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In this specialization, you will master deep learning using TensorFlow. It’s divided into three key sections: starting with the basics of artificial neural networks (ANNs), progressing through Recurrent Neural Networks (RNNs), and ending with Convolutional Neural Networks (CNNs). You will learn key concepts such as forward propagation, activation functions, and multiclass classification, with hands-on coding using real-world datasets like MNIST, CIFAR-10, and Fashion MNIST.
You will explore advanced architectures such as RNNs, GRUs, LSTMs, and CNNs. Practical sessions cover model optimization with techniques like gradient descent and Adam, and you’ll learn how to use TensorFlow and Keras for model evaluation. Additionally, you’ll apply deep learning models to time series prediction, natural language processing, and image classification tasks.
This specialization is suitable for learners with basic machine learning knowledge, and some Python experience is required. It’s an intermediate-level specialization.
By the end, you will be able to design and deploy deep learning models, optimizing them for tasks like image classification and time series forecasting.
Applied Learning Project
The specialization features hands-on projects where you will implement deep learning models using real-world datasets such as MNIST and CIFAR-10. These projects will challenge you to optimize models and apply them to practical tasks like image classification and time series prediction.
