Master Deep Learning for Computer Vision in TensorFlow[2026]

Explore tensor basics across zero-, one-, two-, and three-dimensional tensors, including casting, initialization, indexing, broadcasting, and common matrix operations, shapes, and tensor types like ranked, sparse, and string tensors.

Learn to create and cast tensors in TensorFlow across 0D to 4D, convert numpy arrays, generate identity, ones, zeros, and random or uniform tensors with seeds for reproducibility.

Learn how to index and slice tensors in TensorFlow across 1D, 2D, and 3D arrays, accessing elements, selecting ranges with start and end, negative indices, and steps.

Explore TensorFlow math functions, including absolute value, broadcasting, and safe divide no nan. Learn max, arg max, reduce operations, top k, and sigmoid for computer vision tasks.

Master linear algebra operations in TensorFlow, including matmul and transpose, handling shapes and errors, working with 3D tensors and batched matmul, and using insum for dot, outer, and elementwise products.

Explore common TensorFlow methods such as expanddims, squeeze, and reshape, and learn how to concatenate, stack, gather, and gather_nd to shape, broadcast, and index tensors for computer vision.

Master ragged tensors in TensorFlow, learn why non rectangular data needs them, and create them easily with ragged constants, boolean masks, and row splits.

Represent sparse data with TensorFlow sparse tensors using indices, values, and a dense shape. Convert to dense form and perform efficient computations.

Explore how TensorFlow handles strings with the TF strings module, create and manipulate 1D string tensors, join tensors with a separator, and convert integers to strings.

Learn how TensorFlow variables maintain a shared persistent state, fixed type and shape after initialization, and are updated with methods like assign, assign_add, and assign_sub.

Build a linear regression model to predict second-hand car prices using horsepower and other features, following the machine learning development life cycle.

Prepare car price data from the Kaggle second hand cars dataset by exploring features, visualizing relationships, shuffling data, and adaptive normalization for model training.

Explore building a linear regression model in TensorFlow using y = mx + c, tune weights and biases to fit data, and apply a sequential API with a dense layer.

Learn to measure and reduce regression error using mean squared error, mean absolute error, and Uber loss, with TensorFlow demonstrations.

Demonstrates training with stochastic gradient descent, updating weights via learning rate and loss derivatives toward better parameters. Shows sgd and Adam usage in TensorFlow Keras with epochs and loss tracking.

Define and apply the root mean square error metric to evaluate and compare models A and B in TensorFlow, using model.compile with the root mean square error.

Split data into train, validation, and test sets; apply train-only normalization; avoid data leakage; compare training versus validation performance; assess predictions on unseen test data.

Diagnose underfitting by evaluating training and validation loss, then deepen the network with more layers and nonlinear activations like ReLU, sigmoid, and tanche to improve performance.

Explore TensorFlow data sets via the TensorFlow data api to build efficient data processing pipelines with the from slices method, shuffle, batch, and prefetch for train, validation, and test sets.

Design a convolutional neural network to classify blood cell images as parasitic or uninfected, then prepare data, train, evaluate, and improve model performance for malaria detection.

Visualize a dataset by plotting 16 samples in a 4x4 grid with matplotlib, displaying images and their corresponding labels such as parasitized and uninfected.

Resize and rescale images to a 224 by 224 input, then normalize to 0–1 or standardize with mean and std for a ready, processed dataset.

Master how convolutional neural networks process images using kernels, padding, stride, and feature maps. Implement conv layers, pooling, and dense classifiers in TensorFlow Keras for computer vision.

Build a convolutional neural network in TensorFlow using the sequential API, defining input shape, conv and max-pooling layers, flatten, and dense layers for binary classification with sigmoid.

Explore binary cross entropy loss for binary classification, including y_true vs y_pred, loss behavior on correct and incorrect predictions, and practical TensorFlow usage with from_logits and label smoothing.

Train the convnet with fit on train and validation sets. Use batch size 32, fix outputs to one binary unit, add relu, batch norm, and binary accuracy up to 0.95.

Evaluate a trained TensorFlow-based model on a test dataset, achieving about 93% accuracy, and interpret predictions for parasitized versus uninfected samples, including visualizing nine predictions.

Save and load TensorFlow models for real-world use by exporting the model's configuration and weights, or just the weights, then store and load them from Google Drive in Colab.

Explore the functional API to build complex, multi-input, multi-output TensorFlow models, compare with sequential API, model subclassing, and custom layers, including feature extractors and shared layers.

Learn to implement a feature extractor as a TensorFlow layer subclass, wire it into a model via functional API and subclassing, and tune convolutional and pooling parameters for efficient training.

Build and integrate a custom dense layer in TensorFlow by inheriting from the layer class, defining trainable weights and biases, and applying optional activation in a sequential model.

Explore evaluation metrics for binary classification, including the confusion matrix, precision, recall, and AUC, and learn to tune thresholds with ROC curves.

Learn to build and plot a roc curve from fp, tp, and thresholds, and choose thresholds to balance false positives with true positives in parasite detection.

Create and leverage TensorFlow Keras callbacks, including custom callbacks, csv logger, learning rate scheduler, model checkpointing, early stopping, and reduce learning rate on plateau, to monitor and optimize training.

Balance speed and stability in training by using learning rate scheduling with TensorFlow callbacks, shifting from high to low rates and applying warm-up and cosine strategies.

Explore model checkpointing with validation loss monitoring, saving best weights via checkpoint callbacks, and learning rate scheduling on plateau in TensorFlow for efficient deep learning training.

Explore techniques to mitigate overfitting and underfitting, including data augmentation, dropout, regularization, early stopping, smaller networks, normalization, and hyperparameter tuning for robust deep learning models.

Explore data augmentation with TensorFlow using tf.image and Keras layers, applying brightness, saturation, flips, rotation, and crops to boost model robustness.

Apply mix up data augmentation in a tf.data pipeline by blending two samples using a beta distribution and updating the labels accordingly. Integrate with TensorFlow Probability and ensure proper preprocessing.

Implement cut mix data augmentation in TensorFlow 2, patching random regions from one image onto another with tf.data, including bounding boxes, lambda-based label updates, and clipping.

Master albumentations for fast, unified image augmentation with TensorFlow and PyTorch, preserving bounding boxes and masks for detection and segmentation, and building probabilistic transform pipelines.

Learn to switch between eager and graph modes in TensorFlow using the tf function decorator, and explore tracing, portability, and faster computations on graphics processing units.

Build a custom training loop in TensorFlow replacing the feed method, using gradient tape to compute losses and gradients and an optimizer to update weights during training and validation.

Learn to use the tensor board callback in TensorFlow to log training metrics, visualize graphs and histograms, and log confusion matrices and ROC plots for hyperparameter tuning and profiling.

View and understand TensorFlow model graphs, switching from operation-level graphs to a conceptual Keras model, visualizing dense layers, Adam optimizer, regularizers, with TensorBoard.

Explore hyperparameter tuning in deep learning with TensorFlow and TensorBoard, optimizing dropout, regularization, units, and learning rate. Compare grid search and random search, and use the profiler to improve performance.

Explore TensorBoard visualizations that profile input time, kernel launches, and Python overhead to optimize training steps. Use the input pipeline analyzer, trace viewer, and mixed-precision hints to reduce bottlenecks.