TensorFlow Mastery

From Tensors to Production — The Complete TensorFlow 2 & Keras Guide

Master Google’s flagship deep learning framework from the ground up. Build, train, and deploy neural networks with TensorFlow 2 and Keras — covering everything from tensor operations and automatic differentiation to CNNs, RNNs, Transformers, and production serving.

9Parts
5Deep Dives
9-Part Main Series

All Articles in This Series

A progressive learning path from TensorFlow fundamentals to production deployment. Each part builds on the previous, with every code example independently executable.

Part 1
Tensors, Eager Execution & Autodiff
35 min read
TensorsGradientTapeEager
Master TensorFlow tensors, eager execution, tf.Variable, and automatic differentiation with GradientTape. Build your computational foundation.
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Part 2
Building Models with Keras
30 min read
SequentialFunctionalSubclassing
Master all three Keras model-building APIs — Sequential, Functional, and Subclassing. Build custom layers, multi-input/output models, and shared-weight architectures.
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Part 3
Training & Optimization
30 min read
Loss FunctionsOptimizersLR Schedules
Deep dive into loss functions, optimizers (SGD, Adam, AdamW), learning rate schedules, regularization, and custom training components.
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Part 4
Data Pipelines with tf.data
25 min read
tf.dataAugmentationPrefetch
Build high-performance data pipelines with tf.data. Master map, batch, shuffle, prefetch, caching, and data augmentation for efficient GPU training.
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Part 5
Training Workflows & Callbacks
25 min read
model.fitCustom LoopsCallbacks
Master model.fit vs custom training loops, callbacks (EarlyStopping, ModelCheckpoint, TensorBoard), model saving/loading, and custom metrics.
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Part 6
CNNs & Computer Vision
30 min read
Conv2DClassificationAugmentation
Build convolutional neural networks for image classification, feature extraction, and transfer learning with pretrained Keras models.
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Part 7
RNNs, NLP & Time Series
35 min read
LSTMEmbeddingsForecasting
Master recurrent networks, LSTM/GRU cells, word embeddings, text classification, sentiment analysis, and time series forecasting.
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Part 8
Transformers & Attention
30 min read
AttentionMultiHeadTransformer
Implement scaled dot-product attention, MultiHeadAttention layers, positional encoding, and full Transformer encoder/decoder blocks in Keras.
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Part 9
Deployment, Performance & Best Practices
30 min read
TF ServingTFLiteMulti-GPU
Deploy models with TF Serving, TFLite, and TF.js. Master multi-GPU training, XLA compilation, mixed precision, and production best practices.
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5 Architecture Deep Dives

Architecture Deep Dives

Focused implementation guides for landmark architectures built with TensorFlow and Keras. Each deep dive walks through the paper, builds the model from scratch, trains it on real data, and explains every design decision.

Deep Dive
EfficientNet: Compound Scaling
35 min read
MBConvScalingSE
Implement EfficientNet-B0 through B7 from scratch. Master compound scaling, MBConv blocks, and squeeze-and-excitation.
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Deep Dive
BERT: Bidirectional Transformers
40 min read
Masked LMFine-tuningNER
Build BERT from scratch. Implement masked language modeling, next sentence prediction, and fine-tune for classification and NER.
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Deep Dive
Stable Diffusion: Image Generation
40 min read
DiffusionU-NetCLIP
Implement the complete Stable Diffusion pipeline: U-Net denoiser, VAE, CLIP text encoder, and classifier-free guidance.
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Deep Dive
YOLOv8: Object Detection in TF
35 min read
DetectionTFLiteCSPDarknet
Build YOLO-style detection from anchor-free heads to TFLite deployment. CSPDarknet backbone, C2f modules, and edge inference.
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Deep Dive
Vision Transformer (ViT)
35 min read
PatchesCLS TokenAttention
Build the Vision Transformer from scratch. Patch embeddings, positional encoding, multi-head self-attention, and attention visualization.
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