TensorFlow

Goals

What will you learn in this course:

1. Tensors:

• Tensors are the fundamental data structures in TensorFlow, representing multi-dimensional arrays of numerical values.
• Scalars are 0-dimensional tensors, vectors are 1-dimensional tensors, matrices are 2-dimensional tensors, and higher-dimensional tensors are arrays with more than two dimensions.
• Tensors can be constants (immutable), variables (mutable), or placeholders (for input data during computation).

2. Computational Graph:

• TensorFlow uses a computational graph to represent mathematical operations as nodes and data flow as edges.
• Nodes in the graph represent operations, such as addition, multiplication, and neural network layers.
• Edges represent the flow of tensors between operations, indicating the input-output relationships.

3. Sessions:

• TensorFlow uses sessions to execute operations and evaluate tensors within a computational graph.
• Sessions allocate resources, such as CPU or GPU, and manage the execution of operations in the graph.
• Sessions are responsible for initializing variables, running operations, and releasing resources after computation.

4. Variables and Operations:

• Variables are mutable tensors that hold values that can be updated during computation.
• Operations are functions that take tensors as input, perform computations, and produce output tensors.
• TensorFlow provides a wide range of built-in operations for mathematical operations, neural network layers, optimization algorithms, etc.

5. Neural Networks:

• Neural networks are computational models inspired by the structure and function of the human brain.
• TensorFlow enables the construction and training of neural networks for tasks such as classification, regression, and clustering.
• Common neural network architectures include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.

6. Training and Optimization:

• Training involves iteratively adjusting the parameters of a model to minimize a loss function, using techniques such as gradient descent and backpropagation.
• TensorFlow provides optimizers, loss functions, and metrics for training and evaluating machine learning models.
• Hyperparameter tuning, regularization, and early stopping are common techniques used to improve model performance and prevent overfitting.

7. Deployment and Serving:

• Once trained, TensorFlow models can be deployed for inference on various platforms, including servers, mobile devices, and the cloud.
• TensorFlow Serving is a system for serving machine learning models in production environments, providing scalable and efficient inference capabilities.
• TensorFlow Lite enables the deployment of models on mobile and embedded devices with limited computational resources.

8. TensorFlow Extended (TFX):

• TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines, from data ingestion to model deployment and monitoring.
• TFX components include data validation, preprocessing, model training, evaluation, and serving, facilitating the development and deployment of production-grade ML systems.

9. TensorFlow Quantum (TFQ):

• TensorFlow Quantum (TFQ) is an open-source library for quantum machine learning, allowing researchers and developers to build and train quantum machine learning models using TensorFlow.
• TFQ integrates quantum computing concepts and tools with TensorFlow's computational graph and optimization capabilities, enabling the development of quantum-enhanced machine learning algorithms.

Prerequisites

What are the prerequisites for this course?

None