PyTorch for Deep Learning
52h 27s
English
Paid
June 13, 2024
Learn PyTorch from scratch! This PyTorch course is your step-by-step guide to developing your own deep learning models using PyTorch. You'll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you'll have the skills and portfolio to get hired as a Deep Learning Engineer.
Watch Online PyTorch for Deep Learning
Join premium to watch
Go to premium
# | Title | Duration |
---|---|---|
1 | PyTorch for Deep Learning | 03:34 |
2 | Course Welcome and What Is Deep Learning | 05:54 |
3 | Why Use Machine Learning or Deep Learning | 03:34 |
4 | The Number 1 Rule of Machine Learning and What Is Deep Learning Good For | 05:40 |
5 | Machine Learning vs. Deep Learning | 06:07 |
6 | Anatomy of Neural Networks | 09:22 |
7 | Different Types of Learning Paradigms | 04:31 |
8 | What Can Deep Learning Be Used For | 06:22 |
9 | What Is and Why PyTorch | 10:13 |
10 | What Are Tensors | 04:16 |
11 | What We Are Going To Cover With PyTorch | 06:06 |
12 | How To and How Not To Approach This Course | 05:10 |
13 | Important Resources For This Course | 05:22 |
14 | Getting Setup to Write PyTorch Code | 07:40 |
15 | Introduction to PyTorch Tensors | 13:26 |
16 | Creating Random Tensors in PyTorch | 09:59 |
17 | Creating Tensors With Zeros and Ones in PyTorch | 03:09 |
18 | Creating a Tensor Range and Tensors Like Other Tensors | 05:18 |
19 | Dealing With Tensor Data Types | 09:25 |
20 | Getting Tensor Attributes | 08:23 |
21 | Manipulating Tensors (Tensor Operations) | 06:00 |
22 | Matrix Multiplication (Part 1) | 09:35 |
23 | Matrix Multiplication (Part 2): The Two Main Rules of Matrix Multiplication | 07:52 |
24 | Matrix Multiplication (Part 3): Dealing With Tensor Shape Errors | 12:58 |
25 | Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation) | 06:10 |
26 | Finding The Positional Min and Max of Tensors | 03:17 |
27 | Reshaping, Viewing and Stacking Tensors | 13:41 |
28 | Squeezing, Unsqueezing and Permuting Tensors | 11:56 |
29 | Selecting Data From Tensors (Indexing) | 09:32 |
30 | PyTorch Tensors and NumPy | 09:09 |
31 | PyTorch Reproducibility (Taking the Random Out of Random) | 10:47 |
32 | Different Ways of Accessing a GPU in PyTorch | 11:51 |
33 | Setting up Device Agnostic Code and Putting Tensors On and Off the GPU | 07:44 |
34 | PyTorch Fundamentals: Exercises and Extra-Curriculum | 04:50 |
35 | Introduction and Where You Can Get Help | 02:46 |
36 | Getting Setup and What We Are Covering | 07:15 |
37 | Creating a Simple Dataset Using the Linear Regression Formula | 09:42 |
38 | Splitting Our Data Into Training and Test Sets | 08:21 |
39 | Building a function to Visualize Our Data | 07:46 |
40 | Creating Our First PyTorch Model for Linear Regression | 14:10 |
41 | Breaking Down What's Happening in Our PyTorch Linear regression Model | 06:11 |
42 | Discussing Some of the Most Important PyTorch Model Building Classes | 06:27 |
43 | Checking Out the Internals of Our PyTorch Model | 09:51 |
44 | Making Predictions With Our Random Model Using Inference Mode | 11:13 |
45 | Training a Model Intuition (The Things We Need) | 08:15 |
46 | Setting Up an Optimizer and a Loss Function | 12:52 |
47 | PyTorch Training Loop Steps and Intuition | 13:54 |
48 | Writing Code for a PyTorch Training Loop | 08:47 |
49 | Reviewing the Steps in a Training Loop Step by Step | 14:58 |
50 | Running Our Training Loop Epoch by Epoch and Seeing What Happens | 09:26 |
51 | Writing Testing Loop Code and Discussing What's Happening Step by Step | 11:38 |
52 | Reviewing What Happens in a Testing Loop Step by Step | 14:43 |
53 | Writing Code to Save a PyTorch Model | 13:46 |
54 | Writing Code to Load a PyTorch Model | 08:45 |
55 | Setting Up to Practice Everything We Have Done Using Device-Agnostic Code | 06:03 |
56 | Putting Everything Together (Part 1): Data | 06:09 |
57 | Putting Everything Together (Part 2): Building a Model | 10:08 |
58 | Putting Everything Together (Part 3): Training a Model | 12:41 |
59 | Putting Everything Together (Part 4): Making Predictions With a Trained Model | 05:18 |
60 | Putting Everything Together (Part 5): Saving and Loading a Trained Model | 09:11 |
61 | Exercise: Imposter Syndrome | 02:57 |
62 | PyTorch Workflow: Exercises and Extra-Curriculum | 03:58 |
63 | Introduction to Machine Learning Classification With PyTorch | 09:42 |
64 | Classification Problem Example: Input and Output Shapes | 09:08 |
65 | Typical Architecture of a Classification Neural Network (Overview) | 06:32 |
66 | Making a Toy Classification Dataset | 12:19 |
67 | Turning Our Data into Tensors and Making a Training and Test Split | 11:56 |
68 | Laying Out Steps for Modelling and Setting Up Device-Agnostic Code | 04:20 |
69 | Coding a Small Neural Network to Handle Our Classification Data | 10:58 |
70 | Making Our Neural Network Visual | 06:58 |
71 | Recreating and Exploring the Insides of Our Model Using nn.Sequential | 13:18 |
72 | Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network | 14:51 |
73 | Going from Model Logits to Prediction Probabilities to Prediction Labels | 16:08 |
74 | Coding a Training and Testing Optimization Loop for Our Classification Model | 15:27 |
75 | Writing Code to Download a Helper Function to Visualize Our Models Predictions | 14:14 |
76 | Discussing Options to Improve a Model | 08:03 |
77 | Creating a New Model with More Layers and Hidden Units | 09:07 |
78 | Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better | 12:46 |
79 | Creating a Straight Line Dataset to See if Our Model is Learning Anything | 08:08 |
80 | Building and Training a Model to Fit on Straight Line Data | 10:02 |
81 | Evaluating Our Models Predictions on Straight Line Data | 05:24 |
82 | Introducing the Missing Piece for Our Classification Model Non-Linearity | 10:01 |
83 | Building Our First Neural Network with Non-Linearity | 10:26 |
84 | Writing Training and Testing Code for Our First Non-Linear Model | 15:13 |
85 | Making Predictions with and Evaluating Our First Non-Linear Model | 05:48 |
86 | Replicating Non-Linear Activation Functions with Pure PyTorch | 09:35 |
87 | Putting It All Together (Part 1): Building a Multiclass Dataset | 11:25 |
88 | Creating a Multi-Class Classification Model with PyTorch | 12:28 |
89 | Setting Up a Loss Function and Optimizer for Our Multi-Class Model | 06:41 |
90 | Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model | 11:03 |
91 | Training a Multi-Class Classification Model and Troubleshooting Code on the Fly | 16:18 |
92 | Making Predictions with and Evaluating Our Multi-Class Classification Model | 08:00 |
93 | Discussing a Few More Classification Metrics | 09:18 |
94 | PyTorch Classification: Exercises and Extra-Curriculum | 02:59 |
95 | What Is a Computer Vision Problem and What We Are Going to Cover | 11:48 |
96 | Computer Vision Input and Output Shapes | 10:09 |
97 | What Is a Convolutional Neural Network (CNN) | 05:03 |
98 | Discussing and Importing the Base Computer Vision Libraries in PyTorch | 09:20 |
99 | Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes | 14:31 |
100 | Visualizing Random Samples of Data | 09:52 |
101 | DataLoader Overview Understanding Mini-Batch | 07:18 |
102 | Turning Our Datasets Into DataLoaders | 12:24 |
103 | Model 0: Creating a Baseline Model with Two Linear Layers | 14:39 |
104 | Creating a Loss Function: an Optimizer for Model 0 | 10:30 |
105 | Creating a Function to Time Our Modelling Code | 05:35 |
106 | Writing Training and Testing Loops for Our Batched Data | 21:26 |
107 | Writing an Evaluation Function to Get Our Models Results | 12:59 |
108 | Setup Device-Agnostic Code for Running Experiments on the GPU | 03:47 |
109 | Model 1: Creating a Model with Non-Linear Functions | 09:04 |
110 | Mode 1: Creating a Loss Function and Optimizer | 03:05 |
111 | Turing Our Training Loop into a Function | 08:29 |
112 | Turing Our Testing Loop into a Function | 06:36 |
113 | Training and Testing Model 1 with Our Training and Testing Functions | 11:53 |
114 | Getting a Results Dictionary for Model 1 | 04:09 |
115 | Model 2: Convolutional Neural Networks High Level Overview | 08:25 |
116 | Model 2: Coding Our First Convolutional Neural Network with PyTorch | 19:49 |
117 | Model 2: Breaking Down Conv2D Step by Step | 15:00 |
118 | Model 2: Breaking Down MaxPool2D Step by Step | 15:49 |
119 | Mode 2: Using a Trick to Find the Input and Output Shapes of Each of Our Layers | 13:46 |
120 | Model 2: Setting Up a Loss Function and Optimizer | 02:39 |
121 | Model 2: Training Our First CNN and Evaluating Its Results | 07:55 |
122 | Comparing the Results of Our Modelling Experiments | 07:24 |
123 | Making Predictions on Random Test Samples with the Best Trained Model | 11:40 |
124 | Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them | 08:11 |
125 | Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix | 15:21 |
126 | Evaluating Our Best Models Predictions with a Confusion Matrix | 06:55 |
127 | Saving and Loading Our Best Performing Model | 11:28 |
128 | Recapping What We Have Covered Plus Exercises and Extra-Curriculum | 06:02 |
129 | What Is a Custom Dataset and What We Are Going to Cover | 09:54 |
130 | Importing PyTorch and Setting Up Device-Agnostic Code | 05:55 |
131 | Downloading a Custom Dataset of Pizza, Steak and Sushi Images | 14:05 |
132 | Becoming One With the Data (Part 1): Exploring the Data Format | 08:42 |
133 | Becoming One With the Data (Part 2): Visualizing a Random Image | 11:41 |
134 | Becoming One With the Data (Part 3): Visualizing a Random Image with Matplotlib | 04:48 |
135 | Transforming Data (Part 1): Turning Images Into Tensors | 08:54 |
136 | Transforming Data (Part 2): Visualizing Transformed Images | 11:31 |
137 | Loading All of Our Images and Turning Them Into Tensors With ImageFolder | 09:19 |
138 | Visualizing a Loaded Image From the Train Dataset | 07:19 |
139 | Turning Our Image Datasets into PyTorch DataLoaders | 09:04 |
140 | Creating a Custom Dataset Class in PyTorch High Level Overview | 08:01 |
141 | Creating a Helper Function to Get Class Names From a Directory | 09:07 |
142 | Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images | 17:47 |
143 | Compare Our Custom Dataset Class to the Original ImageFolder Class | 07:14 |
144 | Writing a Helper Function to Visualize Random Images from Our Custom Dataset | 14:19 |
145 | Turning Our Custom Datasets Into DataLoaders | 07:00 |
146 | Exploring State of the Art Data Augmentation With Torchvision Transforms | 14:24 |
147 | Building a Baseline Model (Part 1): Loading and Transforming Data | 08:16 |
148 | Building a Baseline Model (Part 2): Replicating Tiny VGG from Scratch | 11:25 |
149 | Building a Baseline Model (Part 3): Doing a Forward Pass to Test Our Model Shapes | 08:10 |
150 | Using the Torchinfo Package to Get a Summary of Our Model | 06:39 |
151 | Creating Training and Testing loop Functions | 13:04 |
152 | Creating a Train Function to Train and Evaluate Our Models | 10:15 |
153 | Training and Evaluating Model 0 With Our Training Functions | 09:54 |
154 | Plotting the Loss Curves of Model 0 | 09:03 |
155 | Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each | 14:14 |
156 | Creating Augmented Training Datasets and DataLoaders for Model 1 | 11:04 |
157 | Constructing and Training Model 1 | 07:11 |
158 | Plotting the Loss Curves of Model 1 | 03:23 |
159 | Plotting the Loss Curves of All of Our Models Against Each Other | 10:56 |
160 | Predicting on Custom Data (Part 1): Downloading an Image | 05:33 |
161 | Predicting on Custom Data (Part2): Loading In a Custom Image With PyTorch | 07:01 |
162 | Predicting on Custom Data (Part 3): Getting Our Custom Image Into the Right Format | 14:08 |
163 | Predicting on Custom Data (Part 4): Turning Our Models Raw Outputs Into Prediction Labels | 04:25 |
164 | Predicting on Custom Data (Part 5): Putting It All Together | 12:48 |
165 | Summary of What We Have Covered Plus Exercises and Extra-Curriculum | 06:05 |
166 | What Is Going Modular and What We Are Going to Cover | 11:35 |
167 | Going Modular Notebook (Part 1): Running It End to End | 07:41 |
168 | Downloading a Dataset | 04:51 |
169 | Writing the Outline for Our First Python Script to Setup the Data | 13:51 |
170 | Creating a Python Script to Create Our PyTorch DataLoaders | 10:36 |
171 | Turning Our Model Building Code into a Python Script | 09:19 |
172 | Turning Our Model Training Code into a Python Script | 06:17 |
173 | Turning Our Utility Function to Save a Model into a Python Script | 06:08 |
174 | Creating a Training Script to Train Our Model in One Line of Code | 15:47 |
175 | Going Modular: Summary, Exercises and Extra-Curriculum | 06:00 |
176 | Introduction: What is Transfer Learning and Why Use It | 10:06 |
177 | Where Can You Find Pretrained Models and What We Are Going to Cover | 05:13 |
178 | Installing the Latest Versions of Torch and Torchvision | 08:06 |
179 | Downloading Our Previously Written Code from Going Modular | 06:42 |
180 | Downloading Pizza, Steak, Sushi Image Data from Github | 08:01 |
181 | Turning Our Data into DataLoaders with Manually Created Transforms | 14:41 |
182 | Turning Our Data into DataLoaders with Automatic Created Transforms | 13:07 |
183 | Which Pretrained Model Should You Use | 12:16 |
184 | Setting Up a Pretrained Model with Torchvision | 10:57 |
185 | Different Kinds of Transfer Learning | 07:12 |
186 | Getting a Summary of the Different Layers of Our Model | 06:50 |
187 | Freezing the Base Layers of Our Model and Updating the Classifier Head | 13:27 |
188 | Training Our First Transfer Learning Feature Extractor Model | 07:55 |
189 | Plotting the Loss Curves of Our Transfer Learning Model | 06:27 |
190 | Outlining the Steps to Make Predictions on the Test Images | 07:58 |
191 | Creating a Function Predict On and Plot Images | 10:01 |
192 | Making and Plotting Predictions on Test Images | 07:24 |
193 | Making a Prediction on a Custom Image | 06:22 |
194 | Main Takeaways, Exercises and Extra Curriculum | 03:22 |
195 | What Is Experiment Tracking and Why Track Experiments | 07:07 |
196 | Getting Setup by Importing Torch Libraries and Going Modular Code | 08:14 |
197 | Creating a Function to Download Data | 10:24 |
198 | Turning Our Data into DataLoaders Using Manual Transforms | 08:31 |
199 | Turning Our Data into DataLoaders Using Automatic Transforms | 07:48 |
200 | Preparing a Pretrained Model for Our Own Problem | 10:29 |
201 | Setting Up a Way to Track a Single Model Experiment with TensorBoard | 13:36 |
202 | Training a Single Model and Saving the Results to TensorBoard | 04:39 |
203 | Exploring Our Single Models Results with TensorBoard | 10:18 |
204 | Creating a Function to Create SummaryWriter Instances | 10:45 |
205 | Adapting Our Train Function to Be Able to Track Multiple Experiments | 04:58 |
206 | What Experiments Should You Try | 06:00 |
207 | Discussing the Experiments We Are Going to Try | 06:02 |
208 | Downloading Datasets for Our Modelling Experiments | 06:32 |
209 | Turning Our Datasets into DataLoaders Ready for Experimentation | 08:29 |
210 | Creating Functions to Prepare Our Feature Extractor Models | 15:55 |
211 | Coding Out the Steps to Run a Series of Modelling Experiments | 14:28 |
212 | Running Eight Different Modelling Experiments in 5 Minutes | 03:51 |
213 | Viewing Our Modelling Experiments in TensorBoard | 13:39 |
214 | Loading In the Best Model and Making Predictions on Random Images from the Test Set | 10:33 |
215 | Making a Prediction on Our Own Custom Image with the Best Model | 03:45 |
216 | Main Takeaways, Exercises and Extra Curriculum | 03:57 |
217 | What Is a Machine Learning Research Paper? | 07:35 |
218 | Why Replicate a Machine Learning Research Paper? | 03:14 |
219 | Where Can You Find Machine Learning Research Papers and Code? | 08:19 |
220 | What We Are Going to Cover | 08:22 |
221 | Getting Setup for Coding in Google Colab | 08:22 |
222 | Downloading Data for Food Vision Mini | 04:03 |
223 | Turning Our Food Vision Mini Images into PyTorch DataLoaders | 09:48 |
224 | Visualizing a Single Image | 03:46 |
225 | Replicating a Vision Transformer - High Level Overview | 09:54 |
226 | Breaking Down Figure 1 of the ViT Paper | 11:13 |
227 | Breaking Down the Four Equations Overview and a Trick for Reading Papers | 10:56 |
228 | Breaking Down Equation 1 | 08:15 |
229 | Breaking Down Equations 2 and 3 | 10:04 |
230 | Breaking Down Equation 4 | 07:28 |
231 | Breaking Down Table 1 | 11:06 |
232 | Calculating the Input and Output Shape of the Embedding Layer by Hand | 15:42 |
233 | Turning a Single Image into Patches (Part 1: Patching the Top Row) | 15:04 |
234 | Turning a Single Image into Patches (Part 2: Patching the Entire Image) | 12:34 |
235 | Creating Patch Embeddings with a Convolutional Layer | 13:34 |
236 | Exploring the Outputs of Our Convolutional Patch Embedding Layer | 12:55 |
237 | Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings | 10:00 |
238 | Visualizing a Single Sequence Vector of Patch Embeddings | 05:04 |
239 | Creating the Patch Embedding Layer with PyTorch | 17:02 |
240 | Creating the Class Token Embedding | 13:25 |
241 | Creating the Class Token Embedding - Less Birds | 13:25 |
242 | Creating the Position Embedding | 11:26 |
243 | Equation 1: Putting it All Together | 13:26 |
244 | Equation 2: Multihead Attention Overview | 14:31 |
245 | Equation 2: Layernorm Overview | 09:04 |
246 | Turning Equation 2 into Code | 14:34 |
247 | Checking the Inputs and Outputs of Equation | 05:41 |
248 | Equation 3: Replication Overview | 09:12 |
249 | Turning Equation 3 into Code | 11:26 |
250 | Transformer Encoder Overview | 08:51 |
251 | Combining Equation 2 and 3 to Create the Transformer Encoder | 09:17 |
252 | Creating a Transformer Encoder Layer with In-Built PyTorch Layer | 15:55 |
253 | Bringing Our Own Vision Transformer to Life - Part 1: Gathering the Pieces of the Puzzle | 18:20 |
254 | Bringing Our Own Vision Transformer to Life - Part 2: Putting Together the Forward Method | 10:42 |
255 | Getting a Visual Summary of Our Custom Vision Transformer | 07:14 |
256 | Creating a Loss Function and Optimizer from the ViT Paper | 11:27 |
257 | Training our Custom ViT on Food Vision Mini | 04:30 |
258 | Discussing what Our Training Setup Is Missing | 09:09 |
259 | Plotting a Loss Curve for Our ViT Model | 06:14 |
260 | Getting a Pretrained Vision Transformer from Torchvision and Setting it Up | 14:38 |
261 | Preparing Data to Be Used with a Pretrained ViT | 05:54 |
262 | Training a Pretrained ViT Feature Extractor Model for Food Vision Mini | 07:16 |
263 | Saving Our Pretrained ViT Model to File and Inspecting Its Size | 05:14 |
264 | Discussing the Trade-Offs Between Using a Larger Model for Deployments | 03:47 |
265 | Making Predictions on a Custom Image with Our Pretrained ViT | 03:31 |
266 | PyTorch Paper Replicating: Main Takeaways, Exercises and Extra-Curriculum | 06:51 |
267 | What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model | 09:36 |
268 | Three Questions to Ask for Machine Learning Model Deployment | 07:14 |
269 | Where Is My Model Going to Go? | 13:35 |
270 | How Is My Model Going to Function? | 08:00 |
271 | Some Tools and Places to Deploy Machine Learning Models | 05:50 |
272 | What We Are Going to Cover | 04:02 |
273 | Getting Setup to Code | 06:16 |
274 | Downloading a Dataset for Food Vision Mini | 03:24 |
275 | Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments | 08:00 |
276 | Creating an EffNetB2 Feature Extractor Model | 09:46 |
277 | Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms | 06:30 |
278 | Creating DataLoaders for EffNetB2 | 03:32 |
279 | Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves | 09:16 |
280 | Saving Our EffNetB2 Model to File | 03:25 |
281 | Getting the Size of Our EffNetB2 Model in Megabytes | 05:52 |
282 | Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model | 06:35 |
283 | Creating a Vision Transformer Feature Extractor Model | 07:52 |
284 | Creating DataLoaders for Our ViT Feature Extractor Model | 02:31 |
285 | Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves | 06:20 |
286 | Saving Our ViT Feature Extractor and Inspecting Its Size | 05:09 |
287 | Collecting Stats About Our ViT Feature Extractor | 05:52 |
288 | Outlining the Steps for Making and Timing Predictions for Our Models | 11:16 |
289 | Creating a Function to Make and Time Predictions with Our Models | 16:21 |
290 | Making and Timing Predictions with EffNetB2 | 10:44 |
291 | Making and Timing Predictions with ViT | 07:35 |
292 | Comparing EffNetB2 and ViT Model Statistics | 11:32 |
293 | Visualizing the Performance vs Speed Trade-off | 15:55 |
294 | Gradio Overview and Installation | 08:40 |
295 | Gradio Function Outline | 08:50 |
296 | Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs | 09:52 |
297 | Creating a List of Examples to Pass to Our Gradio Demo | 05:27 |
298 | Bringing Food Vision Mini to Life in a Live Web Application | 12:13 |
299 | Getting Ready to Deploy Our App Hugging Face Spaces Overview | 06:27 |
300 | Outlining the File Structure of Our Deployed App | 08:12 |
301 | Creating a Food Vision Mini Demo Directory to House Our App Files | 04:12 |
302 | Creating an Examples Directory with Example Food Vision Mini Images | 09:14 |
303 | Writing Code to Move Our Saved EffNetB2 Model File | 07:43 |
304 | Turning Our EffNetB2 Model Creation Function Into a Python Script | 04:02 |
305 | Turning Our Food Vision Mini Demo App Into a Python Script | 13:28 |
306 | Creating a Requirements File for Our Food Vision Mini App | 04:12 |
307 | Downloading Our Food Vision Mini App Files from Google Colab | 11:31 |
308 | Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically | 13:37 |
309 | Running Food Vision Mini on Hugging Face Spaces and Trying it Out | 07:45 |
310 | Food Vision Big Project Outline | 04:18 |
311 | Preparing an EffNetB2 Feature Extractor Model for Food Vision Big | 09:39 |
312 | Downloading the Food 101 Dataset | 07:46 |
313 | Creating a Function to Split Our Food 101 Dataset into Smaller Portions | 13:37 |
314 | Turning Our Food 101 Datasets into DataLoaders | 07:24 |
315 | Training Food Vision Big: Our Biggest Model Yet! | 20:16 |
316 | Outlining the File Structure for Our Food Vision Big | 05:49 |
317 | Downloading an Example Image and Moving Our Food Vision Big Model File | 03:34 |
318 | Saving Food 101 Class Names to a Text File and Reading them Back In | 06:57 |
319 | Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script | 02:21 |
320 | Creating an App Script for Our Food Vision Big Model Gradio Demo | 10:42 |
321 | Zipping and Downloading Our Food Vision Big App Files | 03:46 |
322 | Deploying Food Vision Big to Hugging Face Spaces | 13:35 |
323 | PyTorch Mode Deployment: Main Takeaways, Extra-Curriculum and Exercises | 06:14 |
324 | Introduction to PyTorch 2.0 | 06:02 |
325 | What We Are Going to Cover and PyTorch 2 Reference Materials | 01:22 |
326 | Getting Started with PyTorch 2.0 in Google Colab | 04:20 |
327 | PyTorch 2.0 - 30 Second Intro | 03:21 |
328 | Getting Setup for PyTorch 2.0 | 02:23 |
329 | Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.0 | 06:50 |
330 | Setting the Default Device in PyTorch 2.0 | 09:41 |
331 | Discussing the Experiments We Are Going to Run for PyTorch 2.0 | 06:43 |
332 | Creating a Function to Setup Our Model and Transforms | 10:18 |
333 | Discussing How to Get Better Relative Speedups for Training Models | 08:24 |
334 | Setting the Batch Size and Data Size Programmatically | 07:16 |
335 | Getting More Speedups with TensorFloat-32 | 09:54 |
336 | Downloading the CIFAR10 Dataset | 07:01 |
337 | Creating Training and Test DataLoaders | 07:39 |
338 | Preparing Training and Testing Loops with Timing Steps | 04:59 |
339 | Experiment 1 - Single Run without Torch Compile | 08:23 |
340 | Experiment 2 - Single Run with Torch Compile | 10:39 |
341 | Comparing the Results of Experiments 1 and 2 | 11:20 |
342 | Saving the Results of Experiments 1 and 2 | 04:40 |
343 | Preparing Functions for Experiments 3 and 4 | 12:42 |
344 | Experiment 3 - Training a Non-Compiled Model for Multiple Runs | 12:45 |
345 | Experiment 4 - Training a Compiled Model for Multiple Runs | 09:58 |
346 | Comparing the Results of Experiments 3 and 4 | 05:24 |
347 | Potential Extensions and Resources to Learn More | 05:51 |
348 | Thank You! | 01:18 |
Similar courses to PyTorch for Deep Learning
Spark and Python for Big Data with PySparkudemy
Duration 10 hours 35 minutes 43 seconds
Course
Python for Business Data Analytics & Intelligencezerotomastery.io
Duration 15 hours 25 minutes 6 seconds
Course
Deep Learning A-Z™: Hands-On Artificial Neural Networksudemy
Duration 22 hours 36 minutes 30 seconds
Course
2022 Python for Machine Learning & Data Science Masterclassudemy
Duration 44 hours 5 minutes 31 seconds
Course
Mathematical Foundations of Machine Learningudemy
Duration 16 hours 25 minutes 26 seconds
Course
Build Fast MasterclassBuildFast Academy
Duration 7 hours 22 minutes 11 seconds
Course
Complete linear algebra: theory and implementationudemy
Duration 32 hours 53 minutes 26 seconds
Course
Machine Learning in JavaScript with TensorFlow.jsudemy
Duration 6 hours 42 minutes 20 seconds
Course
Machine Learning A-Z : Become Kaggle Masterudemy
Duration 36 hours 23 minutes 54 seconds
Course
Data Analysis for Beginners: Excel & Pivot Tableszerotomastery.io
Duration 2 hours 10 minutes 21 seconds
Course