TensorFlow 2.0 Practical

Build your first artificial neural network by constructing a simple neuron with weights and a bias to map temperature in C to Fahrenheit, trained with TensorFlow 2.0.

Use TensorFlow Playground to build a fully connected neural network with two inputs for classification and regression. Tune learning rate, activation, regularization, and epochs to minimize loss and learn boundaries.

Train a simple single neuron model in TensorFlow 2.0 on Google Colab to map Celsius to Fahrenheit, loading data from a CSV and mounting Google Drive.

Explore TensorFlow 2.0 practical project 1 by visualizing Celsius–Fahrenheit data with seaborn, creating training data, and building a simple dense neural network trained with Adam and mean squared error.

Develop a TensorFlow 2.0 regression model in Google Colab to map temperature to revenue, training and evaluating a neural network and comparing with linear regression.

Use scikit-learn linear regression to compare with a TensorFlow 2.0 neural network, training a single neuron and matching its slope and intercept.

Predict bike rental usage using a multi-layer perceptron in TensorFlow 2.0, with real-world weather, season, and hour features, training a fully connected network in Google Colab.

demonstrate a fully connected multi-layer perceptron network with two neurons and a three-input setup, using matrix multiplication, biases, and activation functions.

In TensorFlow 2.0 practical, learn supervised, unsupervised, and reinforcement learning, define epochs, and split data into training, validation, and testing to train, evaluate, and generalize neural networks.

Explore TensorFlow playground to build a dense, fully connected neural network with hidden layers. Learn activation functions, learning rate, epochs, regularization, and how training and testing losses reveal model generalization.

Learn back propagation to train artificial neural networks by performing forward propagation, calculating error, and updating weights via gradient descent using a learning rate.

Explore the bias-variance trade-off in machine learning by comparing linear regression and a high-order polynomial, emphasizing training versus testing performance and regularization in TensorFlow 2.0 practical.

Study regression metrics to evaluate model performance in TensorFlow 2.0, including MAE, MSE, RMSE, MAPE, and R-squared, plus the adjusted version. Understand residuals and predictions to assess fit.

Build and evaluate a TensorFlow 2.0 neural network on Google Colab using bike rental data features to train and test a regression model.

Visualize the dataset by formatting dates and plotting weekly, monthly, quarterly counts with seaborn pair plots and heatmap. Prepare numerical features for training and testing in TensorFlow 2.0 Practical.

Encode categorical features with one-hot encoding, merge with numerical data, drop unused columns, normalize the target, and split 80/20 for training and testing to train and evaluate the model.

Build and train a neural network using Keras sequential API, add dense layers with relu, linear output for regression; compile with Adam and mean squared error, and fit 20 epochs.

Evaluate and visualize a TensorFlow 2.0 model using Keras, training and validation loss, apply cross validation, check for overfitting, and assess performance with RMSE, MSE, MAE, R-squared, and predictions.

Train a fully neural regression model on King County house data from Kaggle to predict sale prices using features like bedrooms, bathrooms, square footage, year built, and location.

Explore predicting house prices with an artificial neural network in TensorFlow 2.0 using Google Colab, visualization, and a two-stage regression approach that improves R squared from 0.578 to about 0.87.

Learn to predict house prices with TensorFlow 2.0 by loading and visualizing data, selecting features such as bedrooms, bathrooms, and square footage, and scaling inputs and outputs.

Expand features and retrain a TensorFlow 2.0 regression model with 19 inputs, apply min-max scaling, split training and testing, and evaluate RMSE and R-squared.

Learn to read a confusion matrix for classification, identify true/false positives and negatives, and apply accuracy, error rate, precision, and recall through a cancer-patient example.

Build and evaluate a TensorFlow 2.0 neural network classifier for sentiment analysis on 3,000 Amazon reviews, using countvectorizer, data visualization, and data cleaning before training.

Visualize and clean the data by separating positive and negative reviews with the feedback column. Plot counts and distributions with seaborn, and examine ratings and variations such as fabric finishes.

Clean and engineer data by dropping date and rating, encoding variation with get_dummies, and concatenating results, then apply countvectorizer tokenization to reviews to create features (X) and output (Y).

Train a binary classifier with TensorFlow 2.0 and Keras, using two 400-neuron hidden layers with ReLU and a sigmoid output, 80/20 train-test split, Adam, binary cross-entropy, for 10 epochs.

Train a dense neural network to classify diabetes using a provided csv dataset and Colab skeleton notebook; evaluate the model with features like glucose, blood pressure, BMI, and age.

Build and evaluate a diabetes prediction model in TensorFlow 2.0 on Google Colab, covering data import, visualization, feature scaling, train-test split, model training, and performance reporting.

Train a neural network on the diabetes dataset in Google Colab, using features like pregnancies, age, blood pressure, and insulin, with scaling and evaluation.

Explore the diabetes dataset with TensorFlow 2.0, visualize with seaborn (count plot, pair plot, heatmap), and prepare data with cleaning, scaling, and a train-test split.

Apply convolutions with kernels to extract features and create feature maps, then use relu, pool, flatten, and feed into a fully connected network for TensorFlow 2.0.

Explore how relu adds non-linearity to feature maps by zeroing negatives and passing positives, boosting sparsity and training effectiveness in TensorFlow 2.0 cnn architectures and lenet.

Explore pooling and downsampling in CNNs, using 2x2 max pooling with stride 2 to reduce a 4x4 feature map, then flatten for a dense network that classifies digits.

Load the fashion mnist dataset from a csv, split into training and testing arrays, and visualize 28x28 grayscale samples with a 15 by 15 image grid using numpy and matplotlib.

Normalize and split fashion dataset, build and train a convolutional neural network with conv layers, pooling, and dense layers to classify 60,000 28 by 28 images into ten classes.

Evaluate a CNN on test data to achieve about 91% accuracy, explore validation data and cross-validation to improve generalization, and visualize predictions, confusion matrix, and a detailed classification report.

Train a deep network using the LeNet architecture to classify 43 traffic sign classes from 32x32 images in the German data set, applying TensorFlow 2.0 in Google Colab.

Explore the Lenet architecture, with convolutions, max pooling, and downsampling, and learn to implement Lenet-5 in TensorFlow 2.0 using Keras.

Evaluate a TensorFlow 2.0 model on a 43-class dataset, track training and validation accuracy and loss, and apply image augmentation to improve generalization.

Learn a practical tensorflow 2.0 workflow by loading the cifar-10 dataset from keras.datasets and inspecting train and test shapes, 50,000 and 10,000 images.

Train a fashion-MNIST classifier with convolution layers in TensorFlow 2.0, save and deploy the model on a server via TensorFlow Serving, and perform inference on uploaded images.

Save a trained TensorFlow 2.0 model in the saved model format with a versioned export path, preparing it for deployment via TensorFlow serving. Define inputs and outputs for smooth serving.

Save and explore a trained model with metagraph and signature, then serve it via TensorFlow Serving on port 8501 and perform three-sample JSON inferences.

Learn to use tensor board to visualize training progress, track accuracy and loss, and view the network architecture graph, including layer details and weight distributions.

Explore TensorFlow 2.0 distributed strategy, including mirrored strategy, to run a single model across multiple GPUs or TPUs. Build once, then run with the chosen strategy.