Complete Guide to TensorFlow for Deep Learning with Python

Demonstrate how a perceptron mimics a biological neuron by coupling inputs, weights, and a bias with a simple activation function to form neural networks.

Learn to manually create a neural network in Python without TensorFlow, building basic object-oriented programming concepts, the super keyword, and simple graph operations like addition and matrix multiplication.

Create and initialize TensorFlow variables to hold weights and biases, and define placeholders to feed data in batches, specifying data types and shapes such as none or four by four.

Build your first TensorFlow neural network with placeholders, variables, graphs, and a session to fit a simple linear model using WX, B, sigmoid activation, and a cost function.

Build a simple neural network in TensorFlow, from placeholders and variables to a dense model with sigmoid activation, trained via gradient descent to minimize a cost function for regression.

Build a TensorFlow regression model to predict housing prices using the estimator API. Use California housing data with a 70/30 train-test split, min-max scaling, and a dense neural network regressor.

Demonstrates a tf regression solution using a jupyter notebook to load housing data, split train/test, scale features, and train a tf estimator dense regressor to predict median house value.

Explore a TensorFlow classification exercise using California census data to predict income brackets with numeric and categorical features, hash buckets, train-test split, and a linear estimator.

Learn how to save and restore TensorFlow models using a saver object, manage checkpoint directories, and restore parameters to continue training or evaluate on new data.

Explore pooling in CNNs, including 2x2 max-pooling with stride and data reduction, plus dropout regularization; review LeNet-5, AlexNet, and GoogleNet, and implement CNNs in TensorFlow on EMNIST.

Load and organize the 10 image data batches, reshape 3072 features into 32×32×3 images, apply one-hot encoding for 10 labels, and visualize samples for CNN prep.

Build a simple recurrent neural network by hand in TensorFlow, using two time steps and two weight sets WX and WY to illustrate unrolling through time.

Explore why vanishing gradients occur with sigmoid and tanh and how relu, leaky relu, and elu help, plus batch normalization and gradient clipping for time-series RNNs like LSTM and GRU.

Learn to build a recurrent neural network with TensorFlow's API to predict time series shifted by one step and generate sequences from a seed.

Create a basic recurrent neural network in TensorFlow to forecast a sinusoidal time series with one input and one output, using dynamic rnn, mean squared error, and the Adam optimizer.

Apply a time series solution using numpy, pandas, and matplotlib to load, scale, and forecast milk production with a TensorFlow lstm cell.

Explore building deep nets with TensorFlow abstractions by stacking layers and using high-level APIs like Estimator and Keras, and prepare data with scikit-learn using the wine dataset.

Explore the layers api in TensorFlow and how to build a network with placeholders, dense layers, and one-hot encoded labels, using softmax cross-entropy loss and a simple training loop.

Learn the basics of autoencoders, a simple neural network that reproduces its input at the output, using an encoder and decoder with a smaller hidden layer for compression.

Demonstrate dimensionality reduction with a linear autoencoder that performs principle component analysis by creating two new features from three input dimensions, using a hidden layer with no activation function.

Explore a linear autoencoder PCA exercise to reduce 30-dimensional data to two dimensions, preserving class separation. Follow the notebook steps—scale data, design layers, loss, and optimizer, and run the session.