Practical Deep Learning with Tensorflow 2.x and Keras

Learn regression from scratch by modeling price as a function of area, fitting a line to data points, and minimizing mean squared error to learn model parameters.

Learn binary and multiclass classification with deep neural networks, using sigmoid outputs and softmax decisions to predict discrete classes.

Recap fitting a line by adjusting model parameters, explain binary and multiclass classification, and show how simple classifiers evolve into deep neural networks with multiple hidden layers.

Set up a Windows development environment for TensorFlow 2.x by installing Python 3.8 with Anaconda, creating a virtual environment, and launching Jupyter notebooks.

Set up a cross-platform development environment for macOS and Linux by installing Python 3.8 with Anaconda, creating a virtual environment, and installing TensorFlow, Jupyter, and essential packages.

Prepare data for deep learning by loading the dataset, flattening 28x28 images to 784-length vectors, normalizing pixel values to [0,1], and applying one-hot encoding to labels.

Build a small sequential neural network in Keras, train with fit, compile with categorical cross-entropy, use SGD, and evaluate on test data to measure accuracy.

Learn to use Google Colab for free GPU/TPU access and persist learning progress by mounting Google Drive, saving model parameters, and handling six-hour session limits.

Explore a real-world protein sequence dataset: from protein IDs and amino acid sequences to labeling ATP binding behavior, emphasizing data preparation and dataset construction for deep learning.

Prepare and pre-process data by loading multiple fasta files, normalizing paths across platforms, extracting protein IDs and sequences with regular expressions, and saving timestamped outputs for model training.

Transform raw protein sequences into fixed-length numeric features by converting letters to indices, padding with underscores up to 500, and pairing sequences with ATP-binding labels for model training.

Examine data shapes, including seven data points with a 500-element vector and seven labels, then shuffle and apply a 66/32 percent train-test split to prepare the data for training.

Master how data shapes affect model input in deep learning, from five samples with 500 features to a proper flat representation, and why correct shaping matters.

Explore the functional API in TensorFlow and Keras, moving beyond sequential models to define inputs, embeddings, flattening, and dense layers, enabling multi-input graphs.

Compare dense networks and convolutional networks as convolutional networks reduce parameters and learn robust features with filters, using 3x3 windows on RGB images to classify a 60,000-image, 32×32, 10-class dataset.

Explore building a cnn in keras with tensorflow 2.x using cifar-10, covering conv2d layers, padding, relu and sigmoid activations, flattening, and dense classification.

Learn how 2x2 pooling, especially max pooling, reduces image size in CNNs to speed training, introduces translation invariance, and compares to average pooling, with practical wiring into a CNN architecture.

Use dropout to hide parts of input or activations, reducing overfitting and promoting generalization as the network learns underlying concepts by randomly setting a percentage of values to zero.

Learn to convert a CNN from sequential to the functional API in TensorFlow 2.x and Keras, using conv layers, max pooling, dropout, flatten, dense, and softmax.

Explore the inception module, a multi-branch convolution block inspired by a Google paper on going deeper with convolutions, using 1x1, 3x3, and 5x5 paths plus max pooling, then concatenating outputs.

Apply residual connections to convolutional models by adding a short-circuited path that feeds the input forward through two convolution layers, enabling element-wise addition with the original input and easing training.

Learn to save and load trained model weights using Keras model checkpoint callbacks, monitor validation accuracy, and resume training across sessions.

Review the official documentation and developer blog to understand the architecture, combine saved model components, and build image classification with very little data, while grasping the math.

Explore transfer learning, why it thrives with big data and GPUs, and how to reuse pretrained feature extractors with simple classifiers to train on limited data.

Compare training from scratch versus transfer learning with TensorFlow 2.x and Keras on a cats vs dogs dataset, highlighting speedups, validation accuracy, and on-device performance.

Master transfer learning by freezing the base model, training only the top layers for image feature extraction, and optionally fine-tuning to improve accuracy using a pretrained model.