TensorFlow Course: Basic to Advanced Neural Network & Beyond

Create, manipulate, and index tensors in TensorFlow using Python, from scalars and vectors to matrices. Learn reshaping, basic math operations, and slicing to print and inspect tensor results.

Explore mathematical operations and broadcasting in the TensorFlow package, using constants and variables to perform add, multiply, and power operations with scalar and broadcasted results.

Explore variables and constants in TensorFlow with Python, creating scalar and vector constants, declaring and updating variables, and printing outputs to understand value assignment, types, and shapes.

Learn automatic differentiation with gradient tape in TensorFlow, computing scalar and multi-variable gradients using variables and operations to train neural networks and optimize models.

Learn to build simple models with TensorFlow core APIs, starting with a linear model and advancing to manual training, loss, gradients, and a polynomial example.

Explore the Keras API and its advantages as an open source, high-level framework. Build sequential and functional models with dense and convolutional layers using backends like TensorFlow for quick prototyping.

Explore building neural networks with Keras using sequential and functional APIs in TensorFlow, creating dense layers, specifying activations, inputs, and outputs, and inspecting model summaries.

Learn to compile and train Keras models with TensorFlow, building a sequential network, selecting activations, and using Adam, cross-entropy loss, and softmax for binary and multi-class classification.

Explore model evaluation and performance metrics in TensorFlow, including accuracy, precision, recall, and F1 score, using classification and regression examples with a synthetic data set.

Learn to load, normalize, and resize image datasets in Keras with TensorFlow, convert labels to one-hot vectors, and prep data for training and evaluation in a multi-class model.

Explore convolutional neural networks and their architecture, including convolutional, pooling, activation, and fully connected layers, with a TensorFlow tutorial on building CNNs for image classification and facial recognition.

Explore convolutional layers, pooling layers, and activation functions within neural networks, and learn how max pooling 2D shapes feature extraction in TensorFlow models.

build cnn models for image classification and object detection in the TensorFlow course, covering data loading, model construction with conv and dense layers, training, and basic YOLOv3 detections.

Learn to apply transfer learning with pre-trained CNN models like ResNet and VGG, using feature extraction or fine-tuning on image data to build strong classifiers.

Explore recurrent neural networks for processing sequential data, including vanilla RNNs and LSTM, and discover applications from natural language processing to stock and weather forecasting, plus a TensorFlow RNN.

Explore sequence data and time series analysis by generating synthetic daily series, visualizing it, and applying additive seasonal decomposition to reveal trend, seasonal, and residual components.

Learn to build and train recurrent neural networks for text generation and sentiment analysis in Python using a Keras sequential model, with preprocessing sequences and character-level generation.

Introduces long short term memory and gated recurrent unit networks for sequential data, demonstrates a sine wave prediction with LSTM, and explores GRU-based sequence classification in TensorFlow.

Explore natural language processing basics, including tokenization, parts of speech, named entity recognition, sentiment analysis, translation, and text summarization, and understand word embeddings like word2vec.

Explore text preprocessing in Python, including tokenization, stemming, and lemmatization, to prepare text data for natural language processing and machine learning models.

Explore word embeddings in Python for natural language processing, including Word2Vec, GloVe, and embedding layers, to convert words into vectors for machine learning.

Build rnn and transformer models for NLP tasks in TensorFlow, using embeddings and binary text classification. Train and evaluate with accuracy, and explore attention mechanisms in transformers.

Apply natural language processing with Python to build text classification and sentiment analysis models using sklearn and vector representations. Demonstrate machine translation and language detection with practical translation examples.

Create and customize layers and models in TensorFlow, including custom dense layers with initialization and weights, then build a custom model via subclassing.

Explore loading and preparing data with the TensorFlow datasets API. Cover image datasets like CIFAR-10 and text datasets like IMDb reviews, with supervised splits and automatic downloading.

Explore how TensorBoard visualizes training metrics like loss and accuracy, logs graphs and histograms, and helps debugging neural networks with TensorFlow workflows.

Master model optimization using hyperparameter tuning, grid search with cross-validation, and early stopping in TensorFlow neural networks and random forest classifiers to boost performance.

Learn to save and load TensorFlow models, preserving architecture, weights, and optimizer with the H5 format, then reload and verify with model.summary for deployment in cloud environments.

Deploy machine learning models with TensorFlow Serving, start a Docker container, and expose a REST API for production-ready inference. Learn to save, serve, and query a simple linear regression model.

Learn how to convert models to TensorFlow Lite, deploy them on mobile and embedded devices, and run real-time inference using interpreters and input-output details.

Explore how to train a model in Python and run it in the browser with TensorFlow.js, building a simple linear regression in JavaScript and converting/saving the model for browser deployment.

Learn to deploy Python applications and machine learning models to the cloud using Google Cloud Platform and App Engine, with gcloud sdk setup, authentication, and model storage.