PyTorch Ultimate 2023: From Basics to Cutting-Edge

Become an expert applying the most popular Deep Learning framework PyTorch

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 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
Who this course is for:
Python developers willing to learn one of the most interesting and in-demand techniques


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