DigitalSreeni Courses

Colab vs Colab Pro vs Purchasing Your Own System

Explore cloud-based systems vs. personal workstations for GPU computing. Compare Colab options, Google Cloud Notebooks, and buying your own GPU, with recommendations for different needs.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Semantic Segmentation of Aerial Imagery Using U-Net

Learn to preprocess aerial imagery, train U-net for semantic segmentation, and make predictions using Python, focusing on data preparation and model implementation for satellite image analysis.

• YouTube
• 42 minutes
• On-Demand
• Free Video

3D U-Net for Semantic Segmentation

Learn to implement 3D U-Net for semantic segmentation on volumetric data like FIB-SEM, CT, and MRI. Covers data preparation, model architecture, training, and multichannel image processing using Python libraries.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Improving Semantic Segmentation - U-Net Performance via Ensemble of Multiple Trained Networks

Enhance U-Net performance for semantic segmentation using ensemble methods with ResNet34, Inception V3, and VGG16 networks. Includes code, dataset, and practical implementation tips.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Classification of MNIST Sign Language Alphabets Using Deep Learning

Learn to classify hand sign language alphabets using deep learning, covering data preparation, model creation, and result analysis for 25 classes.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Skin Cancer Lesion Classification Using the HAM10000 Dataset

Learn to classify skin cancer lesions using HAM10000 dataset. Covers data preparation, model building with Auto Keras, and result analysis for 7 types of skin lesions.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Working with Geotiff Files Using Rasterio in Python - Also Quick Demo of NDVI Calculation

Learn to manipulate geotiff files using rasterio in Python, including reading, plotting, and performing NDVI calculations. Gain practical skills for working with satellite imagery and geospatial data analysis.

• YouTube
• 24 minutes
• On-Demand
• Free Video

What Is XGBoost and Is It Really Better Than Random Forest and Deep Learning?

Explore XGBoost's advantages over Random Forest and Deep Learning, comparing performance and understanding its unique features for machine learning tasks.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Semantic Segmentation Made Easy - Using Segmentation Models Library

Learn to implement semantic segmentation using the Segmentation Models library, covering model types, backbones, data augmentation, preprocessing, and training on electron microscopy datasets.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Text Prediction Using LSTM

Learn to train LSTM networks for English text prediction, covering data preparation, model architecture, and character-level forecasting techniques.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Pretrained CNN Features for Semantic Segmentation Using Random Forest

Explore semantic segmentation using VGG16 features and Random Forest, offering improved results for limited training data compared to U-net.

• YouTube
• 22 minutes
• On-Demand
• Free Video

How Many Hidden Layers and Neurons Do You Need in Your Artificial Neural Network?

Explore the process of determining optimal hidden layers and neurons in neural networks through practical examples and code demonstrations.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Understanding the Training and Validation Loss Curves

Interpret loss curves from neural network training using Wisconsin breast cancer dataset. Explore various scenarios including underfitting and good fit.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Working With Imbalanced Data for ML - Demonstrated Using Liver Disease Data

Learn techniques for handling imbalanced datasets in machine learning, demonstrated using liver disease data. Covers data up-scaling, SMOTE, and ROCAUC evaluation to improve model accuracy.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Techniques to Work with Imbalanced Data for Machine Learning in Python

Learn 7 effective techniques to handle imbalanced datasets in machine learning, including sampling methods, algorithm selection, and synthetic data generation.

• YouTube
• 37 minutes
• On-Demand
• Free Video