Keras: Practical AI Projects & Deep Learning using Keras
Keras: Practical AI Projects & Deep Learning using Keras, available at $54.99, has an average rating of 4, with 73 lectures, based on 2 reviews, and has 2273 subscribers.
You will learn about Building chatbots using Keras. Sentiment analysis implementation with recurrent neural networks (RNN). Image classification techniques using Keras. Advanced face recognition applications using computer vision and deep learning. Practical project implementation on Google Colab. Text preprocessing techniques like Bow Model, Count Vectorizer, Stemming, and Lemmatization. Model training, evaluation, and prediction. Pretrained model utilization and fine-tuning. Image preprocessing, augmentation, and visualization. Face detection and recognition algorithms. Embedding generation and classification. Real-world implementation and testing of AI models. This course is ideal for individuals who are Students or professionals seeking to enhance their skills in machine learning and deep learning. or Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision. or Software engineers interested in developing advanced applications using Keras and TensorFlow. or Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects. or Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles. or Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning. It is particularly useful for Students or professionals seeking to enhance their skills in machine learning and deep learning. or Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision. or Software engineers interested in developing advanced applications using Keras and TensorFlow. or Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects. or Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles. or Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning.
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Summary
Title: Keras: Practical AI Projects & Deep Learning using Keras
Price: $54.99
Average Rating: 4
Number of Lectures: 73
Number of Published Lectures: 73
Number of Curriculum Items: 73
Number of Published Curriculum Objects: 73
Original Price: $99.99
Quality Status: approved
Status: Live
What You Will Learn
- Building chatbots using Keras. Sentiment analysis implementation with recurrent neural networks (RNN).
- Image classification techniques using Keras. Advanced face recognition applications using computer vision and deep learning.
- Practical project implementation on Google Colab. Text preprocessing techniques like Bow Model, Count Vectorizer, Stemming, and Lemmatization.
- Model training, evaluation, and prediction. Pretrained model utilization and fine-tuning. Image preprocessing, augmentation, and visualization.
- Face detection and recognition algorithms. Embedding generation and classification. Real-world implementation and testing of AI models.
Who Should Attend
- 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.
Target Audiences
- 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.
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.
Course Curriculum
Chapter 1: Building A Chatbot with keras
Lecture 1: Introduction to Project
Lecture 2: Bow Model
Lecture 3: Count Vectorizer
Lecture 4: Text Data
Lecture 5: Text Data Continue
Lecture 6: Limit Number of Features
Lecture 7: Stop Words
Lecture 8: Stemming
Lecture 9: Stemming Continue
Lecture 10: Lemmatization
Lecture 11: ML Model on Text Data
Lecture 12: TF-TF-IDF Vectorizer
Lecture 13: Spacy Word2Vec
Lecture 14: Requirements
Lecture 15: Hindson Implementation
Lecture 16: Hindson Implementation Continue
Lecture 17: Neural Networks
Lecture 18: Generative Chatbots Part 1
Lecture 19: Generative Chatbots Part 2
Lecture 20: Generative Chatbots Part 3
Lecture 21: Generative Chatbots Part 4
Lecture 22: Generative Chatbots Part 5
Lecture 23: Attentive Chatbots Part 1
Lecture 24: Attentive Chatbots Part 2
Lecture 25: Attentive Chatbots Part 3
Lecture 26: Advanced Chatbot
Lecture 27: Advanced Chatbot – Evaluation
Lecture 28: Conclusion
Chapter 2: Project On Keras: Sentimental Analysis Using RNN
Lecture 1: Introduction to Project
Lecture 2: Google Collab
Lecture 3: Downloading IMBD Dataset
Lecture 4: Padding Sequences
Lecture 5: Basic LSTM Model
Lecture 6: Training
Lecture 7: Plotting
Lecture 8: Predicting on Basic LSTM
Lecture 9: Complex LSTM Model with Training
Lecture 10: Prediction with Complex LSTM
Chapter 3: Project On Keras – Image Classification
Lecture 1: Introduction to Project
Lecture 2: Google Collab
Lecture 3: Uploading
Lecture 4: Downloading the Dataset
Lecture 5: Pretrained Model
Lecture 6: Intermediate Layer Visualization
Lecture 7: Model Creation and Image Augmentation
Lecture 8: Compiling and Training Model
Lecture 9: Loss Values
Lecture 10: Test Images and Visualization
Lecture 11: Retraining the Model
Chapter 4: Project On Keras – Creating An Advanced Face Recognition Computer Vision App
Lecture 1: Introduction to Course
Lecture 2: CNN for Image Processing
Lecture 3: Image Preprocessing
Lecture 4: Saving and Loading the Models
Lecture 5: Getting System Ready
Lecture 6: Reading the Image Data
Lecture 7: Detect Faces MTCNN
Lecture 8: Draw Bounding Box
Lecture 9: Draw Key points
Lecture 10: Apply on Group of Images
Lecture 11: Extract Faces from Image
Lecture 12: Face Detection Summary
Lecture 13: Face Recognition
Lecture 14: Fashion Dataset
Lecture 15: Load Faces
Lecture 16: Load Dataset from Folders
Lecture 17: Load Dataset from Folders Continue
Lecture 18: Generate Face Embeddings
Lecture 19: Face Embeddings
Lecture 20: Building Classifier on Embeddings
Lecture 21: Building Classifier on Embeddings Continue
Lecture 22: Testing for Real Implementation
Lecture 23: Use Kera's DNN with Face net
Lecture 24: Conclusion
Instructors
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EDUCBA Bridging the Gap
Learn real world skills online
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- 5 stars: 1 votes
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