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
Machine Learning
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1Course Overview
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2PyTorch Introduction
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3System Setup
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4How to Get the Course Material
You can get the material from Github via https://github.com/DataScienceHamburg/PyTorchUltimateMaterial
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5Additional Information for Mac-Users
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6Setting up the conda environment
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7General Environment Setup Error Handling
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8How to work with the course
Deep Learning Introduction
Model Evaluation
Neural Network from Scratch (opt. but highly recommended)
Tensors
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23Section Overview
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24NN from Scratch (101)
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25Calculating the dot-product (Coding)
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26NN from Scratch (Data Prep)
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27NN from Scratch Modeling __init__ function
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28NN from Scratch Modeling Helper Functions
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29NN from Scratch Modeling forward function
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30NN from Scratch Modeling backward function
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31NN from Scratch Modeling optimizer function
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32NN from Scratch Modeling train function
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33NN from Scratch Model Training
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34NN from Scratch Model Evaluation
PyTorch Modeling Introduction
Classification Models
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38Section Overview
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39Linear Regression from Scratch (Coding, Model Training)
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40Linear Regression from Scratch (Coding, Model Evaluation)
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41Model Class (Coding)
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42Exercise: Learning Rate and Number of Epochs
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43Solution: Learning Rate and Number of Epochs
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44Batches (101)
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45Batches (Coding)
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46Datasets and Dataloaders (101)
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47Datasets and Dataloaders (Coding)
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48Saving and Loading Models (101)
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49Saving and Loading Models (Coding)
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50Model Training (101)
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51Hyperparameter Tuning (101)
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52Hyperparameter Tuning (Coding)
CNN: Image Classification
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53Section Overview
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54Classification Types (101)
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55Confusion Matrix (101)
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56ROC curve (101)
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57Multi-Class 1: Data Prep
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58Multi-Class 2: Dataset class (Exercise)
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59Multi-Class 3: Dataset class (Solution)
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60Multi-Class 4: Network Class (Exercise)
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61Multi-Class 5: Network Class (Solution)
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62Multi-Class 6: Loss, Optimizer, and Hyper Parameters
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63Multi-Class 7: Training Loop
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64Multi-Class 8: Model Evaluation
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65Multi-Class 9: Naive Classifier
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66Multi-Class 10: Summary
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67Multi-Label (Exercise)
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68Multi-Label (Solution)
CNN: Audio Classification
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69Section Overview
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70CNNs (101)
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71CNN (Interactive)
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72Image Preprocessing (101)
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73Image Preprocessing (Coding)
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74Binary Image Classification (101)
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75Binary Image Classification (Coding)
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76MultiClass Image Classification (Exercise)
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77MultiClass Image Classification (Solution)
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78Layer Calculations (101)
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79Layer Calculations (Coding)