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PyTorch Ultimate 2024: From Basics to Cutting-Edge

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Bert Gollnick

8:21:37

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  • 1 - Course Overview.mp4
    04:43
  • 2 - PyTorch Introduction.mp4
    03:06
  • 3 - System Setup.mp4
    04:25
  • 4 - How to Get the Course Material.mp4
    02:49
  • 5 - Setting up the conda environment.mp4
    05:41
  • 6 - Artificial Intelligence 101.mp4
    05:06
  • 7 - Machine Learning 101.mp4
    07:09
  • 8 - Machine Learning Models 101.mp4
    05:33
  • 9 - Deep Learning General Overview.mp4
    03:41
  • 10 - Deep Learning Modeling 101.mp4
    03:33
  • 11 - Performance.mp4
    02:33
  • 12 - From Perceptron to Neural Network.mp4
    03:46
  • 13 - Layer Types.mp4
    03:57
  • 14 - Activation Functions.mp4
    04:14
  • 15 - Loss Functions.mp4
    03:33
  • 16 - Optimizers.mp4
    06:16
  • 17 - Underfitting Overfitting 101.mp4
    11:19
  • 18 - Train Test Split 101.mp4
    02:56
  • 19 - Resampling Techniques 101.mp4
    04:52
  • 20 - Section Overview.mp4
    01:02
  • 21 - From Tensors to Computational Graphs 101.mp4
    08:17
  • 22 - Tensor Coding.mp4
    13:11
  • 23 - Section Overview.mp4
    02:27
  • 24 - Linear Regression from Scratch Coding Model Training.mp4
    09:55
  • 25 - Linear Regression from Scratch Coding Model Evaluation.mp4
    07:09
  • 26 - Model Class Coding.mp4
    14:05
  • 27 - Exercise Learning Rate and Number of Epochs.mp4
    00:41
  • 28 - Solution Learning Rate and Number of Epochs.mp4
    05:01
  • 29 - Batches 101.mp4
    02:59
  • 30 - Batches Coding.mp4
    05:09
  • 31 - Datasets and Dataloaders 101.mp4
    04:22
  • 32 - Datasets and Dataloaders Coding.mp4
    10:40
  • 33 - Saving and Loading Models 101.mp4
    03:12
  • 34 - Saving and Loading Models Coding.mp4
    10:40
  • 35 - Model Training 101.mp4
    06:27
  • 36 - Hyperparameter Tuning 101.mp4
    09:17
  • 37 - Hyperparameter Tuning Coding.mp4
    07:55
  • 38 - Section Overview.mp4
    02:14
  • 39 - Classification Types 101.mp4
    05:12
  • 40 - Confusion Matrix 101.mp4
    06:16
  • 41 - ROC curve 101.mp4
    07:11
  • 42 - MultiClass 1 Data Prep.mp4
    02:35
  • 43 - MultiClass 2 Dataset class Exercise.mp4
    00:19
  • 44 - MultiClass 3 Dataset class Solution.mp4
    02:24
  • 45 - MultiClass 4 Network Class Exercise.mp4
    00:52
  • 46 - MultiClass 5 Network Class Solution.mp4
    02:20
  • 47 - MultiClass 6 Loss Optimizer and Hyper Parameters.mp4
    03:06
  • 48 - MultiClass 7 Training Loop.mp4
    03:21
  • 49 - MultiClass 8 Model Evaluation.mp4
    02:51
  • 50 - MultiClass 9 Naive Classifier.mp4
    02:27
  • 51 - MultiClass 10 Summary.mp4
    01:04
  • 52 - MultiLabel Exercise.mp4
    06:38
  • 53 - MultiLabel Solution.mp4
    15:38
  • 54 - Section Overview.mp4
    01:40
  • 55 - CNNs 101.mp4
    10:04
  • 56 - CNN Interactive.mp4
    03:44
  • 57 - Image Preprocessing 101.mp4
    08:38
  • 58 - Image Preprocessing Coding.mp4
    09:27
  • 59 - Binary Image Classification 101.mp4
    01:16
  • 60 - Binary Image Classification Coding.mp4
    18:41
  • 61 - MultiClass Image Classification Exercise.mp4
    03:54
  • 62 - MultiClass Image Classification Solution.mp4
    09:04
  • 63 - Layer Calculations 101.mp4
    06:53
  • 64 - Layer Calculations Coding.mp4
    10:43
  • 65 - Section Overview.mp4
    00:46
  • 66 - Accuracy Metrics 101.mp4
    07:16
  • 67 - Object Detection 101.mp4
    03:06
  • 68 - Object Detection Coding.mp4
    07:50
  • 69 - Training a Model on GPU for free Coding.mp4
    03:14
  • 70 - Section Overview.mp4
    00:38
  • 71 - Style Transfer 101.mp4
    08:10
  • 72 - Style Transfer Coding.mp4
    15:00
  • 73 - Section Overview.mp4
    00:50
  • 74 - Transfer Learning and Pretrained Networks 101.mp4
    04:52
  • 75 - Transfer Learning Coding.mp4
    10:20
  • 76 - Section Overview.mp4
    01:07
  • 77 - RNN 101.mp4
    05:58
  • 78 - LSTM Coding.mp4
    16:24
  • 79 - LSTM Exercise.mp4
    02:44
  • 80 - LSTM Solution.html
  • 81 - Section Overview.mp4
    00:56
  • 82 - Autoencoders 101.mp4
    05:02
  • 83 - Autoencoders Coding.mp4
    17:10
  • 84 - Section Overview.mp4
    01:21
  • 85 - GANs 101.mp4
    11:49
  • 86 - GANs Coding.mp4
    13:12
  • 87 - GANs Exercise.mp4
    02:08
  • 88 - Thank you & Further Resources.mp4
    01:31
  • Description


    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

    Who is this for?


  • Python developers willing to learn one of the most interesting and in-demand techniques
  • What You Need to Know?


  • basic Python knowledge
  • More details


    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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    Bert Gollnick
    Bert Gollnick
    Instructor's Courses
    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, Data Science, and Blockchain.
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 87
    • duration 8:21:37
    • English subtitles has
    • Release Date 2024/03/13