Programming

TensorFlow: Deep Learning with TensorFlow

Master deep learning and neural network engineering with TensorFlow and Keras. From fundamental tensor operations, automatic differentiation (tf.GradientTape), and custom model architectures to Convolutional Neural Networks (CNN), Recurrent Neural Networks (LSTM/GRU), transfer learning, model evaluation, and production deployment with TensorFlow …

6 Months Duration
15 Modules
Paid Access

Course fee ~~₹20000.00~~ ₹10000.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

Introduction to TensorFlow

Get started with TensorFlow, Google's premier open-source machine learning and deep learning framework. Understand what TensorFlow is, explore its industry applications, architecture, and rich ecosystem (Keras, TFLite, TF Serving). Configure your Python environment, verify GPU …

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Learning objectives

• Understand what TensorFlow is and why it is an industry-standard deep learning framework
• Explore key features of TensorFlow for research and production scale
• Navigate the TensorFlow ecosystem (Keras, TF.js, TFLite, TF Serving)
• Understand the internal architecture and hardware execution engine
• Install TensorFlow and set up your Python/CUDA development environment
• Verify GPU hardware acceleration with device listing utilities
• Build and execute your first functional TensorFlow application

Module 2 Locked

Tensors

Tensors are the fundamental data structures of TensorFlow. Learn tensor ranks, shapes, dimensions, and data types. Perform element-wise operations, master broadcasting semantics, reshape and index tensors, convert between NumPy arrays and tensors, and apply tensor …

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Learning objectives

• Understand the mathematical and computational definition of a Tensor
• Create tensors from Python primitives and NumPy arrays using tf.constant()
• Inspect tensor properties: rank, shape, dimension, and dtype
• Apply broadcasting rules when operating on tensors of unequal dimensions
• Perform tensor reshaping, transposition, squeezing, and expansion
• Slice and index multi-dimensional tensors precisely
• Seamlessly convert between TensorFlow tensors and NumPy ndarrays

Module 3 Locked

Variables and Constants

Manage mutable and immutable states in computational graphs. Differentiate tf.constant from tf.Variable, assign and update values in-place, manage variable scopes and initialization, configure trainable parameters for gradient optimization, and follow state management best practices.

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Learning objectives

• Understand the distinction between immutable tf.constant and mutable tf.Variable
• Instantiate tf.Variable with initial values and explicit dtypes
• Update variable values in-place using assign(), assign_add(), and assign_sub()
• Understand variable initialization and memory allocation
• Configure trainable=True/False flags for fine-tuning and parameter freezing
• Track model parameters and manage variable scopes cleanly

Module 4 Locked

TensorFlow Operations

Harness TensorFlow's powerful mathematical and linear algebra computational engine. Master element-wise arithmetic, matrix multiplication (tf.matmul), reduction operations (reduce_mean, reduce_sum), random sampling, logical and comparison operations, tensor manipulations, and performance optimization techniques.

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Learning objectives

• Perform element-wise mathematical operations (+, -, *, /, exp, log)
• Execute matrix multiplication and linear algebra routines with tf.matmul and tf.linalg
• Aggregate data across dimensions using reduction operations (reduce_sum, reduce_mean)
• Generate random tensors with uniform and normal distributions
• Apply logical and boolean comparison operations
• Concatenate, stack, split, and gather tensors efficiently
• Optimize computational throughput with vectorised hardware execution

Module 5 Locked

Automatic Differentiation

Understand the heart of deep learning optimization: automatic differentiation with tf.GradientTape. Compute gradients of functions and loss expressions, handle multiple variables, utilize persistent tapes, calculate higher-order derivatives, implement custom gradient rules, and apply gradient clipping.

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Learning objectives

• Understand the concept and mechanics of Automatic Differentiation (autodiff)
• Record forward computational graphs inside tf.GradientTape contexts
• Compute partial derivatives of loss with respect to trainable variables
• Manage multi-variable gradients and Jacobians
• Use persistent=True for multiple gradient calls on a single tape
• Explicitly watch non-variable tensors with tape.watch()
• Prevent exploding gradients using gradient clipping (clip_by_norm/value)

Module 6 Locked

Keras Fundamentals

Master Keras, the high-level neural modeling API built into TensorFlow. Build architectures using the Sequential and Functional APIs, explore core layers, activation functions (ReLU, Sigmoid, Softmax), loss functions (MSE, Cross-Entropy), optimizers (Adam, SGD), metrics, and …

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Learning objectives

• Understand the Keras philosophy of user-friendliness, modularity, and extensibility
• Construct feedforward architectures using the Sequential API
• Build complex multi-input, multi-output, and residual models with the Functional API
• Select appropriate activation functions for hidden and output layers
• Choose correct loss functions for regression, binary, and multi-class classification
• Configure modern adaptive optimizers (Adam, RMSprop, SGD with momentum)
• Compile models with model.compile() ready for training

Module 7 Locked

Building Neural Networks

Design, configure, and train Artificial Neural Networks (ANN / Multi-Layer Perceptrons). Explore Dense layers, hidden layer sizing, output layer activation selection, forward and backpropagation mechanics, weight initialization strategies (Glorot, He), and regularization techniques including Dropout …

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Learning objectives

• Understand the structural anatomy of an Artificial Neural Network
• Connect Dense (fully connected) layers to form hidden and output representations
• Trace data flow during forward propagation and error flow during backpropagation
• Select weight initialization strategies (He, Xavier/Glorot) to prevent saturation
• Mitigate overfitting using L1/L2 weight regularization
• Apply Dropout layers to deactivate random units during training
• Implement Batch Normalization to stabilize internal covariate shift

Module 8 Locked

Data Processing

Build high-performance, production-grade data pipelines using the tf.data API. Load datasets from files, memory, and TensorFlow Datasets (TFDS), perform preprocessing, apply on-the-fly data augmentation, batch, shuffle, and prefetch data, and optimize pipelines to eliminate GPU …

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Learning objectives

• Ingest data from NumPy, CSV, images, and TFRecord formats with tf.data
• Explore and load prebuilt benchmark datasets using TensorFlow Datasets (TFDS)
• Apply preprocessing transformations with dataset.map() and num_parallel_calls
• Implement real-time data augmentation for computer vision workflows
• Configure robust batching, shuffling, and caching strategies
• Eliminate GPU idle time using .prefetch(tf.data.AUTOTUNE)
• Stream massive out-of-core datasets that exceed system RAM

Module 9 Locked

Model Training

Execute and control model training effectively with Keras. Understand epochs, mini-batch sizing, learning rate schedules, and validation split dynamics. Implement essential callbacks including EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, and TensorBoard to monitor, log, and safeguard training.

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Learning objectives

• Train models using model.fit() with training and validation datasets
• Tune training hyperparameters: epochs, batch size, and learning rate
• Prevent overtraining and save compute using EarlyStopping callbacks
• Save optimal model checkpoints automatically with ModelCheckpoint
• Dynamically decay learning rates with ReduceLROnPlateau
• Monitor live training loss, accuracy, and graph activations with TensorBoard
• Diagnose and troubleshoot training stalls and loss divergence

Module 10 Locked

Model Evaluation

Rigourously evaluate model performance and identify failure modes. Explore quantitative metrics beyond accuracy: Precision, Recall, F1 Score, Confusion Matrix, and ROC-AUC curves. Diagnose overfitting and underfitting, understand generalization error, and implement strategies to boost performance.

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Learning objectives

• Evaluate trained models on unseen test data using model.evaluate()
• Analyze classification accuracy, precision, recall, and F1 score
• Generate and interpret Confusion Matrices using tf.math.confusion_matrix
• Plot and analyze Receiver Operating Characteristic (ROC) and AUC metrics
• Diagnose overfitting (high variance) vs underfitting (high bias)
• Apply systematic performance improvement techniques (capacity, data, regularization)

Module 11 Locked

Convolutional Neural Networks (CNN)

Build computer vision applications with Convolutional Neural Networks. Learn convolution mechanics, kernels, feature maps, pooling layers (MaxPooling2D), padding, and strides. Construct image classification networks, leverage Transfer Learning with pre-trained models (MobileNet, ResNet), and fine-tune CNNs.

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Learning objectives

• Understand how convolutional layers detect local spatial visual features (edges, textures, patterns)
• Configure Conv2D layers with kernel size, filters, padding ("valid" vs "same"), and stride
• Downsample representations and build spatial invariance with MaxPooling2D
• Transition from 2D feature maps to classification outputs using Flatten and GlobalAveragePooling2D
• Implement Transfer Learning using pre-trained backbones from tf.keras.applications
• Fine-tune top layers with low learning rates for high-accuracy domain adaptation
• Apply best practices for image sizing, normalization, and CNN design

Module 12 Locked

Recurrent Neural Networks (RNN)

Process temporal, time series, and sequential data with Recurrent Neural Networks. Understand internal memory states, master LSTM and GRU architectures to overcome vanishing gradients, build sequence-to-sequence and sequence-to-label models, implement Bidirectional RNNs, and explore Attention …

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Learning objectives

• Understand how recurrent architectures process sequential dependencies over time
• Identify the vanishing and exploding gradient problem in standard simple RNNs
• Implement Long Short-Term Memory (LSTM) networks with input, forget, and output gates
• Build Gated Recurrent Unit (GRU) networks for efficient sequence modeling
• Build time series forecasting models with sliding window sequences
• Process text data for sentiment analysis and sequence classification
• Implement Bidirectional layers and understand the role of Attention mechanisms

Module 13 Locked

Custom Models

Extend TensorFlow beyond off-the-shelf components. Build custom layers by subclassing tf.keras.layers.Layer, create complex architectures by subclassing tf.keras.Model, write custom loss functions and metrics, implement custom training loops using tf.GradientTape, build custom callbacks, and manage serialization.

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Learning objectives

• Subclass tf.keras.layers.Layer implementing __init__, build(), and call()
• Subclass tf.keras.Model to define custom forward passes and dynamic execution flows
• Write custom loss functions with specialized mathematical penalties
• Implement custom stateful evaluation metrics subclassing tf.keras.metrics.Metric
• Construct low-level custom training loops with tf.GradientTape and optimizers
• Build custom lifecycle callbacks subclassing tf.keras.callbacks.Callback
• Support model serialization and layer reconstruction with get_config()

Module 14 Locked

Model Deployment

Deploy trained deep learning models into production environments. Master the SavedModel and H5 formats, deploy scalable microservices with TensorFlow Serving (REST and gRPC), optimize and convert models for mobile and edge devices with TensorFlow Lite …

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Learning objectives

• Export trained models in the standard SavedModel format and legacy H5 format
• Inspect SavedModel signatures and tags using the saved_model_cli utility
• Deploy models as scalable microservices using TensorFlow Serving via Docker
• Query TensorFlow Serving endpoints with REST and high-speed gRPC protocols
• Convert and quantize models for mobile and IoT devices using TensorFlow Lite (TFLite)
• Export models to run client-side in browsers using TensorFlow.js (TF.js)
• Apply model versioning, latency optimization, and zero-downtime deployment practices

Module 15 Locked

TensorFlow Best Practices

Adopt professional software engineering and high-performance practices for enterprise TensorFlow projects. Structure maintainable machine learning repositories, accelerate graph execution with @tf.function, train at scale with Mixed Precision and Distributed Strategies (MirroredStrategy), diagnose numerical instability and …

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Learning objectives

• Structure production TensorFlow codebases with clean separation of data, model, and training
• Accelerate Python execution with @tf.function graph compilation and autograph tracing
• Double training throughput and cut memory usage with Mixed Precision training (float16/bfloat16)
• Scale training across multiple GPUs seamlessly with tf.distribute.MirroredStrategy
• Troubleshoot device placement, memory leaks, and numerical NaNs
• Implement structured logging, profiling, and monitoring
• Adhere to production engineering guidelines for model lifecycle management

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