Complete Guide to TensorFlow for Deep Learning with Python Course Course

Module 1: Introduction to Deep Learning & TensorFlow

⏳ 30 minutes

• Overview of deep learning, AI history, and TensorFlow’s role.

• Installing Python, TensorFlow, and setting up your environment.

Module 2: TensorFlow Basics & Tensors

⏳ 45 minutes

• Working with tensors, operations, and broadcasting.

• Introduction to auto-differentiation and computational graphs.

Module 3: Neural Networks & Keras API

⏳ 60 minutes

• Building neural networks with Sequential and Functional APIs.

• Understanding loss functions, optimizers, and evaluation metrics.

Module 4: Image Classification with CNNs

⏳ 60 minutes

• Implementing convolutional layers and pooling operations.

• Building models for CIFAR-10 and MNIST datasets.

Module 5: Recurrent Neural Networks (RNNs)

⏳ 60 minutes

• Sequence modeling with SimpleRNN, LSTM, and GRU layers.

• Applications in time series forecasting and text analysis.

Module 6: Advanced Topics & Custom Training

⏳ 60 minutes

• Writing custom training loops with GradientTape.

• Learning rate scheduling, callbacks, and model checkpoints.

Module 7: TensorBoard & Model Deployment

⏳ 45 minutes

• Logging training progress and metrics with TensorBoard.

• Saving models and deployment best practices.

Module 8: Final Projects and Capstone Work

⏳ 75 minutes

• Real-world image and sequence modeling projects.

• Best practices for scaling and refining deep learning workflows.

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