TensorFlow — Course #409 · Powered by Google

Build Deep Learning

Models with TensorFlow 🔥

TensorFlow is Google's open-source deep learning framework used in production by Google Search, YouTube, Google Photos, and thousands of AI startups. Master the Keras API, CNNs, RNNs, Transformers, computer vision, NLP, and TF deployment to build and ship real AI systems.

Keras API CNNs RNNs & LSTMs Transformers Computer Vision NLP Transfer Learning TFX TF Lite TF Serving

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TensorFlow — CNN Image Classifier

import tensorflow as tf
# Build CNN for image classification
model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2,2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
            loss='categorical_crossentropy',
            metrics=['accuracy'])

35K+

Jobs

DL roles in India

47 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

What You Master

The Complete TensorFlow Skill Set

From Keras basics and CNNs to Transformers, production deployment, and TFX pipelines.

🔥

Keras API & Neural Network Architecture

Build deep learning models with the Keras Sequential and Functional API. Design dense networks, CNNs, RNNs, and custom layers. Understand activation functions, loss functions, optimizers, callbacks, and model checkpointing. Train, evaluate, and tune models using Keras Tuner and tf.GradientTape for full control.

Sequential API Functional API Dense Layers BatchNorm Dropout Custom Loops Callbacks Keras Tuner

👁️

Computer Vision & CNNs

Object detection, image segmentation, face recognition, transfer learning with VGG16, ResNet, and MobileNet.

💬

RNNs, LSTMs & NLP

Sequence modeling, sentiment analysis, machine translation, BERT with TensorFlow, and text generation.

🚀

TF Deployment — Serving, Lite & TFX

Deploy models with TF Serving REST API, TF Lite for mobile & edge, TF.js for browsers, and TFX pipelines for production ML.

TF Serving TF Lite TFX

Syllabus

Course Curriculum

12 comprehensive modules — from TensorFlow basics to CNNs, RNNs, GANs, and production deployment.

Overview of TensorFlow & Deep Learning ecosystem
TensorFlow 2.x vs 1.x — Key differences
TensorFlow vs PyTorch vs Scikit-learn
Installation, Setup & Jupyter Notebooks
Understanding Tensors and Computational Graphs

Tensors — Rank, Shape, and Data Types
Variables, Constants, and Operations
Tensor Operations and Manipulations
Eager Execution vs. Graph Execution
Automatic Differentiation with tf.GradientTape

Keras Sequential vs. Functional API
Building a Simple Feedforward Neural Network
Activation Functions — ReLU, Sigmoid, Softmax, Tanh
Loss Functions and Optimizers — Adam, SGD, RMSprop
Model Compilation, Training, Evaluation, and Callbacks

Loading CSV, Images, and TFRecord datasets
tf.data API — Map, Batch, Shuffle, Prefetch
Data Augmentation Techniques for Images
Normalization and Feature Scaling
Handling Imbalanced Datasets

Understanding Convolutions and Pooling Layers
Building CNNs for Image Classification
Transfer Learning — VGG16, ResNet, MobileNet, InceptionV3
Fine-tuning Pretrained Models
Data Augmentation for Image Recognition

Introduction to Sequence Modeling
Understanding LSTMs and GRUs
Text Preprocessing — Tokenization and Word Embeddings
Sentiment Analysis with RNNs
Transformer Architecture and Attention Mechanism

Object Detection with TensorFlow Object Detection API
Image Segmentation with U-Net
Face Recognition with TensorFlow
Generative Adversarial Networks (GANs) for Image Synthesis
Style Transfer and Image-to-Image Translation

Word Embeddings — Word2Vec, GloVe, FastText
Text Classification with CNNs and LSTMs
Machine Translation with Seq2Seq models
Transformers & BERT with TensorFlow/Hugging Face
Text Summarization and Generation

Custom Training Loops with tf.GradientTape
Advanced Callbacks — EarlyStopping, LR Scheduler
Hyperparameter Tuning with Keras Tuner
Distributed Training with tf.distribute.Strategy
Model Pruning and Quantization for Efficiency

Introduction to Time Series Data
Forecasting with LSTMs and GRUs
WaveNet and Temporal Convolutional Networks
Autoencoders for Anomaly Detection
Attention Mechanism for Time Series Predictions

Exporting and Saving TensorFlow Models
TF Serving for REST API Inference
TensorFlow Lite for Mobile & Edge Devices
TensorFlow.js for Web-Based ML
Cloud Deployment on GCP, AWS, and Azure

TensorFlow Extended (TFX) Overview
Data Validation with TensorFlow Data Validation
Feature Engineering with TF Transform
ML Pipelines — TFX, Kubeflow, Vertex AI
Model Monitoring, Versioning & CI/CD for ML

Career Outcomes

TensorFlow Careers in 2026

TensorFlow powers Google, Airbnb, Twitter, Intel, and thousands of AI companies globally. Deep learning engineers are among the highest-paid tech professionals, with demand growing 40%+ year-on-year across every sector.

₹14–45L

India Avg LPA

$120K–$210K

USA Avg

35K+

Active DL Jobs

42% Growth

Year-on-Year

Deep Learning Engineer

Design, train, and optimize neural networks for image recognition, NLP, and predictive AI products

₹14–40 LPA · $120K–$195K USA

Computer Vision Engineer

Build CNN-based image classification, object detection, and video analysis systems for real-world applications

₹15–42 LPA · $125K–$200K USA

ML Engineer (TensorFlow)

Build production ML pipelines with TFX, deploy models via TF Serving and TF Lite for mobile and cloud

₹16–45 LPA · $130K–$205K USA

AI Research Engineer

Research novel deep learning architectures, optimize model performance, and publish cutting-edge findings

₹18–55 LPA · $140K–$230K USA

Build Deep Learning

Models with TensorFlow 🔥

The Complete TensorFlow Skill Set

Course Curriculum

TensorFlow Careers in 2026

Ready to Master TensorFlow?