Practical Transfer Learning ( Deep Learning )in Python

Learn how transfer learning reuses knowledge from related data to speed up learning with limited labeled data, reducing training costs and GPU usage.

Explore how transfer learning leverages source knowledge to improve target tasks across domains with different feature spaces and distributions.

Learn what to transfer in transfer learning, using cancer data features from low-level to high-level, and when to apply it for limited power or data.

Explore transfer learning approaches with a pre-trained model. Replace and train the final softmax for 10 classes, use features with a support vector machine classifier, and unfreeze layers to fine-tune.

Learn practical transfer learning in deep learning by choosing fine-tuning versus training from scratch based on data size, and compare top-1 and top-5 accuracy.

Train an image classification model with teachable machine learning to recognize Indian rupee denominations by gathering webcam samples of 20, 100, 200, and 500 notes and training in the browser.

Distinguish two actors from Men in Black by voice using audio classification, background noise capture, sample extraction, and model training to evaluate accuracy.

Learn to build a pose classifier using teachable machine learning to distinguish straight versus tilted posture by collecting samples, training the model, and applying it for teachers and students.

Explore deploying a teachable machine model in the browser with tensorflow.js and p5, export options, and live classification of two classes via video.

Discover how Amplify, a JavaScript machine learning library, enables simple image and audio classification using prebuilt deep learning models built on TensorFlow.

Learn how a transfer learning model outputs labeled objects with confidence scores, how to interpret varying confidences, and how to apply thresholds to decide when to label results.

Learn how to prepare a custom image dataset for transfer learning by converting images into labeled tensors, creating class mappings, applying one-hot encoding, shuffling, and performing train–test splits.

Observe the downloading of three pre-trained models for transfer learning, including the Inception model and others. Weights appear only after download completes, showing where the models are stored.

Load images from 10 directories, assemble image data and labels, convert them into arrays, and save them for quick reload in a 10-class transfer learning classifier.

Load the 10-class monkey image dataset, convert labels to one-hot encoding, then shuffle and split into 80/20 train-test sets for x and y.

this lecture demonstrates building a resnet model for 10-class image classification using 224x224x3 input, extracting the last layer, with one-hot labels, an 80/20 train-test split, and a dense softmax output.

fit a custom resnet model for a 10-class classification task using training and validation data, evaluate on unknown data, and plot the learning curve showing decreasing loss and rising accuracy.

Learn practical transfer learning in python by loading images, building datasets, training a basic cnn, and exploring yolo and image segmentation models in a production-focused module.

Explore transfer learning with pre-trained models in Python using Keras applications like VGG16 and Inception, including image preprocessing, loading, decoding predictions into human-readable labels, and evaluating top-2 accuracy.

Prepare a custom image dataset for a transfer learning model by converting image folders into labeled data, applying image preprocessing, one-hot encoding, shuffling, and train-test splits.

Prepare a cat and dog dataset from kaggle’s 25000 images by splitting into training, validation, and testing sets, randomly selecting 2000 per class, and copying files into dedicated folders.

Scale image data to the 0–1 range, apply label encoding and one-hot encoding for cat and dog classes, and prepare the dataset for CNN model training.

Build and train a basic CNN model with convolutional layers, dropout, and dense layers in a sequential architecture to perform binary dog vs cat classification using sigmoid and binary crossentropy.