Become an expert applying the most popular Deep Learning framework PyTorch
What you will learn
learn all relevant aspects of PyTorch from simple models to state-of-the-art models
deploy your model on-premise and to Cloud
Natural Language Processing (NLP), CNNs (Image-, Audio-Classification; Object Detection), RNNs, Transformers, Style Transfer, Autoencoders, GANs, Recommenders
adapt top-notch algorithms like Transformers to custom datasets
develop CNN models for image classification, object detection, Style Transfer
develop RNN models, Autoencoders, Generative Adversarial Networks
learn about new frameworks (e.g. PyTorch Lightning) and new models like OpenAI ChatGPT
use transfer learning
Description
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
English
language
Content
Course Overview & System Setup
Course Overview
PyTorch Introduction
System Setup
How to Get the Course Material
Setting up the conda environment
Machine Learning
Artificial Intelligence (101)
Machine Learning (101)
Machine Learning Models (101)
Deep Learning Introduction
Deep Learning General Overview
Deep Learning Modeling 101
Performance
From Perceptron to Neural Network
Layer Types
Activation Functions
Loss Functions
Optimizers
Model Evaluation
Underfitting Overfitting (101)
Train Test Split (101)
Resampling Techniques (101)
Tensors
Section Overview
From Tensors to Computational Graphs (101)
Tensor (Coding)
Modeling Introduction
Section Overview
Linear Regression from Scratch (Coding, Model Training)
Linear Regression from Scratch (Coding, Model Evaluation)
Model Class (Coding)
Exercise: Learning Rate and Number of Epochs
Solution: Learning Rate and Number of Epochs
Batches (101)
Batches (Coding)
Datasets and Dataloaders (101)
Datasets and Dataloaders (Coding)
Saving and Loading Models (101)
Saving and Loading Models (Coding)
Model Training (101)
Hyperparameter Tuning (101)
Hyperparameter Tuning (Coding)
Classification Models
Section Overview
Classification Types (101)
Confusion Matrix (101)
ROC curve (101)
Multi-Class 1: Data Prep
Multi-Class 2: Dataset class (Exercise)
Multi-Class 3: Dataset class (Solution)
Multi-Class 4: Network Class (Exercise)
Multi-Class 5: Network Class (Solution)
Multi-Class 6: Loss, Optimizer, and Hyper Parameters
Multi-Class 7: Training Loop
Multi-Class 8: Model Evaluation
Multi-Class 9: Naive Classifier
Multi-Class 10: Summary
Multi-Label (Exercise)
Multi-Label (Solution)
CNN: Image Classification
Section Overview
CNNs (101)
CNN (Interactive)
Image Preprocessing (101)
Image Preprocessing (Coding)
Binary Image Classification (101)
Binary Image Classification (Coding)
MultiClass Image Classification (Exercise)
MultiClass Image Classification (Solution)
Layer Calculations (101)
Layer Calculations (Coding)
CNN: Object Detection
Section Overview
Accuracy Metrics (101)
Object Detection (101)
Object Detection (Coding)
Training a Model on GPU for free (Coding)
Style Transfer
Section Overview
Style Transfer (101)
Style Transfer (Coding)
Pretrained Networks and Transfer Learning
Section Overview
Transfer Learning and Pretrained Networks (101)
Transfer Learning (Coding)
Recurrent Neural Networks
Section Overview
RNN (101)
LSTM (Coding)
LSTM (Exercise)
LSTM (Solution)
Autoencoders
Section Overview
Autoencoders (101)
Autoencoders (Coding)
Generative Adversarial Networks
Section Overview
GANs (101)
GANs (Coding)
GANs (Exercise)
Transformers
Transformers 101
Vision Transformers (ViT)
Train ViT on Custom Dataset (Coding)
PyTorch Lightning
PyTorch Lighting (101)
PyTorch Ligthning (Coding)
Early Stopping (101)
Early Stopping (Coding)
Closing Remarks
Thank you & Further Resources