PyTorch Ultimate 2023: From Basics to Cutting-Edge
What you'll 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
- basic Python knowledge
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
creation and specific features of tensors
automatic gradient calculation (autograd)
Modeling introduction, incl.
Linear Regression from scratch
understanding PyTorch model training
Datasets and Dataloaders
saving and loading models
Convolutional Neural Networks
develop an image classification model
layer dimension calculation
Audio Classification with torchaudio and spectrograms
object detection theory
develop an object detection model
YOLO v7, YOLO v8
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
Understand Transformers, including Vision Transformers (ViT)
adapt ViT to a custom dataset
Generative Adversarial Networks
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
deployment to on-premise and cloud, specifically Google Cloud
Extreme Learning Machine (ELM)
Enroll right now to learn some of the coolest techniques and boost your career with your new skills.
Who this course is for:
- Python developers willing to learn one of the most interesting and in-demand techniques
I am a hands-on Data Scientist with a lot of domain knowledge on Renewable Energies, especially Wind Energy.
Currently I work for a leading manufacturer of wind turbines. I provide trainings on Data Science and Machine Learning with R and Python since many years.
I studied Aeronautics, and Economics. My main interests are Machine Learning and Data Science.