Embark on a journey through the intricate world of deep learning and neural networks. This course starts with a foundation in the history and basic concepts of neural networks, including perceptrons and multi-layer structures. As you progress, you'll explore the mechanics of training neural networks, covering activation functions and the backpropagation algorithm. The course then advances to artificial neural networks and their real-world applications, drawing inspiration from the human brain's architecture. You'll gain practical insights into input and output layers, the Sigmoid function, and key datasets like MNIST. Specialized topics such as feed-forward networks, backpropagation, and regularization techniques, including dropout strategies and batch normalization, are thoroughly covered. You'll also be introduced to powerful frameworks like TensorFlow and Keras. The course concludes with an in-depth study of convolutional neural networks (CNNs), focusing on their applications and principles for image and video analysis. This course is ideal for tech professionals and students with a basic understanding of programming and mathematics, particularly linear algebra, calculus, and basic probability.
Vectorized Methods
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Skills You'll Learn
TensorFlow, Backpropagation, Keras (Neural Network Library), Machine Learning, Artificial Intelligence
From the lesson
ANN - Feed Forward Network
In this module, we will focus on feed-forward networks, their operation modes, and the dimensions involved. We'll break down the pseudocode required for batch processing and introduce vectorized methods to optimize neural network training.
Taught By
Packt - Course Instructors