Course Content
Introduction to TensorFlow and Deep Learning
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Overview of TensorFlow
Basics of Deep Learning
TensorFlow Basics
Data Handling with TensorFlow and NumPy
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Working with Tensors
Input Pipelines with tf.data
Working with Datasets and Image Processing
Building Machine Learning Models with TensorFlow
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Linear and Logistic Regression
Implementing Neural Networks
Using Keras API for Model Building
Training and Optimizing Deep Learning Models
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Loss Functions and Optimizers
Evaluating Model Performance
Hyperparameter Tuning
Convolutional Neural Networks (CNNs) for Computer Vision
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Introduction to CNNs
Implementing CNNs with TensorFlow
Data Augmentation and Image Processing
Recurrent Neural Networks (RNNs) and NLP
0/3
Introduction to Sequence Models
Text Processing with TensorFlow
Implementing LSTMs for Time Series Forecasting
Generative Models and Advanced TensorFlow Applications
0/3
Generative Adversarial Networks (GANs)
Autoencoders for Anomaly Detection
Reinforcement Learning with TensorFlow
Model Deployment and Real-World Applications
0/3
Exporting and Saving Models
TensorFlow Lite for Mobile and Edge AI
Deploying Models on Cloud Platforms
Capstone Project, Assessment & Certification
0/3
Final Deep Learning Project
Practical Assignments & Case Studies
Certification Exam
TensorFlow with Python
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