The Introduction to TensorFlow for AI, ML, and Deep Learning course is designed to equip learners with a strong foundation in using TensorFlow, one of the most popular open-source frameworks for building machine learning and deep learning models. This course covers the essential concepts of artificial intelligence, machine learning, and deep learning, while providing hands-on experience in developing models using TensorFlow. Learners will gain practical skills to build, train, and deploy AI models effectively.
Module 1: Introduction to AI, ML, and Deep Learning Overview of Artificial Intelligence, Machine Learning, and Deep Learning Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning Applications of AI and ML in real-world scenarios Introduction to AI frameworks and tools.
Module 2: Getting Started with TensorFlow Installing TensorFlow and setting up the environment Introduction to TensorFlow 2.x Understanding tensors and basic TensorFlow operations Overview of Keras API.
Module 3: Tensors and Tensor Operations Creating and manipulating tensors Tensor indexing, slicing, and reshaping Mathematical operations with tensors Broadcasting and advanced tensor operations.
Module 4: Machine Learning with TensorFlow Building simple regression and classification models Loss functions, optimizers, and metrics Training and evaluating ML models Using datasets with TensorFlow.
Module 5: Deep Learning Fundamentals Introduction to neural networks Activation functions and their importance Forward and backward propagation Understanding model loss, accuracy, and evaluation metrics.
Module 6: Convolutional Neural Networks (CNNs) Understanding CNN architecture and layers Building image classification models Implementing pooling and convolution layers Practical projects on image recognition.
Module 7: Recurrent Neural Networks (RNNs) and LSTM Understanding sequence modeling Building RNN and LSTM models Applications in time-series prediction and NLP Hands-on project with sequential data.
Module 8: Model Optimization and Regularization Techniques to improve model performance Dropout, batch normalization, and early stopping Hyperparameter tuning Preventing overfitting and underfitting.
Module 9: TensorFlow Data Pipelines Handling large datasets efficiently Preprocessing and augmenting data Using TensorFlow Dataset API Data loading, shuffling, and batching.
Module 10: Model Deployment and Real-World Projects Saving and loading trained models Exporting models for web and mobile applications Deployment using TensorFlow Serving or TFLite.
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