Deep Learning : Image Classification with Tensorflow in 2025

Explore how TensorFlow represents sparse data with sparse tensors using indices, values, and a dense shape to store many zeros efficiently, and enable sparse to dense conversion for computation.

Learn how variables in TensorFlow maintain a shared state defined by an initial tensor, with the type and shape fixed, and update values using assign methods like assign_add and assign_sub.

Learn to build a linear regression model to predict car prices using multiple features, following the machine learning life cycle from task definition to training and evaluation.

Define and use the root mean square error to measure and compare model performance in TensorFlow, including RMSE in compilation, monitoring during training, and evaluation with X and y.

Split data into train, validation, and test sets to prevent leakage, evaluate unseen data, and apply normalization only on the train set during TensorFlow training.

Resize all inputs to 224 by 224 and normalize to the 0–1 range before feeding data into the model, using TensorFlow image resize and optional mean-std standardization.

Learn how to save and load TensorFlow models in Google Drive or Colab, including full model and weights, with the HDF5 format and checkpoints to resume training.

Learn how to create a custom dense layer in TensorFlow by defining weights and biases, handling input shapes, and integrating it into a sequential model with activation.

Explore strategies to combat overfitting and underfitting, including data augmentation, dropout, regularization, early stopping, smaller networks, hyperparameter tuning, and normalization, with practical TensorFlow examples.

Learn mixup data augmentation in TensorFlow by blending two images with a beta-distributed lambda, updating labels, and integrating the technique into a tf.data pipeline with preprocessing.

Learn to implement data augmentation with albumentations in TensorFlow and PyTorch, building a reusable transform pipeline that handles bounding boxes for object detection and masks for segmentation.

Learn to log training data with the tensorboard callback, visualize model graphs on a web interface, and tune hyperparameters using logs, distributions, histograms, ROC plots, confusion matrices, and profiling.

Explore hyperparameter tuning in TensorFlow using TensorBoard hparams: define, sweep, and evaluate choices like dropout, regularization, units, and learning rate with grid and random search.

Explore profiling and visualizations in tensorboard to identify input time as the bottleneck, then use input pipeline analyzer, trace viewer, and tf data bottlenecks to examine prefetch and 16-bit operations.