DigitalSreeni Courses

Colab vs Colab Pro vs Purchasing Your Own System

Compare Colab tiers, Google Cloud Notebooks, and self-owned GPUs to choose computing infrastructure for deep learning projects.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Semantic Segmentation of Aerial Imagery Using U-Net

Train a U-Net to segment six land-cover classes in satellite imagery, from resizing and RGB mask encoding to patch-based prediction.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

3D U-Net for Semantic Segmentation

Apply a 3D U-Net to multiclass semantic segmentation of volumetric images, producing labeled 3D and multichannel outputs.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

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

Improve multiclass semantic segmentation by combining predictions from independently trained U-Net models with different backbones.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Classification of MNIST Sign Language Alphabets Using Deep Learning

Builds a convolutional neural network in Python to classify 25 sign-language alphabet images, then evaluates accuracy and confusion among commonly confused letters.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Skin Cancer Lesion Classification Using the HAM10000 Dataset

Train a deep learning model to classify seven types of skin lesions, handling imbalanced HAM10000 image data and evaluating predictions with confusion matrices.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

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

Learn to read and process GeoTIFF imagery with rasterio in Python, including band extraction, image clipping, plotting, and NDVI calculation.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

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

Learn how XGBoost builds on gradient boosting and compare its classification performance with random forests and deep learning.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

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

Learn how to choose hidden-layer and neuron counts for neural networks by relating architecture to data complexity and checking for underfitting or overfitting.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Application of Autoencoders - Image Colorization

Use convolutional autoencoders trained on grayscale/color image pairs to predict Lab color channels and colorize black-and-white images.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

An Overview of Deep Learning and Neural Networks

A concise introduction to deep learning and neural network terminology, illustrated through convolutional image-segmentation workflows.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Linear Regression Using Sci-Kit Learn in Python

Use Python and scikit-learn to fit a linear regression model to CSV data, evaluate its fit, and generate predictions.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

What Is Machine Learning Anyway?

A beginner-friendly overview of machine learning, using microscopy image segmentation to explain supervised, unsupervised, and deep learning.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Introduction to Pandas - Data Reading and Handling

Learn to load CSV files into pandas DataFrames, inspect their structure and data types, and create a histogram for basic analysis.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Grain Size Analysis in Python Using Watershed

Use watershed segmentation in Python to separate touching grains in microscope images and calculate their geometric measurements.

• YouTube
• 39 minutes
• Self-Paced
• Free Video