PyTorch Ultimate 2024: From Basics to Cutting-Edge
- Description
- Curriculum
- FAQ
- Reviews
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:
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Introduction to Deep Learning
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high level understanding
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perceptrons
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layers
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activation functions
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loss functions
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optimizers
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Tensor handling
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creation and specific features of tensors
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automatic gradient calculation (autograd)
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Modeling introduction, incl.
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Linear Regression from scratch
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understanding PyTorch model training
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Batches
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Datasets and Dataloaders
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Hyperparameter Tuning
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saving and loading models
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Classification models
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multilabel classification
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multiclass classification
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Convolutional Neural Networks
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CNN theory
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develop an image classification model
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layer dimension calculation
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image transformations
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Audio Classification with torchaudio and spectrograms
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Object Detection
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object detection theory
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develop an object detection model
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YOLO v7, YOLO v8
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Faster RCNN
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Style Transfer
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Style transfer theory
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developing your own style transfer model
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Pretrained Models and Transfer Learning
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Recurrent Neural Networks
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Recurrent Neural Network theory
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developing LSTM models
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Recommender Systems with Matrix Factorization
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Autoencoders
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Transformers
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Understand Transformers, including Vision Transformers (ViT)
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adapt ViT to a custom dataset
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Generative Adversarial Networks
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Semi-Supervised Learning
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Natural Language Processing (NLP)
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Word Embeddings Introduction
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Word Embeddings with Neural Networks
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Developing a Sentiment Analysis Model based on One-Hot Encoding, and GloVe
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Application of Pre-Trained NLP models
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Model Debugging
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Hooks
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Model Deployment
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deployment strategies
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deployment to on-premise and cloud, specifically Google Cloud
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Miscellanious Topics
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ChatGPT
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ResNet
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Extreme Learning Machine (ELM)
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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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1Course OverviewVideo lesson
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2PyTorch IntroductionVideo lesson
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3System SetupVideo lesson
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4How to Get the Course MaterialVideo lesson
You can get the material from Github via https://github.com/DataScienceHamburg/PyTorchUltimateMaterial
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5Additional Information for Mac-UsersText lesson
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6Setting up the conda environmentVideo lesson
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7General Environment Setup Error HandlingText lesson
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8How to work with the courseVideo lesson
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23Section OverviewVideo lesson
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24NN from Scratch (101)Video lesson
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25Calculating the dot-product (Coding)Video lesson
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26NN from Scratch (Data Prep)Video lesson
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27NN from Scratch Modeling __init__ functionVideo lesson
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28NN from Scratch Modeling Helper FunctionsVideo lesson
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29NN from Scratch Modeling forward functionVideo lesson
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30NN from Scratch Modeling backward functionVideo lesson
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31NN from Scratch Modeling optimizer functionVideo lesson
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32NN from Scratch Modeling train functionVideo lesson
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33NN from Scratch Model TrainingVideo lesson
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34NN from Scratch Model EvaluationVideo lesson
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38Section OverviewVideo lesson
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39Linear Regression from Scratch (Coding, Model Training)Video lesson
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40Linear Regression from Scratch (Coding, Model Evaluation)Video lesson
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41Model Class (Coding)Video lesson
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42Exercise: Learning Rate and Number of EpochsVideo lesson
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43Solution: Learning Rate and Number of EpochsVideo lesson
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44Batches (101)Video lesson
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45Batches (Coding)Video lesson
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46Datasets and Dataloaders (101)Video lesson
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47Datasets and Dataloaders (Coding)Video lesson
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48Saving and Loading Models (101)Video lesson
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49Saving and Loading Models (Coding)Video lesson
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50Model Training (101)Video lesson
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51Hyperparameter Tuning (101)Video lesson
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52Hyperparameter Tuning (Coding)Video lesson
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53Section OverviewVideo lesson
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54Classification Types (101)Video lesson
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55Confusion Matrix (101)Video lesson
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56ROC curve (101)Video lesson
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57Multi-Class 1: Data PrepVideo lesson
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58Multi-Class 2: Dataset class (Exercise)Video lesson
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59Multi-Class 3: Dataset class (Solution)Video lesson
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60Multi-Class 4: Network Class (Exercise)Video lesson
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61Multi-Class 5: Network Class (Solution)Video lesson
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62Multi-Class 6: Loss, Optimizer, and Hyper ParametersVideo lesson
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63Multi-Class 7: Training LoopVideo lesson
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64Multi-Class 8: Model EvaluationVideo lesson
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65Multi-Class 9: Naive ClassifierVideo lesson
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66Multi-Class 10: SummaryVideo lesson
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67Multi-Label (Exercise)Video lesson
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68Multi-Label (Solution)Video lesson
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69Section OverviewVideo lesson
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70CNNs (101)Video lesson
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71CNN (Interactive)Video lesson
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72Image Preprocessing (101)Video lesson
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73Image Preprocessing (Coding)Video lesson
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74Binary Image Classification (101)Video lesson
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75Binary Image Classification (Coding)Video lesson
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76MultiClass Image Classification (Exercise)Video lesson
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77MultiClass Image Classification (Solution)Video lesson
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78Layer Calculations (101)Video lesson
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79Layer Calculations (Coding)Video lesson
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