PyTorch for Deep Learning

Name: PyTorch for Deep Learning
Price: 9 USD
Availability: InStock

52h 27s

English

Paid

June 13, 2024

Course description

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.

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#1: PyTorch for Deep Learning

All Course Lessons (348)

#	Lesson Title	Duration
1	PyTorch for Deep Learning Demo	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

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