Requirements
- Python programming language.
- Fundamentals of machine learning and deep learning concepts.
Description
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, students will transition to another project focusing on sentiment analysis with Recurrent Neural Networks (RNNs) using Keras. They will be introduced to Google Colab for collaborative work and IMBD dataset for sentiment analysis. The section will cover topics such as padding sequences, basic and complex LSTM models, and training procedures, enabling students to gain practical experience in sentiment analysis.
Section 3: Project On Keras – Image Classification
Continuing the journey, students will move to image classification projects in this section. They will learn to set up Google Colab, download datasets, and employ pretrained models for image classification tasks. Topics covered will include intermediate layer visualization, model creation, image augmentation, and model evaluation techniques.
Section 4: Project On Keras – Creating An Advanced Face Recognition Computer Vision App
In the final section, students will engage in creating an advanced face recognition application using computer vision techniques with Keras. They will explore Convolutional Neural Networks (CNNs) for image processing, face detection using MTCNN, and building a classifier for face recognition. This section will culminate in a comprehensive understanding of implementing deep learning models for real-world applications.
Who this course is for:
- Students or professionals seeking to enhance their skills in machine learning and deep learning.
- Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision.
- Software engineers interested in developing advanced applications using Keras and TensorFlow.
- Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects.
- Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles.
- Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning.
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