TensorFlow Learning Roadmap

Why TensorFlow

TensorFlow is Google’s production ML framework with best-in-class deployment tooling: TF Serving for high-throughput APIs, TFLite for mobile/edge, TensorFlow.js for browser inference, and TFX for end-to-end ML pipelines. If you’re deploying models to production at scale, TensorFlow’s ecosystem is unmatched.

Stage 1: Keras Fundamentals (Weeks 1-2)

Goal: Build and train models using the Keras API.

Topics:

• Introduction to TensorFlow — tensors, Keras APIs (Sequential, Functional, subclassing), custom layers

Milestone: Build a Functional API model for a multi-input task (numeric + categorical features). Compile with a custom learning rate schedule, train with callbacks (EarlyStopping, ModelCheckpoint), and load the best checkpoint.

Stage 2: Computer Vision (Weeks 2-3)

Goal: Train CNNs from scratch and use transfer learning.

Topics:

• CNNs with Transfer Learning — CNN from scratch, EfficientNetB0 fine-tuning, tf.data pipeline

Milestone: Fine-tune EfficientNetB0 on a 10-class image dataset using tf.data with augmentation, cache, and prefetch. Achieve a better result than training from scratch in 1/3 the epochs.

Stage 3: Sequence Models (Week 3)

Goal: Handle text and time series with LSTMs.

Topics:

• RNNs and LSTMs — BiLSTM text classifier, time-series LSTM, GRU text generation

Milestone: Build a BiLSTM sentiment classifier on IMDB. Compare token-level vs. sentence-level aggregation. Implement a simple text generation system with temperature sampling.

Stage 4: Transformers and NLP (Week 4)

Goal: Fine-tune transformer models using HuggingFace + TF backend.

Topics:

• Transformers and BERT — BERT fine-tuning, zero-shot classification, sentence embeddings, NER

Milestone: Fine-tune BERT for a multi-class text classification task. Implement a warmup LR schedule. Compare fine-tuned BERT vs. BiLSTM vs. zero-shot classification.

Stage 5: Deployment (Week 5)

Goal: Deploy TensorFlow models to production.

Topics:

• TF Deployment — SavedModel, TFLite quantization, FastAPI serving, TF Serving

Milestone: Take a trained model and produce: a SavedModel, a quantized TFLite model (int8), and a FastAPI endpoint. Benchmark latency and size for each format.

Stage 6: Projects

Beginner: Fashion MNIST classifier, image autoencoder, text generation with LSTM
Intermediate: Object detection with TF Object Detection API, U-Net segmentation, DCGAN
Advanced: Full TFX pipeline, quantization-aware training, distributed training with MirroredStrategy

See TensorFlow Projects for full descriptions.

What Comes After TensorFlow

• TFX — production ML pipelines at scale
• TF Serving + Kubernetes — scalable model serving
• TFLite / Edge TPU — mobile and embedded deployment
• MLOps — experiment tracking, monitoring, CI/CD for ML

Resources

• TensorFlow official tutorials: tensorflow.org/tutorials
• Keras documentation: keras.io
• “Hands-On Machine Learning” by Aurélien Géron (chapters 10-19)
• TensorFlow Developer Certificate curriculum (structured learning path)