Overview

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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.

Syllabus

Welcome

In this module, we will introduce you to the author and the key objectives of the course. You will gain insights into the learning approach and understand the resources and prerequisites necessary to begin your learning journey. Additionally, this section outlines the topics and content that will be covered throughout the course.

Convolutional Neural Networks (CNNs)

In this module, we will explore the fundamentals of Convolutional Neural Networks (CNNs), beginning with the core concept of convolution and its mathematical interpretation. You will learn how CNNs are structured and implemented, with hands-on applications using popular datasets like Fashion MNIST and CIFAR-10. Additionally, we'll cover advanced techniques such as data augmentation and batch normalization to enhance model accuracy.

Natural Language Processing (NLP)

In this module, we will explore the fundamentals of Natural Language Processing (NLP), starting with how text can be represented as sequence data using embeddings. You will learn how to preprocess text data using practical coding examples, and then dive into applying Convolutional Neural Networks (CNNs) to text for sequence analysis. The module concludes with hands-on work on text classification using CNN models.

Transfer Learning for Computer Vision

In this module, we will introduce you to transfer learning and its application in computer vision. You will explore popular pre-trained models, learn to manage large datasets, and implement two different approaches to transfer learning. Through practical coding exercises, you'll apply these techniques with and without data augmentation to enhance your understanding of how transfer learning optimizes deep learning models for new tasks.