Udemy - Deep Learning with PyTorch for Medical Image Analysis

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.6 GB
  • Uploaded Byfreecoursewb
  • Downloads79
  • Last checkedNov. 07th '21
  • Date uploadedNov. 04th '21
  • Seeders 6
  • Leechers6

Infohash : 7B9C5C3358F526A92D96AC44E8258F809E0398EE

Deep Learning with PyTorch for Medical Image Analysis



https://TutGator.com

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 3.64 GB | Duration: 12h 0m
Learn how to use Pytorch-Lightning to solve real world medical imaging tasks!
What you'll learn
Learn how to use NumPy
Learn classic machine learning theory principals
Foundations of Medical Imaging
Data Formats in Medical Imaging
Creating Artificial Neural Networks with PyTorch
Use PyTorch-Lightning for state of the art training
Visualize the decision of a CNN
2D & 3D data handling
Automatic Cancer Segmentation

Description
Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?

Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?

Then this is the right course for you!

Files:

[ TutGator.com ] Udemy - Deep Learning with PyTorch for Medical Image Analysis
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 (34.4 MB)
    • 1.1 Complete Course Material.html (0.1 KB)
    • 2. Installation and Environment Setup.mp4 (99.1 MB)
    • 2.1 Environment file.html (0.1 KB)
    • 3. Course Curriculum.mp4 (7.1 MB)
    10. Atrium-Segmentation
    • 1. 01-Introduction.mp4 (47.8 MB)
    • 2. Preprocessing-01-Visualization.mp4 (47.1 MB)
    • 3. Preprocessing-02-Processing.mp4 (43.5 MB)
    • 4. Dataset-01-Dataset-Creation.mp4 (40.4 MB)
    • 5. Dataset-02-Dataset-Validation.mp4 (24.5 MB)
    • 6. UNet.mp4 (64.5 MB)
    • 7. Train-01-Data-Loading-and-Loss.mp4 (29.5 MB)
    • 8. Train-02-Model-Creation.mp4 (50.7 MB)
    • 9. Train-03-Evaluation.mp4 (63.4 MB)
    11. Capstone-Project Lung Tumor Segmentation
    • 1. Introduction.mp4 (23.2 MB)
    • 2. Overview.mp4 (6.8 MB)
    • 3. Oversampling.mp4 (27.1 MB)
    • 4. Discussion.mp4 (27.1 MB)
    12. 3D Liver and Liver Tumor Segmentation
    • 1. Introduction.mp4 (27.1 MB)
    • 2. Data-Visualization.mp4 (31.6 MB)
    • 3. Model.mp4 (26.3 MB)
    • 4. Train-01-TorchIO-Dataset.mp4 (59.8 MB)
    • 5. Train-02-Model-Creation.mp4 (35.8 MB)
    • 6. Train-03-Evaluation.mp4 (43.1 MB)
    2. Crash Course NumPy
    • 1. Introduction to NumPy.mp4 (11.3 MB)
    • 2. NumPy Arrays.mp4 (50.8 MB)
    • 3. NumPy Arrays Part Two.mp4 (46.6 MB)
    • 4. NumPy Index Selection.mp4 (46.3 MB)
    • 5. NumPy Operations.mp4 (33.3 MB)
    • 6. NumPy Exercises.mp4 (11.5 MB)
    • 7. NumPy Exercise - Solutions.mp4 (48.6 MB)
    3. Machine Learning Concepts Overview
    • 1. What is Machine Learning.mp4 (20.0 MB)
    • 2. Supervised Learning.mp4 (39.9 MB)
    • 3. Overfitting.mp4 (26.2 MB)
    • 4. Evaluating Performance - Classification Error Metrics.mp4 (82.5 MB)
    • 5. Evaluating Performance - Regression Error Metrics.mp4 (23.6 MB)
    4. PyTorch Basics
    • 1. PyTorch Basics Introduction.mp4 (14.3 MB)
    • 2. Tensor Basics.mp4 (35.2 MB)
    • 3. Tensor Basics-Part Two.mp4 (66.8 MB)
    • 4. Tensor Operations.mp4 (58.6 MB)
    • 5. Tensor Operations-Part Two.mp4 (28.2 MB)
    • 6. PyTorch Basics - Exercise.mp4 (15.2 MB)
    • 7. PyTorch Basics - Exercise Solutions.mp4 (29.8 MB)
    5. CNN - Convolutional Neural Networks
    • 1. Introduction to CNNs.mp4 (4.6 MB)
    • 10. MNIST Data Revisited.mp4 (9.2 MB)
    • 11. MNIST with CNN - Code Along - Part One.mp4 (101.9 MB)
    • 12. MNIST with CNN - Code Along - Part Two.mp4 (87.5 MB)
    • 13. MNIST with CNN - Code Along - Part Three.mp4 (46.9 MB)
    • 14. Why do we need GPUs.mp4 (93.0 MB)
    • 15. Using GPUs for PyTorch.mp4 (96.5 MB)
    • 2. Understanding the MNIST data set.mp4 (14.4 MB)
    • 3. ANN with MNIST - Part One - Data.mp4 (97.9 MB)
    • 4. ANN with MNIST - Part Two - Creating the Network.mp4 (52.5 MB)
    • 5. ANN with MNIST - Part Three - Training.mp4 (78.2 MB)
    • 6. ANN with MNIST - Part Four - Evaluation.mp4 (50.2 MB)
    • 7. Image Filters and Kernels.mp4 (72.3 MB)
    • 8. Convolutional Layers.mp4 (58.0 MB)
    • 9. Pooling Layers.mp4 (27.6 MB)
    6. Medical Imaging - A short Introduction
    • 1. Introduction.mp4 (23.7 MB)
    • 2. X-RAY.mp4 (15.6 MB)
    • 3. CT.mp4 (27.1 MB)
    • 4. MRI.mp4 (18.0 MB)
    • 5. PET.mp4 (14.2 MB)
    7. Data Formats in Medical Imaging
    • 1. Introduction.mp4 (4.6 MB)
    • 2. DICOM.mp4 (20.0 MB)
    • 3. DICOM-in-Python.mp4 (89.0 MB)
    • 4. NIfTI.mp4 (10.0 MB)
    • 5. NIfTI-in-Python.mp4 (40.6 MB)
    • 6. Preprocessing.mp4 (65.1 MB)
    • 7. Preprocessing-in-Python-Part-1.mp4 (60.2 MB)
    • 8. Preprocessing-in-Python-Part-2.mp4 (60.3 MB)
    8. Pneumonia-Classification
    • 1. Introduction.mp4 (55.4 MB)
    • 2. Preprocessing.mp4 (86.0 MB)
    • 3. Train-01-Data-Loading.mp4 (69.8 MB)
    • 4. Train-02-Model-Creation.mp4 (82.8 MB)
    • 5. Train-03-Trainer.mp4 (20.7 MB)
    • 6. Train-04-Evaluation.mp4 (60.4 MB)
    • 7. Interpretability.mp4 (122.1 MB)
    9. Cardiac-Detection
    • 1. 01-Introduction.mp4 (27.1 MB)
    • 2. 02-Preprocessing.mp4 (66.9 MB)
    • 3. 03-Dataset-Part-1.mp4 (57.4 MB)
    • 4. 04-Dataset-Part-2.mp4 (32.3 MB)
    • 5. Train-01-Data-Loading.mp4 (22.5 MB)
    • 6. Train-02-Model-Creation.mp4 (95.7 MB)
    • 7. Train-03-Evaluation.mp4 (42.6 MB)
    • Bonus Resources.txt (0.3 KB)

Code:

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