A Complete Guide on TensorFlow 2.0 using Keras API

Module 1: Introduction to TensorFlow 2

⏳ 30 minutes

• Overview of TensorFlow and its role in deep learning.

• Setting up the development environment and installing TensorFlow.

Module 2: TensorFlow Basics and Keras API

⏳ 45 minutes

• Understanding tensors, operations, and automatic differentiation.

• Building models using the Sequential and Functional APIs.

Module 3: Training Neural Networks

⏳ 60 minutes

• Implementing loss functions, optimizers, and evaluation metrics.

• Training, validation, and testing workflows.

Module 4: Convolutional Neural Networks (CNNs)

⏳ 60 minutes

• Designing and training CNNs for image classification tasks.

• Data augmentation, dropout, and batch normalization techniques.

Module 5: Recurrent Neural Networks (RNNs) and LSTMs

⏳ 60 minutes

• Building RNNs for sequential and time series data.

• Using LSTMs and GRUs for more complex patterns.

Module 6: Natural Language Processing Projects

⏳ 60 minutes

• Text preprocessing, tokenization, and word embeddings.

• Implementing models for text classification and generation.

Module 7: Transfer Learning and Pretrained Models

⏳ 45 minutes

• Applying pretrained models like MobileNet and Inception.

• Fine-tuning vs. feature extraction.

Module 8: TensorFlow Tools and Deployment

⏳ 45 minutes

• Using TensorBoard for tracking training progress.

• Saving models and deploying with TensorFlow Lite and TF Serving.

Module 9: Real-World Projects and Best Practices

⏳ 75 minutes

• Complete ML/DL project implementation from data to deployment.

• Debugging, performance tuning, and production insights.

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