Neural Networks and Convolutional Neural Networks Essential Training

Deepen your understanding of neural networks and convolutional neural networks (CNNs) with this comprehensive course. Instructor Jonathan Fernandes shows how to build and train models in Keras and PyTorch using datasets like MNIST and CIFAR‑10 for image classification. Apply activation functions, backpropagation, pooling techniques, and optimizers to strengthen model performance. Gain practical experience with essential datasets and pretrained models that support modern computer vision, including techniques such as image augmentation and transfer learning.

Designed for machine learning engineers with a solid grasp of Python and introductory AI concepts, this course balances theory with hands‑on implementation. Tune in and explore why different architectures work well for particular challenges and develop the skills needed to design, implement, and evaluate neural networks across a range of applications.

Learning objectives
Develop a comprehensive understanding of neural network fundamentals, enabling you to design appropriate solutions for machine learning problems.
Construct and implement fully-functional neural networks and CNNs using the Keras framework, with the ability to preprocess data, configure models, and evaluate performance.
Apply advanced techniques such as image augmentation and transfer learning to enhance CNN performance and efficiency in real-world image classification tasks.
Describe the historical context and significance of neural network development, including landmark challenges like XOR and ImageNet, to inform your approach to modern deep learning applications.