[2026] Tensorflow 2: Deep Learning & Artificial Intelligence

Explore Google Colab, a cloud notebook for writing Python deep learning code with free GPU or TPU access, preinstalled libraries, and easy sharing via Google Drive.

Colab remains the official environment; updates occur as needed, replacing fitgenerator with fit, using tensorflow datasets and keras augmentation for images, and text vectorization layer for NLP.

Frame machine learning as a geometry problem, exploring regression and classification under supervised learning with two-feature examples.

Explore linear classification with logistic regression in tensorflow 2.0 using sigmoid activation, w^T x plus b, binary cross-entropy, and training with Adam, epochs, and accuracy metrics.

Learn linear classification using TensorFlow 2 on the breast cancer data set to predict malignant versus benign tumors, with practical coding, data loading, preprocessing, and model training.

Prepare linear regression in tensorflow by loading and normalizing data, building a no-activation dense model, training with mean squared error and sgd, and applying log-transform insights to moore's law.

Learn how to generate predictions with TensorFlow models using fit and predict, convert probabilities to class outputs, and verify accuracy while handling 2D and 1D shapes.

Save a trained model with model.save(file_path) and reload it using tf.keras.models.load_model, mindful of a bug with explicit input layers and the workaround of specifying input shape in dense layers.

Explore feed forward neural networks, inspired by brain neurons, and how architecture, activation functions, and multi-class classification extend linear models to unstructured data like images, text, and sound.

Pace yourself by focusing on the TensorFlow 2 API first, using an API-first approach with optional math, and outline high-level goals with words, pictures, and code.

Discover how neural networks form nonlinear decision boundaries with multiple neurons and sigmoid activations. Learn that automatic feature learning via gradient descent replaces manual feature engineering.

Learn multiclass classification by applying the softmax activation to produce a probability distribution over K categories, and contrast it with binary sigmoid for two-class tasks.

Explore how images are represented for deep learning: RGB color channels form a 3D tensor, 8-bit values scaled to 0–1, with grayscale as 2D, and flattening into vectors.

Build a 10-class handwritten digit classifier on the M.A. dataset of 28x28 grayscale images using a feedforward network with a 128-unit dense layer, dropout, softmax, and sparse categorical cross entropy.

Explore a tensorflow 2.0 neural network for image classification on the m.a. dataset. Load and normalize data, build a 2-layer model with dropout, and evaluate with a confusion matrix.

Explore a synthetic data regression with a neural network of two dense layers and a cosine target, visualizing the three-dimensional data and its predicted surface, noting extrapolation limits.

Discover convolution basics in neural networks with input and output images, a filter (kernel), and padding; learn how add and multiply produce blur, edge detection, and valid, same, full modes.

Demonstrate the equivalence of convolution and matrix multiplication, using a one-dimensional example to show how repeating the filter creates a matrix; explain weight sharing for translational invariance and efficiency.

Learn the typical cnn architecture: convolution and pooling layers shrink images while increasing feature maps, then flatten and dense layers, with stride and global pooling for varying sizes.

Build a CNN in TensorFlow 2.0 for fashion mnist, with three 3x3 conv layers, dropout, and softmax, reshaping 28x28 grayscale images and evaluating via confusion matrices.

Explore image classification with a TensorFlow 2 convolutional neural network on CIFAR-10, via a prepared code lab notebook that builds, trains, and evaluates the model, covering data loading and overfitting.

Learn to boost model performance with data augmentation using TensorFlow's Keras image data generator, generating augmented batches on the fly with rotation, shift, brightness, zoom, and flips to improve generalization.

Introduce batch normalization to normalize data at every layer, learn gamma and beta for optimal scale and shift, and understand its regularization effects in CNNs.

Learn about sequence data and time series in deep learning, including shapes n by t by d, one- and multi-dimensional data, and practical examples like stock, weather, speech, and text.

Forecast time series by predicting multiple future values over a horizon. Use linear regression as a baseline, then extend to multi-step forecasts with sequential inputs and past values.

Show how a linear model learns a sine wave using an R2 model with two pass values of the time series to perfectly forecast a non-linear time series.

Explore recurrent neural networks and how they model sequences with hidden states and time-step dependencies. Learn about shared weights across time steps and per-time-step predictions.

Learn to build a simple rnn in TensorFlow 2.0: load synthetic data, shape inputs to t by d, instantiate the rnn layer, and train, evaluate, and predict while handling shapes.

Explore using a recurrent neural network for time series prediction with sine waves, noise, and comparisons to autoregressive linear models. Learn about activation effects on forecasts and evaluation.

Explore how shapes influence art understanding. Track shapes with a manual recurrent neural network forward pass, detailing t, d, m, k, and the associated weight matrices and biases.

Compare GRU and LSTM architectures, showing how forget, input, and output gates control the hidden and cell states to preserve long-term dependencies; two papers suggest LSTM often outperforms GRU.

Predict stock returns with LSTMs using real Starbucks data, exploring open, high, low, close, and volume, and applying scaling, windowing, and a supervised 10-step window approach.

reframe stock price prediction to stock return forecasting, and demonstrate vectorized data preparation, normalization, and training an autoregressive lstm model for single and multi-step forecasts.

Build and evaluate an LSTM-based stock return predictor using open, high, low, close and volume data, framing it as a binary up/down classification and highlighting overfitting and historical data limitations.

Explore different forecasting strategies, including one-step versus multi-step forecasts, baseline naive forecasts, and multi-output models, clarifying when to use each and concepts like the random walk.

Convert words to integers and map them to dense vectors with an embedding layer, replacing costly one-hot encoding. Train embeddings with gradient descent, yielding a trainable t-by-d input for RNNs.

Explore how one dimensional convolution applies to text by treating embeddings as a sequence, using sliding filters, pooling, and dense layers for text classification.

Explore two transfer learning approaches: training the full cnn with data augmentation versus precomputing feature vectors z for logistic regression, noting tradeoffs in time and generalization.

Learn transfer learning with data augmentation in TensorFlow using a pre-trained VGG16 model, including input preprocessing, augmentation layers, and a dense classifier, with a comparison to a non-augmented approach.

Explains transfer learning without data augmentation by extracting feature vectors from a pre-trained model and training a top classifier, while highlighting vector representations that bridge vision and NLP.