šŸ”„ Deep Learning with TensorFlow

Master TensorFlow framework, build deep neural networks, and deploy scalable machine learning solutions

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Deep Learning with TensorFlow Curriculum

12
Core Units
~90
Key Concepts
20+
TF Components
40+
Practical Examples
1

TensorFlow Fundamentals

Learn the core concepts of TensorFlow and its ecosystem for deep learning.

  • What is TensorFlow
  • TensorFlow ecosystem
  • Installation and setup
  • TensorFlow vs other frameworks
  • Computational graphs
  • Eager execution
  • TensorFlow 2.x features
  • Development workflow
2

Tensors and Operations

Master tensors, the fundamental data structure in TensorFlow.

  • Tensor basics
  • Tensor shapes and ranks
  • Creating tensors
  • Tensor operations
  • Broadcasting
  • Indexing and slicing
  • Data types
  • Memory management
3

Keras High-Level API

Build neural networks quickly with Keras, TensorFlow's high-level API.

  • Keras overview
  • Sequential API
  • Functional API
  • Model subclassing
  • Layers and activations
  • Model compilation
  • Training and evaluation
  • Model saving and loading
4

Building Neural Networks

Construct various neural network architectures using TensorFlow and Keras.

  • Dense (fully connected) layers
  • Multi-layer perceptrons
  • Activation functions
  • Loss functions
  • Optimizers
  • Metrics
  • Custom layers
  • Model architecture design
5

Convolutional Neural Networks

Master CNNs for image processing and computer vision tasks in TensorFlow.

  • Convolution layers
  • Pooling layers
  • CNN architectures
  • Image preprocessing
  • Data augmentation
  • Transfer learning
  • Pre-trained models
  • Image classification
6

Recurrent Neural Networks

Build RNNs for sequential data processing and time series analysis.

  • RNN fundamentals
  • LSTM layers
  • GRU layers
  • Bidirectional RNNs
  • Sequence modeling
  • Text processing
  • Time series forecasting
  • Sequence-to-sequence models
7

Advanced Training Techniques

Optimize training with advanced techniques and regularization methods.

  • Custom training loops
  • GradientTape
  • Callbacks
  • Learning rate scheduling
  • Regularization techniques
  • Batch normalization
  • Dropout layers
  • Early stopping
8

Data Pipelines

Create efficient data pipelines for large-scale machine learning with tf.data.

  • tf.data API
  • Dataset creation
  • Data transformations
  • Batching and shuffling
  • Performance optimization
  • Prefetching
  • Parallel processing
  • Large dataset handling
9

Model Deployment

Deploy TensorFlow models to production environments and edge devices.

  • TensorFlow Serving
  • Model serialization
  • TensorFlow Lite
  • TensorFlow.js
  • Cloud deployment
  • Model optimization
  • Quantization
  • Edge deployment
10

TensorBoard and Monitoring

Monitor training progress and visualize models using TensorBoard.

  • TensorBoard setup
  • Logging metrics
  • Visualization dashboards
  • Model graph visualization
  • Hyperparameter tuning
  • Profiling
  • Debugging tools
  • Experiment tracking
11

Advanced Architectures

Implement cutting-edge deep learning architectures and techniques.

  • Autoencoder networks
  • Generative Adversarial Networks
  • Transformer models
  • Attention mechanisms
  • Residual networks
  • DenseNet architecture
  • Neural style transfer
  • Custom architectures
12

Real-World Projects

Apply TensorFlow to solve real-world problems with end-to-end projects.

  • Image classification project
  • Natural language processing
  • Time series forecasting
  • Recommendation systems
  • Computer vision applications
  • MLOps with TensorFlow
  • Production best practices
  • Portfolio projects

Unit 1: TensorFlow Fundamentals

Learn the core concepts of TensorFlow and its ecosystem for deep learning.

What is TensorFlow

Understand TensorFlow as Google's open-source machine learning framework.

Framework Open Source Google
TensorFlow is an end-to-end open source platform for machine learning that provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying ML-powered applications.

TensorFlow Ecosystem

Explore the complete TensorFlow ecosystem and its various components.

Core TensorFlow: Main library for building and training models
Keras: High-level API for rapid prototyping
TensorFlow Lite: Mobile and embedded deployment
TensorFlow.js: JavaScript implementation for web/Node.js
TensorFlow Serving: Production model serving
TensorBoard: Visualization and monitoring tools
# TensorFlow ecosystem overview
import tensorflow as tf
print(f"TensorFlow version: {tf.__version__}")

# Core components demonstration
def explore_tensorflow_ecosystem():
  """Explore different TensorFlow components"""
  
  print("=== TENSORFLOW ECOSYSTEM ===")
  
  # 1. Core TensorFlow
  print("\\nšŸ”„ Core TensorFlow:")
  print(f" Version: {tf.__version__}")
  print(f" GPU Available: {tf.config.list_physical_devices('GPU')}")
  print(f" Built with CUDA: {tf.test.is_built_with_cuda()}")
  
  # 2. Keras Integration
  print("\\nšŸŽÆ Keras (High-level API):")
  print(f" Keras version: {tf.keras.__version__}")
  print(f" Available optimizers: {len(tf.keras.optimizers.__dict__)} types")
  
  # 3. Data handling
  print("\\nšŸ“Š Data Pipeline (tf.data):")
  sample_dataset = tf.data.Dataset.range(10)
  print(f" Sample dataset created: {sample_dataset}")
  
  # 4. Available modules
  print("\\nšŸ§° Available Modules:")
  key_modules = {
    "tf.keras": "High-level neural network API",
    "tf.data": "Input pipeline API",
    "tf.image": "Image processing operations",
    "tf.math": "Mathematical operations",
    "tf.nn": "Neural network operations",
    "tf.train": "Training utilities",
    "tf.saved_model": "Model serialization",
    "tf.summary": "TensorBoard logging"
  }
  
  for module, description in key_modules.items():
    print(f" {module}: {description}")
  
  return key_modules

# Run the exploration
ecosystem_info = explore_tensorflow_ecosystem()

print("\\n=== TENSORFLOW ADVANTAGES ===")
advantages = [
  "šŸ”„ End-to-end ML platform",
  "āš” Scalable from mobile to clusters",
  "šŸŽÆ Production-ready deployment",
  "šŸ§‘ā€šŸ’» Large community and ecosystem",
  "šŸ“š Extensive documentation and tutorials",
  "šŸ”§ Flexible for research and production"
]

for advantage in advantages:
  print(f" {advantage}")

Installation and Setup

Learn how to install and configure TensorFlow for your development environment.

TensorFlow can be installed via pip, conda, or Docker. GPU support requires additional CUDA and cuDNN installations for optimal performance.
# TensorFlow installation guide

"""
=== INSTALLATION METHODS ===

1. CPU-only installation:
pip install tensorflow

2. GPU installation (recommended):
pip install tensorflow[and-cuda]

3. Using conda:
conda install -c conda-forge tensorflow

4. Development version:
pip install tf-nightly
"""

# Installation verification
import tensorflow as tf
import sys

def verify_installation():
  """Verify TensorFlow installation"""
  
  print("=== TENSORFLOW INSTALLATION VERIFICATION ===")
  
  # Basic info
  print(f"Python version: {sys.version}")
  print(f"TensorFlow version: {tf.__version__}")
  
  # Hardware detection
  print("\\nšŸ–„ļø Hardware Detection:")
  print(f" CPU devices: {len(tf.config.list_physical_devices('CPU'))}")
  print(f" GPU devices: {len(tf.config.list_physical_devices('GPU'))}")
  
  # GPU details if available
  gpus = tf.config.list_physical_devices('GPU')
  if gpus:
    print("\\nšŸš€ GPU Information:")
    for i, gpu in enumerate(gpus):
      print(f"