PyTorch for Deep Learning and Computer Vision

Name: PyTorch for Deep Learning and Computer Vision
Price: 9 USD
Availability: InStock

10h 20m 51s

English

Paid

November 29, 2024

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models.

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#	Title	Duration
1	Introduction	01:48
2	Finding the codes (Github)	00:47
3	A Look at the Projects	02:42
4	Intro	00:19
5	1 Dimensional Tensors	08:54
6	Vector Operations	05:24
7	2 Dimensional Tensors	05:31
8	Slicing 3D Tensors	03:04
9	Matrix Multiplication	03:22
10	Gradient with PyTorch	04:24
11	Outro	00:14
12	Intro	00:45
13	Making Predictions	06:16
14	Linear Class	04:30
15	Custom Modules	08:10
16	Creating Dataset	10:36
17	Loss Function	03:34
18	Gradient Descent	04:42
19	Mean Squared Error	03:16
20	Training - Code Implementation	11:37
21	Outro	00:32
22	Intro	00:35
23	What is Deep Learning	01:20
24	Creating Dataset	09:35
25	Perceptron Model	11:57
26	Model Setup	11:23
27	Model Training	10:39
28	Model Testing	05:24
29	Outro	00:24
30	Intro	00:29
31	Non-Linear Boundaries	03:12
32	Architecture	09:07
33	Feedforward Process	07:47
34	Error Function	04:11
35	Backpropagation	05:04
36	Code Implementation	08:50
37	Testing Model	15:22
38	Outro	00:23
39	Intro	00:37
40	MNIST Dataset	05:51
41	Training and Test Datasets	12:40
42	Image Transforms	16:27
43	Neural Network Implementation	30:45
44	Neural Network Validation	12:22
45	Final Tests	13:27
46	A note on adjusting batch size	01:29
47	Outro	00:22
48	Convolutions and MNIST	06:10
49	Convolutional Layer	18:12
50	Convolutions II	08:08
51	Pooling	14:12
52	Fully Connected Network	06:24
53	Neural Network Implementation with PyTorch	12:47
54	Model Training with PyTorch	17:19
55	The CIFAR 10 Dataset	01:45
56	Testing LeNet	09:52
57	Hyperparameter Tuning	07:53
58	Data Augmentation	12:26
59	Pre-trained Sophisticated Models	14:41
60	AlexNet and VGG16	27:35
61	VGG 19	09:46
62	Image Transforms	17:27
63	Feature Extraction	12:10
64	The Gram Matrix	12:02
65	Optimization	25:13
66	Style Transfer with Video	10:07
67	Python Crash Course - Free Access	00:56
68	Overview	00:49
69	Arrays vs Lists	12:04
70	Multidimensional Arrays	11:47
71	One Dimensional Slicing	03:34
72	Reshaping	03:35
73	Multidimensional Slicing	07:21
74	Manipulating Array Shapes	08:18
75	Matrix Multiplication	04:20
76	Stacking	13:51
77	Outro	00:09
78	Softmax	11:47
79	Cross Entropy	08:02