[NEW] 2026: Deep Learning Mastery With Tensorflow2.x & Keras

discover the basics of tensor data structures in TensorFlow, where tensors are multi-dimensional arrays. learn scalar and vector concepts, matrices, and word embeddings as directional data in a multi-dimensional space.

Understand tensors as TensorFlow's core units, including scalar, vector, matrix, and three-dimensional tensors with shapes and ranks. Implement and manipulate them using constants, variables, reshape, matmul, transpose, and inv.

Learn tensor basics, ranks, shapes, dtypes, and axis concepts, and apply operations like reshape, transpose, broadcasting, concatenation, and ragged tensors in TensorFlow.

Learn how tf.variable creates and manages TensorFlow variables backed by tensors, enabling reading, assignment, shape and dtype handling, and memory reuse for model parameters used by Keras.

Solve TensorFlow tensor and variable exercises in Python using Jupyter notebook, including creating constants and variables, printing shapes and dtypes, element-wise operations, dot products, zeros-to-nine updates, casting, and string tensors.

Explore eager execution versus graph execution in TensorFlow 2.x. Learn how eager runs operations immediately while graph execution builds a computation graph to optimize and parallelize selective steps.

Convert simple Python functions into TensorFlow functions with the tf.function decorator, enabling graph execution and distribution. Learn tf.print vs print behavior and autograph for Python control flow.

Understand the intuition behind neural networks, linking logistic regression with features, weights, bias, and activation to multi-layer architectures for binary classification and probability-based outputs.

Define the problem and goal, assess data quality and size, choose between machine learning or deep learning, and consider latency and pre-trained models.

Master the five steps of the deep learning model lifecycle with tf.keras: define the model (sequential or functional API), then compile, fit, evaluate, and predict.

Explore the differences between the sequential and functional APIs in TensorFlow Keras, building models with dense layers, input shapes, and flexible layer workflows.

Explore building and training a multilayer perceptron using the TensorFlow Keras sequential API on the mnist handwritten digits dataset, covering data loading, preprocessing, activation, dropout, and evaluation.

Explore the keras functional api by building a fashion-mnist classifier with scaling, one-hot encoding, flatten, batch norm, dense and dropout layers, and compare it with the sequential api.

Explore how cost and gradient descent minimize the linear regression cost function by updating beta values with a learning rate, and understand holdout and cross-validation strategies.

Explore TensorFlow and Keras model components, activations, losses, metrics, and callbacks to build and optimize neural networks, and learn last layer activations for regression and classification.

Explore how gradient descent aims for the global minimum, navigate local minima, and improve speed with batch, mini-batch, stochastic gradient descent, momentum, rmsprop, and the Adam optimizer.

Discover how keras loss functions measure error in regression and classification, including mean squared error, mean squared log error, mean absolute error, cross-entropy, hinge losses, and KL divergence.

Explore regression and classification performance metrics, including mean squared error, mean absolute error, mean absolute percentage error, binary/categorical/sparse categorical accuracy, and top-k accuracy for recommendation engines.

Improve model performance by building a base model with dense layers, choosing Adam as the go-to optimizer, and applying batch normalization, dropout, L1/L2 regularization, and proper activation.

Implement a feedforward neural network using Keras for binary classification, with data standardization, train-test split, class weights, dense layers, batch norm, dropout, and callbacks for checkpoints and early stopping.

Explore convolutional neural networks, how filters create feature maps for image abstraction, pooling and padding roles, and transitions to dense layers for classification with MNIST digits.

Implement a convolutional neural network on the cifar-10 dataset with TensorFlow and Keras, covering data loading, preprocessing, conv and pooling layers, flattening, dense layers, and model saving.

train a model to classify numbers using the mNIST dataset, applying either Keras sequential or functional API, with callbacks such as early stopping and checkpoints, then save, reload, and predict.

Master building a CNN classifier for MNIST using sequential and functional API in Keras, including data loading, normalization, one-hot encoding, and model training.

Train your CNN to classify fashion MNIST items using either sequential or functional API, leveraging a Keras callback, saving the model, and reloading to predict.

Build a fashion mNIST classifier with a CNN using the sequential API, including data reshaping, one-hot encoding, model training and evaluation, and saving the model and weights for deployment.

Explore keras preprocessing layers for image, text, and numeric data, including image resizing, rescaling, center cropping, and data augmentation, and apply these layers in model pipelines.

Learn to apply Keras preprocessing layers to the citrus leaves dataset, including resize, random height, zoom, flip, rotate, crop, translation, brightness, and contrast augmentations.

Master text pre-processing with Keras preprocessing layers, including text to word sequence, one-hot encoding and hashing trick. Create and fit a tokenizer, then pad sequences for model-ready input.

Apply Keras preprocessing layers to the cats and dogs dataset, using random crop, random flip, and random translation to transform and print sample images.

Solve a Keras preprocessing layers exercise with the cats and dogs dataset, loading images from a directory via Google Drive, applying resize and random augmentations like flip, rotate, and brightness.

Learn how transfer learning uses pre-trained base models from large datasets like ImageNet to extract features, then trains only new fully connected layers for your task.

Apply transfer learning with MobileNet v2 on a cats and dogs dataset, using an image data generator and data augmentation on gpu to train a two-class classifier.

Apply augmentation on x-ray images with random crop, flip, translation, rotation, zoom, and contrast. Then train a classifier using a pre-trained image model as the base for transfer learning.

Learn transfer learning for chest x-ray classification by fusing MobileNet v2 and Densenet 169 via a concatenate layer in a functional API, with data augmentation and two-class dataset.

Explore sequence models from one-to-one through many-to-many, including one-to-many and many-to-one, and understand recurrent neural networks, vanishing and exploding gradients, and the rise of LSTM and GRU.

Explore how LSTM uses a cell state with forget, input, and output gates to update memory across sequences, and how GRU offers a compact alternative with fewer gates.

Explore univariate time series with RNN, LSTM, and GRU, using a sliding window and min-max scaling to forecast the next value in airline passenger data.

Explore multiple time series with lstm, rnn, and gru as interchangeable layers, transform stock or product data into time-window inputs, and build a Python lstm model to predict future values.

Learn how text data is converted into embeddings to capture word relationships, from one-hot encoding to word2vec, glove, fasttext, and bert.

Explore text embeddings and building a sentiment classifier with word2vec, using a thousand reviews dataset with positive and negative labels, including tokenization, sequences, embedding layer, lstm, and dense output.

Learn to build an lstm text classifier for IMDb reviews using glove embeddings, tokenization, and padded sequences, then train, test, and evaluate binary classification accuracy while addressing overfitting with dropout.