17. Making and Plotting Predictions on Test Images.mp4	81.9 MB
18. Making a Prediction on a Custom Image.mp4	71.1 MB
19. Main Takeaways, Exercises and Extra- Curriculum.mp4	46.6 MB
2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4	58.6 MB
3. Installing the Latest Versions of Torch and Torchvision.mp4	86.4 MB
4. Downloading Our Previously Written Code from Going Modular.mp4	87.8 MB
5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4	75.7 MB
6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4	148.4 MB
7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4	146.5 MB
8. Which Pretrained Model Should You Use.mp4	135.0 MB
9. Setting Up a Pretrained Model with Torchvision.mp4	118.6 MB
/9. PyTorch Experiment Tracking/
1. What Is Experiment Tracking and Why Track Experiments.mp4	64.9 MB
10. Creating a Function to Create SummaryWriter Instances.mp4	84.0 MB
11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4	69.8 MB
12. What Experiments Should You Try.mp4	49.2 MB
13. Discussing the Experiments We Are Going to Try.mp4	50.6 MB
14. Downloading Datasets for Our Modelling Experiments.mp4	69.6 MB
15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4	81.9 MB
16. Creating Functions to Prepare Our Feature Extractor Models.mp4	166.9 MB
17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4	133.8 MB
18. Running Eight Different Modelling Experiments in 5 Minutes.mp4	47.9 MB
19. Viewing Our Modelling Experiments in TensorBoard.mp4	147.1 MB
2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4	97.9 MB
20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4	104.0 MB
21. Making a Prediction on Our Own Custom Image with the Best Model.mp4	41.6 MB
22. Main Takeaways, Exercises and Extra- Curriculum.mp4	45.7 MB
3. Creating a Function to Download Data.mp4	99.9 MB
4. Turning Our Data into DataLoaders Using Manual Transforms.mp4	97.2 MB
5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4	86.0 MB
6. Preparing a Pretrained Model for Our Own Problem.mp4	118.7 MB
7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4	157.6 MB
8. Training a Single Model and Saving the Results to TensorBoard.mp4	43.8 MB
9. Exploring Our Single Models Results with TensorBoard.mp4	121.9 MB
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Total files 337