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Deep Learning Framework

TensorFlow and Keras Course

Master deep learning with TensorFlow 2.x and Keras from computational graphs to production deployment. Build neural networks using Sequential and Functional APIs, implement CNNs and RNNs, explore transfer learning with pre-trained models, and deploy models using TensorFlow Serving for real-world applications.

Computational Graphs & Tensors
CNNs & RNNs Implementation
Transfer Learning Workflows
Model Deployment & Serving

This comprehensive course covers everything from TensorFlow fundamentals to advanced neural network architectures, including activation functions, optimizers, backpropagation, and practical deployment pipelines for image and text classification projects.

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TensorFlow and Keras Course

Course Overview

Key Features

  • Comprehensive 10-Lesson Deep Learning Framework covering TensorFlow 2.x and Keras
  • Hands-on Neural Network Development using both Sequential and Functional APIs
  • Advanced Architecture Training including CNNs for computer vision and RNNs for sequences
  • Production-Ready Deployment Skills with model checkpoints, SavedModel format, and TensorFlow Serving
  • Transfer Learning Mastery using pre-trained models and fine-tuning workflows
  • Complete Capstone Project developing image or text classification with full deployment pipeline

Skills You'll Master

  • TensorFlow Fundamentals: Computational graphs, tensor operations, and TensorFlow 2.x ecosystem
  • Neural Network Architecture: Layer design, activation functions (ReLU, sigmoid, softmax), and network topology
  • Optimization Techniques: SGD, Adam, RMSProp optimizers, backpropagation, and learning rate tuning
  • Specialized Networks: CNN implementation with convolution/pooling layers and RNN/LSTM for sequence modeling
  • Advanced Methodologies: Transfer learning workflows, pre-trained model fine-tuning, and layer freezing
  • Model Production: Checkpoint management, model serialization, deployment pipelines, and troubleshooting strategies

Curriculum Details

Eligibility:
Strong Python programming and machine learning fundamentals
Prerequisites:
Neural network concepts and deep learning theory
Duration:
10 intensive lessons + neural network capstone project
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