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PyTorch
PyTorch Ultimate 2024: From Basics to Cutting-Edge

PyTorch Ultimate 2024: From Basics to Cutting-Edge

Loại khoá học: Other IT & Software

Become an expert applying the most popular Deep Learning framework PyTorch

image
50.000 VND
90 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

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Mô tả

PyTorch is a Python framework developed by Facebook to develop and deploy Deep Learning models. It is one of the most popular Deep Learning frameworks nowadays.


In this course you will learn everything that is needed for developing and applying Deep Learning models to your own data. All relevant fields like Regression, Classification, CNNs, RNNs, GANs, NLP, Recommender Systems, and many more are covered. Furthermore, state of the art models and architectures  like Transformers, YOLOv7, or ChatGPT are presented.

It is important to me that you learn the underlying concepts as well as how to implement the techniques. You will be challenged to tackle problems on your own, before I present you my solution.


In my course I will teach you:

  • Introduction to Deep Learning

    • high level understanding

    • perceptrons

    • layers

    • activation functions

    • loss functions

    • optimizers

  • Tensor handling

    • creation and specific features of tensors

    • automatic gradient calculation (autograd)

  • Modeling introduction, incl.

    • Linear Regression from scratch

    • understanding PyTorch model training

    • Batches

    • Datasets and Dataloaders

    • Hyperparameter Tuning

    • saving and loading models

  • Classification models

    • multilabel classification

    • multiclass classification

  • Convolutional Neural Networks

    • CNN theory

    • develop an image classification model

    • layer dimension calculation

    • image transformations

    • Audio Classification with torchaudio and spectrograms

  • Object Detection

    • object detection theory

    • develop an object detection model

    • YOLO v7, YOLO v8

    • Faster RCNN

  • Style Transfer

    • Style transfer theory

    • developing your own style transfer model

  • Pretrained Models and Transfer Learning

  • Recurrent Neural Networks

    • Recurrent Neural Network theory

    • developing LSTM models

  • Recommender Systems with Matrix Factorization

  • Autoencoders

  • Transformers

    • Understand Transformers, including Vision Transformers (ViT)

    • adapt ViT to a custom dataset

  • Generative Adversarial Networks

  • Semi-Supervised Learning

  • Natural Language Processing (NLP)

    • Word Embeddings Introduction

    • Word Embeddings with Neural Networks

    • Developing a Sentiment Analysis Model based on One-Hot Encoding, and GloVe

    • Application of Pre-Trained NLP models

  • Model Debugging

    • Hooks

  • Model Deployment

    • deployment strategies

    • deployment to on-premise and cloud, specifically Google Cloud

  • Miscellanious Topics

    • ChatGPT

    • ResNet

    • Extreme Learning Machine (ELM)


Enroll right now to learn some of the coolest techniques and boost your career with your new skills.


Best regards,

Bert

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Yêu cầu

Nội dung khoá học

26 sections

Course Overview & System Setup

8 lectures
Course Overview
04:43
PyTorch Introduction
03:06
System Setup
04:22
How to Get the Course Material
02:16
Additional Information for Mac-Users
00:08
Setting up the conda environment
05:41
General Environment Setup Error Handling
00:26
How to work with the course
02:30

Machine Learning

3 lectures
Artificial Intelligence (101)
05:06
Machine Learning (101)
07:09
Machine Learning Models (101)
05:33

Deep Learning Introduction

8 lectures
Deep Learning General Overview
03:41
Deep Learning Modeling 101
03:33
Performance
02:33
From Perceptron to Neural Network
03:46
Layer Types
03:57
Activation Functions
04:14
Loss Functions
03:33
Optimizers
06:16

Model Evaluation

3 lectures
Underfitting Overfitting (101)
11:19
Train Test Split (101)
02:56
Resampling Techniques (101)
04:52

Neural Network from Scratch (opt. but highly recommended)

12 lectures
Section Overview
01:10
NN from Scratch (101)
11:47
Calculating the dot-product (Coding)
02:40
NN from Scratch (Data Prep)
04:18
NN from Scratch Modeling __init__ function
02:47
NN from Scratch Modeling Helper Functions
01:52
NN from Scratch Modeling forward function
01:10
NN from Scratch Modeling backward function
03:40
NN from Scratch Modeling optimizer function
00:59
NN from Scratch Modeling train function
06:22
NN from Scratch Model Training
01:49
NN from Scratch Model Evaluation
08:24

Tensors

3 lectures
Section Overview
01:02
From Tensors to Computational Graphs (101)
08:17
Tensor (Coding)
13:11

PyTorch Modeling Introduction

15 lectures
Section Overview
02:27
Linear Regression from Scratch (Coding, Model Training)
09:55
Linear Regression from Scratch (Coding, Model Evaluation)
07:09
Model Class (Coding)
14:05
Exercise: Learning Rate and Number of Epochs
00:41
Solution: Learning Rate and Number of Epochs
05:01
Batches (101)
02:59
Batches (Coding)
05:09
Datasets and Dataloaders (101)
04:22
Datasets and Dataloaders (Coding)
10:40
Saving and Loading Models (101)
03:12
Saving and Loading Models (Coding)
03:40
Model Training (101)
06:27
Hyperparameter Tuning (101)
09:17
Hyperparameter Tuning (Coding)
07:55

Classification Models

16 lectures
Section Overview
02:14
Classification Types (101)
05:12
Confusion Matrix (101)
06:16
ROC curve (101)
07:11
Multi-Class 1: Data Prep
02:35
Multi-Class 2: Dataset class (Exercise)
00:19
Multi-Class 3: Dataset class (Solution)
02:24
Multi-Class 4: Network Class (Exercise)
00:52
Multi-Class 5: Network Class (Solution)
02:20
Multi-Class 6: Loss, Optimizer, and Hyper Parameters
03:06
Multi-Class 7: Training Loop
03:21
Multi-Class 8: Model Evaluation
02:51
Multi-Class 9: Naive Classifier
02:27
Multi-Class 10: Summary
01:04
Multi-Label (Exercise)
06:38
Multi-Label (Solution)
15:38

CNN: Image Classification

11 lectures
Section Overview
01:40
CNNs (101)
10:04
CNN (Interactive)
03:44
Image Preprocessing (101)
08:38
Image Preprocessing (Coding)
09:27
Binary Image Classification (101)
01:16
Binary Image Classification (Coding)
18:41
MultiClass Image Classification (Exercise)
03:54
MultiClass Image Classification (Solution)
09:04
Layer Calculations (101)
06:53
Layer Calculations (Coding)
10:43

CNN: Audio Classification

5 lectures
Audio Classification (101)
03:26
Audio Classification (Exercise)
06:55
Audio Classification (Exploratory Data Analysis)
04:54
Audio Classification (Data Prep-Solution)
05:49
Audio Classification (Model-Solution)
11:48

CNN: Object Detection

13 lectures
Section Overview
00:46
Accuracy Metrics (101)
07:16
Object Detection (101)
03:06
Object Detection with detecto (Coding)
07:50
Training a Model on GPU for free (Coding)
03:14
YOLO (101)
05:53
Labeling Formats
04:09
YOLOv7 Project (101)
10:11
YOLOv7 Coding: Setup
07:20
YOLOv7 Coding: Data Prep
05:30
YOLOv7 Coding: Model Training
03:59
YOLOv7 Coding: Model Inference
03:46
YOLOv8 Coding: Model Training and Inference
08:35

Style Transfer

3 lectures
Section Overview
00:38
Style Transfer (101)
08:10
Style Transfer (Coding)
15:00

Pretrained Networks and Transfer Learning

3 lectures
Section Overview
00:50
Transfer Learning and Pretrained Networks (101)
04:52
Transfer Learning (Coding)
10:20

Recurrent Neural Networks

6 lectures
Section Overview
01:07
RNN (101)
05:58
LSTM (Coding)
16:24
LSTM Model Improvements
01:23
LSTM (Exercise)
02:44
LSTM (Solution)
01:08

Recommender Systems

5 lectures
Recommender Systems (101)
07:43
RecSys (Coding 1/4) - Dataset and Model Class
10:28
RecSys (Coding 2/4) - Model Training and Evaluation
08:16
RecSys (Coding 3/4) - Users and Items
03:47
RecSys (Coding 4/4) - Precision@k and Recall@k
13:40

Autoencoders

3 lectures
Section Overview
00:56
Autoencoders (101)
05:02
Autoencoders (Coding)
17:10

Generative Adversarial Networks

4 lectures
Section Overview
01:21
GANs (101)
11:49
GANs (Coding)
13:12
GANs (Exercise)
02:08

Graph Neural Networks

5 lectures
Graph Neural Networks (101)
12:05
Graph Introduction (Coding)
05:20
Node Classification (Coding: Data Prep)
08:29
Node Classification (Coding: Model Train)
11:06
Node Classification (Coding: Model Eval)
09:15

Transformers

3 lectures
Transformers 101
09:32
Vision Transformers (ViT)
06:01
Train ViT on Custom Dataset (Coding)
14:05

PyTorch Lightning

4 lectures
PyTorch Lighting (101)
05:09
PyTorch Ligthning (Coding)
09:49
Early Stopping (101)
03:47
Early Stopping (Coding)
03:51

Semi-Supervised Learning

4 lectures
Semi-Supervised Learning (101)
06:38
Supvervised Learning (Reference Model, Coding)
10:07
Semi-Supervised Learning (1/2: Dataset and Dataloader)
10:57
Semi-Supervised Learning (2/2 Modeling)
14:06

Natural Language Processing (NLP)

16 lectures
Natural Language Processing (101)
06:46
Word Embeddings Intro (101)
06:08
Sentiment OHE Coding Intro
01:49
Sentiment OHE (Coding)
12:20
Word Embeddings with NN (101)
08:52
GloVe: Get Word Embedding (Coding)
06:09
Glove: Find closest words (Coding)
06:11
GloVe: Word Analogy (Coding)
07:49
Glove Word Cluster (101)
01:20
GloVe Word (Coding)
15:54
Sentiment with Embedding (101)
01:23
Sentiment with Embedding (Coding)
10:54
Apply Pre-trained NLP Models (101)
03:48
Apply Pre-Trained NLP Models (Coding)
07:27
Zero-Shot Text Classification (101)
08:50
Zero-Shot Text Classification (Coding)
09:00

Miscellanious Topics

11 lectures
OpenAI ChatGPT (101)
16:38
Resnet (101)
09:58
Inception (101)
04:10
Inception Module (Coding)
18:54
Extreme Learning (101)
06:08
Image Similarity (Coding)
17:13
Retrieval Augmented Generation (101)
03:54
Claude 3 (101)
06:32
Claude 3 (Coding)
09:19
Agents (101)
14:15
Agents (Coding)
14:26

Model Debugging

2 lectures
Hooks (101)
03:42
Hooks (Coding)
11:08

Model Deployment

8 lectures
Model Deployment (101)
07:48
Flask On-Premise Hello World (Coding)
06:07
API On-Premise with Deep Learning Model (Coding)
13:59
API On-Premise: How to consume the data (Coding)
05:17
Google Cloud: Deploy Model Weights (Coding)
09:37
Google Cloud: Deploy REST API (Coding)
14:00
Extreme Learning (Coding)
06:54
Vector Database (101)
08:51

Final Section

2 lectures
Thank you & Further Resources
01:31
Bonus Lecture
00:59

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