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Keras Deep Learning Course

A comprehensive deep learning course using Keras and TensorFlow, organized from beginner to advanced topics.

Course Structure

00 - Introduction

Foundation concepts and course overview.

01 - Basics

  • 00_fashion_mnist_basic_cnn.ipynb: Basic CNN on Fashion MNIST dataset
  • 01_learn_sine_regression.ipynb: Regression task - learning a sine function
  • 02_boston_house_price_regression.ipynb: House price prediction with regression

02 - Image Classification

  • 00_cifar10_classification.ipynb: CIFAR-10 image classification from scratch

03 - Advanced CNN Architectures

  • 00_densenet_architecture.ipynb: DenseNet architecture and implementation

04 - Regularization Techniques

  • 00_cifar10_regularization.ipynb: Regularization techniques applied to CIFAR-10
  • 01_imdb_overfit_underfit.ipynb: Understanding overfitting and underfitting with IMDB data

05 - Transfer Learning

  • 00_imagenet_transfer_learning.ipynb: Transfer learning using ImageNet pretrained models
  • 01_visualize_heat_maps.ipynb: Visualizing attention maps and model interpretability

06 - Image Segmentation

  • 00_cell_tissue_segmentation.ipynb: Semantic segmentation of cells and tissues
  • 01_mitosis_detection_brightfield.ipynb: Detecting mitosis in HeLa cells (brightfield)
  • 02_mitosis_detection_phase_contrast.ipynb: Detecting mitosis with phase contrast microscopy
  • 03_mitosis_xenopus_detection.ipynb: Mitosis detection in Xenopus cells

07 - Time Series

  • 00_time_series_training.ipynb: Training models for time series data
  • 01_time_series_prediction.ipynb: Predicting time series responses to stimuli

08 - NLP & Text Processing

  • 00_text_classification_welcome.ipynb: Introduction to text classification
  • 01_text_classification_deployment.ipynb: Deploying text classification models
  • 02_imdb_reviews_classification.ipynb: Sentiment analysis on IMDB reviews

09 - Advanced Topics

Physics-Informed Neural Networks (PINNs) & PDE Solving

  • 00_pde_introductory.ipynb: Introduction to solving PDEs with neural networks
  • 01_pde_modulus_anatomy.ipynb: NVIDIA Modulus framework anatomy
  • 02_pde_spring_mass_problem.ipynb: Spring-mass system modeling
  • 03_pde_spring_mass_inverse.ipynb: Inverse problem for spring-mass systems
  • 04_pde_diffusion_problem.ipynb: Diffusion equation solving
  • 05_pde_diffusion_parameterized.ipynb: Parameterized diffusion problems
  • 06_pde_cfd_problem.ipynb: Computational fluid dynamics problems
  • 07_pde_challenge_1.ipynb: Challenge problem 1
  • 08_pde_challenge_2.ipynb: Challenge problem 2
  • 09_pde_challenge_3.ipynb: Challenge problem 3
  • 10_pde_fno_darcy.ipynb: Fourier Neural Operators (FNO) for Darcy flow
  • 11_pde_parameterized_inverse.ipynb: Parameterized inverse problems

Video & Real-time AI

  • 12_video_ai_introduction.ipynb: Introduction to video AI
  • 13_video_realtime_ai_applications.ipynb: Real-time video AI applications
  • 14_video_deepstream_sdk.ipynb: NVIDIA DeepStream SDK introduction
  • 15_video_deepstream_application.ipynb: Building DeepStream applications
  • 16_video_mdnn_deepstream_application.ipynb: Multi-DNN DeepStream applications

Learning Path

For Beginners

  1. Start with 01_Basics - Learn fundamental concepts
  2. Move to 02_Image_Classification - Classic image tasks
  3. Try 03_Advanced_CNN - More sophisticated architectures

For Intermediate Learners

  1. Explore 04_Regularization - Prevent overfitting
  2. Learn 05_Transfer_Learning - Leverage pretrained models
  3. Study 06_Image_Segmentation - Pixel-level predictions

For Advanced Learners

  1. 07_Time_Series - Sequential data processing
  2. 08_NLP_Text - Natural language understanding
  3. 09_Advanced_Topics - Cutting-edge techniques:
    • Physics-informed networks for scientific computing
    • Real-time video processing with DeepStream

Prerequisites

  • Python 3.7+
  • TensorFlow/Keras
  • NumPy, Pandas, Matplotlib
  • Jupyter Notebook

Setup

pip install tensorflow keras numpy pandas matplotlib jupyter

Recommended Order

  1. Basics (01) → Foundation
  2. Classification (02) → Core skills
  3. CNN Architectures (03) → Deeper understanding
  4. Regularization (04) → Improve models
  5. Transfer Learning (05) → Efficient learning
  6. Segmentation (06) → Advanced vision
  7. Time Series (07) → Sequential data
  8. NLP (08) → Language understanding
  9. Advanced (09) → Specialized applications

Key Topics Covered

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) for time series
  • Transfer Learning and fine-tuning
  • Regularization and dropout
  • Batch normalization
  • Image segmentation
  • Text classification and NLP
  • Physics-informed neural networks
  • Real-time video processing

Notes

  • Each notebook is self-contained and can be run independently
  • Notebooks include explanations, visualizations, and exercises
  • Data is either downloaded automatically or provided in the repo