Keras: Practical AI Projects & Deep Learning using Keras

About this course

Welcome to the comprehensive course on practical applications of deep learning with Keras! In this course, you will embark on an exciting journey through various projects aimed at developing practical skills in deep learning and neural networks using the Keras framework. Whether you're a beginner looking to get started with deep learning or an experienced practitioner seeking to enhance your skills, this course offers something for everyone.

Throughout this course, you will dive into hands-on projects covering a wide range of topics, including building chatbots, sentiment analysis using recurrent neural networks (RNNs), image classification, and advanced face recognition computer vision applications. Each project is carefully designed to provide you with practical experience and insights into real-world applications of deep learning.

By the end of this course, you will have gained valuable experience in implementing deep learning models, understanding their underlying principles, and applying them to solve complex tasks. Whether you're interested in natural language processing, computer vision, or any other domain, the skills you acquire in this course will be invaluable in your journey as a deep learning practitioner.

Get ready to unlock the full potential of deep learning with Keras and take your skills to the next level!

Section 1: Building A Chatbot with keras

In this section, students will embark on a practical journey of constructing a chatbot using Keras. They will begin with an introduction to the project's objectives, followed by an exploration of foundational concepts such as the Bag of Words (BoW) model, Count Vectorizer, and techniques for handling text data. Through a series of progressive lectures, students will delve into preprocessing steps, feature limitation strategies, and essential text processing elements like stop words and stemming.

Section 2: Project On Keras: Sentimental Analysis Using RNN

In the second section

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