Deep Learning with TensorFlow 2 Training Course

Introduction

• Foundations of TensorFlow and deep learning principles
• Real-world use cases and practical applications
• Exploring the TensorFlow ecosystem and associated tooling
• Workflows in machine learning and deep learning
• Course objectives and an overview of practical exercises

TensorFlow 2.x vs Previous Versions — What's New

• Distinct differences between TensorFlow 1.x and 2.x
• The mechanics of eager execution
• Enhanced usability through simplified APIs
• Evolution in model construction and training processes
• Keras introduced as the primary high-level API
• Strategies for migrating existing TensorFlow applications
• Best practices for TensorFlow 2.x development

Setting up TensorFlow 2.x

• Installing the TensorFlow framework
• Configuring an optimal Python environment
• Verifying the integrity of the TensorFlow installation
• Managing required dependencies
• Setting up CPU and GPU environments
• Utilising TensorFlow within Jupyter notebooks
• Core TensorFlow commands and basic operations
• Resolving common installation and configuration issues

Overview of TensorFlow 2.x Features and Architecture

• Architectural components and core functionality
• Working with tensors and tensor operations
• Managing variables and constants
• Computational graphs vs. eager execution
• Automatic differentiation
• Key TensorFlow APIs and modules
• Deep dive into Keras integration
• Constructing efficient data pipelines using tf.data
• Model serialisation and the TensorFlow SavedModel format
• Development workflow within the TensorFlow ecosystem

How Neural Networks Work

• Core concepts of artificial neural networks
• Structure of neurons, layers, and network architectures
• Selection of activation functions
• The process of forward propagation
• Understanding loss functions
• Mechanisms of backpropagation
• Gradient descent and optimisation methods
• Strategies for learning rates and optimisation
• Diagnosing overfitting and underfitting
• Applying regularisation techniques
• Utilising training, validation, and test datasets

Using TensorFlow 2.x to Create Deep Learning Models

• Generating tensors and variables
• Constructing neural networks using Keras
• Comparing Sequential and Functional model APIs
• Designing custom models and layers
• Configuring optimisers effectively
• Selecting suitable loss functions
• Training models via the fit() method
• Creating custom training loops
• Implementing callbacks and monitoring training
• Managing model checkpoints

Analyzing Data

• Curating datasets for machine learning
• Exploring structured and unstructured data types
• Techniques for data visualisation
• Identifying meaningful patterns and anomalies
• Addressing missing or inconsistent data points
• Partitioning data into training, validation, and test sets
• Feature selection strategies
• Preparing datasets specifically for TensorFlow models

Preprocessing Data

• Data normalisation and standardisation
• Encoding categorical variables
• Strategies for handling missing values
• Feature scaling methods
• Image preprocessing techniques
• Text data preprocessing
• Implementing data augmentation
• Building high-performance input pipelines
• Leveraging tf.data
• Techniques for batching, shuffling, caching, and prefetching
• Final preparation of data for model training

Building a Model

• Selecting the appropriate neural network architecture
• Defining model inputs and outputs
• Constructing dense neural networks
• Choosing the right activation functions
• Configuring the model for optimal training
• Selecting optimisers and loss functions
• Training and validating the model
• Monitoring key training metrics
• Enhancing model performance
• Mitigating overfitting
• Implementing regularisation and dropout

Implementing a State-of-the-Art Image Classifier

• Essentials of image classification
• Preparing image datasets
• Image normalisation and augmentation
• Convolutional Neural Networks (CNNs)
• Understanding convolution and pooling layers
• Designing an effective image classification architecture
• The concept of transfer learning
• Utilising pretrained models
• Fine-tuning pretrained networks
• Constructing an advanced image classifier
• Evaluating classification accuracy and performance

Training the Model

• Configuring training parameters
• Optimising batch size and epoch counts
• Selecting the appropriate optimiser
• Implementing learning-rate scheduling
• Utilising training callbacks
• Applying early stopping techniques
• Checkpointing models during training
• Monitoring training progress
• Detecting signs of overfitting
• Enhancing training efficiency
• Considerations for distributed training

Training on a GPU vs a TPU

• Architectural differences between CPU, GPU, and TPU
• Benefits of hardware acceleration
• Configuring TensorFlow for GPU utilisation
• Understanding TPU-based training
• Matching hardware to specific workloads
• Offloading computations to appropriate devices
• Managing memory and computational resources
• Comparing performance across different hardware
• Strategies for distributed and accelerated training

Evaluating the Model

• Selecting relevant evaluation metrics
• Assessing accuracy, precision, recall, and F1 score
• Metrics for regression tasks
• Interpreting confusion matrices
• Validating model robustness
• Evaluating classification performance
• Assessing generalisation capabilities
• Identifying potential model weaknesses
• Comparing various model configurations

Making Predictions

• Utilising trained models for inference
• Preparing new input data
• Executing batch and individual predictions
• Interpreting model outputs
• Understanding classification probabilities
• Generating regression predictions
• Constructing a reliable inference workflow
• Handling unseen data scenarios
• Managing prediction pipelines

Evaluating the Predictions

• Analyzing the quality of predictions
• Comparing predictions against expected outcomes
• Identifying false positives and false negatives
• Conducting detailed error analysis
• Assessing model confidence
• Visualising prediction results
• Detecting bias in data and predictions
• Refining model performance based on prediction insights

Debugging the Model

• Identifying common training bottlenecks
• Diagnosing the root causes of incorrect predictions
• Debugging data pipeline issues
• Analyzing loss and metric trends
• Detecting exploding and vanishing gradients
• Diagnosing overfitting and underfitting
• Inspecting internal model layers and outputs
• Leveraging TensorFlow debugging and profiling tools
• Improving model stability and overall performance

Saving a Model

• Preserving trained models
• Using the TensorFlow SavedModel format
• Saving and restoring model weights
• Storing model architecture and configuration
• Loading models for subsequent inference
• Implementing model versioning
• Exporting models for deployment
• Managing model artefacts
• Preparing models for production environments

Deploying a Model to the Cloud

• Overview of cloud-based model deployment
• Preparing TensorFlow models for production readiness
• Serving models via APIs
• Core concepts of model serving
• Containerising TensorFlow applications
• Implementing cloud-based inference
• Scaling model-serving workloads
• Monitoring deployed models in real-time
• Managing model versions in the cloud
• Key considerations for production deployment

Deploying a Model to a Mobile Device

• Challenges specific to mobile machine learning
• Introduction to TensorFlow Lite
• Converting TensorFlow models for mobile use
• Optimising model size and efficiency
• Applying quantisation techniques
• Running inference on mobile hardware
• Managing mobile device resource constraints
• Integrating models into mobile applications
• Testing mobile inference performance

Deploying a Model to an Embedded System (IoT)

• Machine learning applications on embedded devices
• Using TensorFlow Lite for embedded scenarios
• Navigating resource constraints and optimisation
• Reducing model footprint and computational load
• Implementing edge inference
• Processing sensor and real-time data
• Executing local predictions
• Considering power and memory limitations
• Integrating TensorFlow models into IoT workflows
• Testing and monitoring edge deployments

Integrating a Model with Different Languages

• Interoperability of TensorFlow models
• Serving models through various APIs
• Accessing TensorFlow models from different programming environments
• Python-based model integration
• Integrating models into web applications
• Performing model inference via REST services
• Incorporating TensorFlow into existing application stacks
• Data exchange and serialisation protocols
• Considerations for production-level integration

Troubleshooting

• Diagnosing TensorFlow installation issues
• Resolving model-building errors
• Debugging data preprocessing complications
• Addressing training failures
• Investigating GPU and TPU configuration conflicts
• Diagnosing memory and performance bottlenecks
• Troubleshooting model loading and saving
• Resolving deployment-related issues
• Practical troubleshooting exercises

Summary and Conclusion

• Recap of key TensorFlow 2.x concepts
• Review of neural network and deep learning workflows
• Summary of data preparation and model development
• Reflection on image classification techniques
• Review of training and evaluation strategies
• Summary of model debugging and optimisation
• Overview of cloud, mobile, and IoT deployment
• Best practices for TensorFlow development
• Final practical exercise
• Open floor for questions and discussion