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
Transformers
Natural Language Processing (NLP), e.g. Word Embeddings, Zero-Shot Classification, Similarity Scores
CNNs (Image-, Audio-Classification; Object Detection)
Style Transfer
Recurrent Neural Networks
Autoencoders
Generative Adversarial Networks
Recommender Systems
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

Requirements
basic Python knowledge

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 Learninghigh level understandingperceptronslayersactivation functionsloss functionsoptimizersTensor handlingcreation and specific features of tensorsautomatic gradient calculation (autograd)Modeling introduction, incl. Linear Regression from scratchunderstanding PyTorch model trainingBatchesDatasets and DataloadersHyperparameter Tuningsaving and loading modelsClassification modelsmultilabel classificationmulticlass classificationConvolutional Neural NetworksCNN theorydevelop an image classification modellayer dimension calculationimage transformationsAudio Classification with torchaudio and spectrogramsObject Detectionobject detection theorydevelop an object detection modelYOLO v7, YOLO v8Faster RCNNStyle TransferStyle transfer theorydeveloping your own style transfer modelPretrained Models and Transfer LearningRecurrent Neural NetworksRecurrent Neural Network theorydeveloping LSTM modelsRecommender Systems with Matrix FactorizationAutoencodersTransformersUnderstand Transformers, including Vision Transformers (ViT)adapt ViT to a custom datasetGenerative Adversarial NetworksSemi-Supervised LearningNatural Language Processing (NLP)Word Embeddings IntroductionWord Embeddings with Neural NetworksDeveloping a Sentiment Analysis Model based on One-Hot Encoding, and GloVeApplication of Pre-Trained NLP modelsModel DebuggingHooksModel Deploymentdeployment strategiesdeployment to on-premise and cloud, specifically Google CloudMiscellanious TopicsChatGPTResNetExtreme Learning Machine (ELM)Enroll right now to learn some of the coolest techniques and boost your career with your new skills.Best regards,Bert