About this Course

Unlock the potential of deep learning by mastering Convolutional Neural Networks (CNNs) and Transfer Learning with hands-on experience using TensorFlow and Keras. This course offers a comprehensive introduction to CNNs, guiding you through their theoretical foundations, practical implementations, and applications in both image and text classification. With hands-on coding in TensorFlow, you\'ll build, optimize, and experiment with real-world datasets like CIFAR-10 and Fashion MNIST. Dive deep into Convolutional Neural Networks (CNNs) with TensorFlow. Starting with the basics of convolution, you\'ll explore advanced topics like data augmentation, batch normalization, and transfer learning. You\'ll not only work on image datasets but also gain insights into applying CNNs for natural language processing (NLP). Whether you are building from scratch or using pre-trained models, this course equips you with the skills to deploy CNNs in real-world applications. The course begins by establishing a strong theoretical understanding of CNNs, breaking down convolutions, filters, and layers. After this, you\'ll implement CNNs for popular datasets like Fashion MNIST and CIFAR-10, diving into hands-on coding sessions with TensorFlow and Keras. Practical exercises such as data augmentation and batch normalization will enhance your ability to improve model performance. Later, you\'ll explore CNNs in the context of natural language processing, understanding how CNNs can be applied to text classification. The final section focuses on transfer learning, where you\'ll work with pre-trained models like VGG and ResNet and apply them to new datasets. This course is ideal for data scientists, machine learning engineers, and developers familiar with Python, TensorFlow, and basic deep learning concepts. You should have a solid understanding of neural networks, and experience with coding in Python is necessary to follow the practical aspects of the course. Familiarity with TensorFlow is recommended but not mandatory.

Created by: Packt