TensorFlow – Deep Learning & Neural Networks

Module 1: Introduction to TensorFlow

• TensorFlow overview: open-source deep learning framework by Google
• TensorFlow 2.x vs 1.x: eager execution and Keras integration
• TensorFlow ecosystem: TF Hub, TFX, TensorFlow Lite, and TensorFlow.js
• Setting up TensorFlow: CPU and GPU installation with CUDA

Module 2: TensorFlow and Keras Fundamentals

• Tensors: creating, reshaping, and operating on TensorFlow tensors
• Keras Sequential API: building simple neural networks layer by layer
• Keras Functional API: multi-input, multi-output, and shared layer models
• Model compilation: optimisers, loss functions, and metrics

Module 3: Training Neural Networks

• Training loop: fit(), batch size, epochs, and validation split
• Callbacks: ModelCheckpoint, EarlyStopping, and ReduceLROnPlateau
• Overfitting prevention: dropout, L1/L2 regularisation, and batch normalisation
• Learning rate schedules: step decay, cosine annealing, and warm-up

Module 4: Convolutional Neural Networks (CNNs)

• CNN architecture: convolutional layers, pooling, and fully connected layers
• Building image classifiers with Keras Conv2D and MaxPooling2D
• Data augmentation: ImageDataGenerator and tf.data pipelines
• Transfer learning: fine-tuning VGG16, ResNet, and EfficientNet

Module 5: Recurrent Neural Networks (RNNs)

• RNN fundamentals: sequence modelling and the vanishing gradient problem
• LSTM and GRU: gated recurrent units for long-sequence learning
• Text classification with LSTM: embedding, LSTM, and Dense layers
• Time series forecasting with RNNs and LSTM networks

Module 6: Custom Training and Advanced TensorFlow

• Custom training loops: GradientTape and manual gradient computation
• Custom layers and models: subclassing tf.keras.Layer and tf.keras.Model
• TensorFlow datasets (TFDS): loading and preprocessing standard datasets
• TensorFlow Serving: deploying TensorFlow models as REST APIs

Module 7: TensorFlow Lite and Edge Deployment

• TensorFlow Lite overview: on-device ML for mobile and IoT
• Model conversion: SavedModel to TFLite with quantisation
• TFLite interpreter: running inference on Android and Raspberry Pi
• TensorFlow.js: running TensorFlow models in the browser

Module 8: Capstone Project and Assessment

• CNN image classifier: training, evaluation, and transfer learning project
• LSTM sequence model: text classification or time series forecasting
• TensorFlow Serving deployment: REST API inference endpoint
• Final assessment and course certification

Experience in the Industry Gain expertise from deep learning engineers who have built and deployed TensorFlow neural networks for computer vision, NLP, and time series applications in technology, healthcare, and autonomous systems.

Backgrounds at the Top Our TensorFlow trainers have worked at AI research labs, technology companies, and deep learning consultancies building production-grade neural network models with TensorFlow and Keras.

Clear & Effective Teaching TensorFlow 2.x, Keras APIs, CNN and RNN architectures, transfer learning, custom training loops, TensorFlow Serving, TFLite, and TensorFlow.js are explained with real deep learning project examples.

Hands-On Learning Focus Students build CNNs, train LSTM models, apply transfer learning, write custom training loops, deploy with TensorFlow Serving, and convert models for edge devices through structured hands-on deep learning labs.

Up-to-Date Knowledge Trainers keep content current with the latest TensorFlow releases, Keras 3 multi-backend support, JAX integration, and evolving deep learning framework and deployment best practices.