PyTorch Ultimate 2024: From Basics to Cutting-Edge from Udemy

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

What's inside

Learning objectives

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
Show more
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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
Show more
Show less

Syllabus

You will get an overview about what we will learn and your system will be prepared so that you are up and running.

Course Overview

PyTorch Introduction

System Setup

You can get the material from Github via https://github.com/DataScienceHamburg/PyTorchUltimateMaterial

Additional Information for Mac-Users

Setting up the conda environment

General Environment Setup Error Handling

How to work with the course

Machine Learning

Artificial Intelligence (101)

Machine Learning (101)

Machine Learning Models (101)

Learn the basics about Deep Learning

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)

In this lecture you will develop a complete neural network completely from scratch.

Section Overview

NN from Scratch (101)

Calculating the dot-product (Coding)

NN from Scratch (Data Prep)

NN from Scratch Modeling __init__ function

NN from Scratch Modeling Helper Functions

NN from Scratch Modeling forward function

NN from Scratch Modeling backward function

NN from Scratch Modeling optimizer function

NN from Scratch Modeling train function

NN from Scratch Model Training

NN from Scratch Model Evaluation

Tensors

From Tensors to Computational Graphs (101)

Tensor (Coding)

PyTorch Modeling Introduction

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)

You will learn to create classification models, so models which can distinguish categorical targets.

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)

These powerful techniques help you to create models for images.

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)

You will learn how to treat Audio files and convert the problem into an Image Classification problem.

Audio Classification (101)

Audio Classification (Exercise)

Audio Classification (Exploratory Data Analysis)

Audio Classification (Data Prep-Solution)

Audio Classification (Model-Solution)

CNN: Object Detection

Accuracy Metrics (101)

Object Detection (101)

Object Detection with detecto (Coding)

Training a Model on GPU for free (Coding)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Delves into top-notch algorithms like Transformers and models like ChatGPT, empowering learners to adapt these technologies to their own datasets

Suitable for both beginners looking to establish a foundation in PyTorch and intermediate learners seeking to advance their skills

Covers a comprehensive range of topics, including Regression, Classification, CNNs, RNNs, GANs, NLP, Recommender Systems, and more

Taught by Bert Gollnick, an experienced instructor recognized for his expertise in PyTorch

Provides hands-on exercises to challenge learners and reinforce their understanding

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch Ultimate 2024: From Basics to Cutting-Edge with these activities:

Organize Course Materials

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Lay a strong foundation for successful learning by organizing lecture notes, assignments, and other course materials for easy access and effective review.

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Create a dedicated folder or notebook for course materials.
Categorize and organize materials based on topics or sections.
Highlight important concepts and make notes for easier referencing.

Review Optimization Techniques

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Refresh your knowledge of optimization algorithms, ensuring a solid foundation for understanding model training and fine-tuning.

Browse courses on Optimization

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Recall concepts of gradient descent and its variants.
Review techniques like momentum, Adam, and RMSprop.
Explore adaptive learning rate optimization algorithms.

Review Linear Algebra Basics

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Brush up on foundational linear algebra concepts to enhance understanding of neural network operations and matrix manipulations.

Browse courses on Linear Algebra

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Recall concepts of vectors, matrices, and their operations.
Practice solving systems of linear equations.
Review matrix decompositions, such as eigen decomposition and singular value decomposition.

Five other activities

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Attend PyTorch Community Meetup

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Connect with fellow PyTorch enthusiasts, share knowledge, and stay updated on the latest developments in the community.

Browse courses on PyTorch

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Find a local PyTorch meetup group or online community.
Attend a meetup and engage in discussions and presentations.
Network with other attendees and learn about their experiences with PyTorch.

Explore Generative Adversarial Networks (GANs)

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Delve into the concepts and implementation of GANs, gaining insights into generative modeling techniques.

Browse courses on GANs

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Understand the theoretical foundations of GANs, including the generator and discriminator networks.
Implement a basic GAN in PyTorch.
Train and evaluate your GAN on different datasets.

Develop a Custom Image Classification Model

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Build a convolutional neural network from scratch to classify images, gaining hands-on experience with model development and training.

Browse courses on Image Classification

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Design the architecture of your CNN model.
Implement forward and backward passes for your CNN.
Train your model on a custom image dataset.
Evaluate the performance of your model using metrics like accuracy and loss.

Master Residual Networks

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Explore the intricacies of residual networks, implementing them in PyTorch to enhance your understanding of deep learning architectures.

Browse courses on ResNet

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Implement the building blocks of ResNet, including residual blocks and skip connections.
Train ResNet models on various datasets.
Analyze the performance and efficiency of ResNet compared to other CNN architectures.

Contribute to a PyTorch Project

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Engage with the PyTorch community by contributing to open-source projects, enhancing your understanding of the framework and its applications.

Browse courses on Open Source

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Identify an open-source PyTorch project that aligns with your interests.
Review the project's documentation and codebase.
Propose and implement a feature or improvement to the project.

Career center

Learners who complete PyTorch Ultimate 2024: From Basics to Cutting-Edge will develop knowledge and skills that may be useful to these careers:

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PyTorch Ultimate 2024

From Basics to Cutting-Edge

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What's inside

Learning objectives

Syllabus

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Activities

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