Title
Advanced Deep Learning Mastery End to End ™
Unlock the Power of Artificial Neural Networks, CNNs, RNNs, GANs, and Transfer Learning for Real-World AI Applications
What you will learn
Introduction to Deep Learning
Applications of Deep Learning in Real-World Scenarios
Artificial Neural Networks (ANN) - The Backbone of Deep Learning
Backpropagation - The Heart of Artificial Neural Networks
Applications of Artificial Neural Networks (ANN) in Real-World Scenarios
Convolutional Neural Networks (CNN) Explained
Applications of Convolutional Neural Networks (CNN) in Real-World AI
Convolutional Neural Network (CNN) Deep Dive
Introduction to Recurrent Neural Networks (RNN)
Vanishing and Exploding Gradient Problem in Deep Learning
Applications of Recurrent Neural Networks (RNN) in Real-World AI
Long Short-Term Memory (LSTM) Networks
Applications of LSTM
Application: Short-Term Memory LSTM in AIML
Gated Recurrent Unit (GRU) Simplified
Gating Mechanisms in GRU
Applications of Gated Recurrent Unit (GRU) Networks
GANs - The Future of Data Generation
Applications of GANs - Revolutionizing AI
What is Transfer Learning?
Pre-trained Models (VGG, ResNet, Inception)
Classification Metrics (Accuracy, Precision, Recall, F1-Score, AUC-ROC)
Regression Metrics (Mean Squared Error, R-Squared)
Loss Functions (Cross-Entropy, Mean Squared Error)
Why take this course?
The outline you've provided describes a comprehensive curriculum on Deep Learning, covering both theoretical concepts and practical applications. Here's a summary of what the course seems to offer based on the topics listed:
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USING PYTHON & TENSORFLOW/KERAS: The course likely begins with an introduction to Python programming and then moves onto TensorFlow/Keras for building and training deep learning models.
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NEURAL NETWORKS: This section would cover the basics of neural networks, including how they work, types of layers (dense, convolutional, recurrent), and how to apply them to various problems.
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DEEP LEARNING ALGORITHMS: Detailed study of different deep learning architectures such as Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU).
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BACKPROPAGATION & OPTIMIZATION: This part would delve into how backpropagation works, optimization algorithms like SGD, Adam, etc., and strategies to minimize loss functions.
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IMPLEMENTING LSTM & GRU: Hands-on practice with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, including understanding their gates and states for processing sequential data.
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GANS (GENERATIVE ADVERSARIAL NETWORKS): Exploration of GANs architecture, how they consist of a generator and discriminator, and how they can generate new data with learned features.
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Transfer LEARNING: Understanding how to leverage pre-trained models like VGG, ResNet, and Inception to improve model performance with less training data and time.
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EVALUATION METRICS AND LOSS FUNCTIONS: Learning about various metrics for evaluating models, including accuracy, precision, recall, F1-Score, AUC-ROC for classification tasks, and Mean Squared Error (MSE) for regression tasks.
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APPLICATIONS IN DIFFERENT DOMAINS: The course likely concludes with examples of how deep learning models can be applied in various domains such as image classification, natural language processing, sequence prediction, anomaly detection, and data generation.
Throughout the course, students would not only learn the theoretical aspects of each topic but also gain practical experience by implementing algorithms, training models, and evaluating their performance on real-world datasets. The curriculum is designed to take students from beginners to experts in the field of deep learning, enabling them to create innovative solutions and tackle complex problems using AI.
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