Complete Guide to TensorFlow for Deep Learning with Python

Explore supervised, unsupervised, and reinforcement learning with TensorFlow, from labeled data and classification to clustering and policy learning, with evaluation methods like accuracy and mean squared error.

This crash course basics section offers quick overviews of NumPy, pandas, Matplotlib, and scikit-learn for reading, manipulating, and cleaning data, supporting the TensorFlow-focused deep learning course.

Learn the fundamentals of NumPy, including creating arrays from lists, zeros and ones, linspace and range, random arrays and seeds, reshaping, indexing, slicing, and boolean masking.

Learn to read CSV files with pandas, access DataFrame columns, filter data with booleans, and describe statistics, then convert the data to a NumPy matrix for machine learning workflows.

Learn quick data visualization in Python using matplotlib and pandas, including simple line plots, scatter plots, image displays with imshow, axis tuning, labels, and color maps.

Learn scikit-learn preprocessing for deep learning with Python, focusing on scaling data with MinMaxScaler and performing train/test splits for model evaluation.

Practice core data skills with crash course review exercises: import libraries, create a matrix, visualize a plot, then use pandas, scale with sklearn, and perform a train-test split for TensorFlow.

This lecture covers crash course solutions, showing how to import libraries and create a NumPy matrix. It demonstrates converting to a Pandas dataframe, scaling with min-max scaler, and train-test split.

Explore neural networks by examining neurons, perceptrons, activation functions, cost functions, gradient descent, and backpropagation, then manually implement a simple neural network in Python before using TensorFlow.

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

Explore how neural networks build from inputs through hidden layers to outputs, and compare activation functions like sigmoid, tanh, and ReLU, including the role of z = wx + b.

Evaluate neural performance with cost functions by defining Y as the true value and A as the prediction, and compare quadratic cost with cross-entropy for faster learning via gradient descent.

Explore gradient descent as a method to minimize cost and see how backpropagation distributes error through neural networks in TensorFlow using the chain rule.

Explore the TensorFlow playground to visualize neural networks with classification and regression, tune training and test data splits, and experiment with features, neurons, activation functions, weights, biases, and losses.

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.

Define an operation class with input and output nodes, a global default graph, and an overridable compute method to support add, multiply, and matrix multiplication operations.

Create a neural network graph with placeholders and variables, connect them to operations, and implement a default graph with a post order traversal and a session to execute the computation.

Create and run a manual neural network session using postorder traversal to execute placeholders, variables, and operations. Implement a session class with a run method handling a feed dictionary.

Explore building a manual neural network for binary classification by defining a sigmoid activation, plotting it, and implementing a simple perceptron with two features.

Explore TensorFlow basics, including syntax, graphs, variables, and placeholders, then implement a neural network for supervised learning with regression and classification tasks through hands-on code-along lectures.

Explore TensorFlow basics by creating constants and tensors, executing operations in a session, and using interactive sessions in notebooks for matrix multiplication, zeros, ones, and random distributions.

Explore how TensorFlow constructs and runs graphs, where nodes and edges form operations and inputs, and learn about the default graph and basic session usage.

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 on a large synthetic dataset using batch training, placeholders, variables, and a gradient-descent optimizer, and explore tf.estimator for simpler supervised learning tasks.

Explore how to solve regression with the TensorFlow estimator API, including feature columns, input functions, and train, evaluate, and predict workflows using linear and DNN regressors.

work with a real diabetes dataset to build a binary classifier using TensorFlow's Estimator API, handling continuous and categorical features, normalization, feature columns, and age bucketing, with a train-test split.

Apply TensorFlow estimator workflows to build linear and dense neural network classifiers with pandas inputs. Use embedding columns for categorical data, train and evaluate, generate predictions with ROC and accuracy.

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.

Import census data with pandas, encode labels to 0/1, and split. Build a tf estimator with categorical and numeric features, train, predict, and evaluate with a classification report.

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 convolutional neural networks by reviewing neural network fundamentals, learning key theory for CNNs, and solving MNIST with both a normal network and CNN approaches, with practice exercises.

Review neural networks by summarizing single neuron math with input X and weights, activation functions like perceptron, sigmoid, tanh, ReLU, and learning via loss, gradient descent, and backpropagation across layers.

Master Xavier initialization and gradient descent concepts, including learning rate, batch size, and Adam, with a look at vanishing gradients. Explore regularization, dropout, data expansion, and MNIST for convolutional networks.

Explore the MNIST dataset: 55,000 training, 10,000 test, 5,000 validation images; 28x28 pixels flattened to 784 features, with one-hot labels for digits 0–9.

Explore MNIST digit classification with softmax regression in TensorFlow, using one-hot labels and vectorized computations. Set up data in a Jupyter notebook and compare with convolutional neural networks improvements.

Build a basic softmax regression model for MNIST by defining placeholders, weights, and bias, then train with cross-entropy loss and gradient descent. Evaluate accuracy using argmax against true labels.

Explore convolutional neural networks, from local receptive fields and padding to filters, stride, and pooling, linking tensors to image feature extraction and comparing dense neural networks with convolutional networks.

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.

Implement a MNIST convolutional neural network in TensorFlow, using a Jupyter notebook to build weights, biases, conv2d, pooling, two conv-pool blocks, a dense layer, and dropout.

train a cnn on mnist in TensorFlow using cross-entropy loss and an Adam optimizer, with dropout and test set accuracy improvements from convolution and pooling layers.

Master convolutional neural networks by classifying CFR 10 images in a 10-class CNN project, focusing on data handling, batch creation, and building CNN layers with a pre-filled notebook.

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 two-layer cnn in TensorFlow for 32x32x3 images using placeholders, dropout, convs, pooling, flattening, and a 10-unit output with cross-entropy loss and Adam optimization.

Explore recurrent neural networks and their ability to handle sequence information, including time series and recurrent neural network implementation. Learn about vanishing gradients, long short-term memory units, and Word2vec embeddings.

Explore the theory of recurrent neural networks for sequence data, memory cells, and time-step unrolling, and build TensorFlow models from manual to API-based sequence-to-sequence and vector outputs.

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.

Explore how LSTM and GRU units address vanishing gradients in recurrent networks, detailing forget and input gates, cell states, and peephole variants, with TensorFlow time series applications.

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 and batch a time series dataset for a recurrent neural network in TensorFlow. Produce X and Y targets with the sign function, and format batches for step prediction.

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.

Generate a new time series from a seed using a saved rnn model in TensorFlow, then seed with zeros and generate outputs to form a sinusoidal-like sequence.

Follow along to build a time series predictor with a recurrent neural network in TensorFlow, using the milk production dataset and a last 12 months train/test split with min-max scaling.

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

Discover word2vec embeddings in TensorFlow and learn why practical implementations favor Gensim for converting strings to vectors, with tutorials and install tips.

Learn how word2vec embeds words into a dense vector space, using cosine similarity to measure relationships, and contrast continuous bag of words and skip-gram models with noise-contrastive training.

Build Word2Vec in TensorFlow using the provided notebook, create data, vocabularies, and batches, and consider gensim for a simpler Word2Vec workflow.

Learn to implement the NCE loss for word embeddings in TensorFlow, initialize the NCE weights and biases, and visualize final embeddings with TSNE.

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.

Use the Estimator API to build a deep neural network classifier for the wine data set with numeric feature columns and three classes, training, evaluation, and prediction via input functions.

Learn how to use the Keras API as a TensorFlow abstraction by building a sequential model with dense layers and softmax. Compile with Adam and sparse_categorical_crossentropy, train, and evaluate.

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.

Explore TensorBoard, the built-in TensorFlow visualization tool, to visualize a simple graph, log it with a summary writer, and organize nodes with names and scopes for clear inspection.

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.

Build a linear autoencoder for dimensionality reduction, train a 2d latent space with MinMaxScaler, TensorFlow, and Adam optimization, then visualize the 2d scatter colored by label.

Explore a stacked autoencoder trained on MNIST digits in TensorFlow, building a 784-input encoder and 784-output decoder, and visualize reconstructions using mean squared error optimization.