Simplified 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'll 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)

Requirements

Anyone can learn this Master Class it is very simple.

Familiarity with Python is essential (e.g., loops, functions, and basic data structures). Understanding of libraries like NumPy, pandas, and matplotlib is a plus.

Description

Master the fundamentals and advanced concepts of Deep Learning in this comprehensive course tailored for aspiring AI professionals. Learn how to build, train, and optimize Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and cutting-edge architectures like GANs, LSTMs, and GRUs.Discover how Deep Learning is transforming real-world industries through practical applications in image recognition, natural language processing, and predictive analytics. Dive into the details of backpropagation, gating mechanisms, and the vanishing/exploding gradient problem, and learn how to overcome these challenges.Gain hands-on experience with Transfer Learning, leveraging pre-trained models like VGG, ResNet, and Inception for faster and more accurate results. Evaluate your models using key metrics, including Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Regression metrics like Mean Squared Error and R-Squared.Whether you're a beginner or an experienced professional, this course will equip you with the skills and knowledge to innovate in the field of Artificial Intelligence.Key Topics Covered1. Introduction to Deep LearningDefinition of Deep Learning and its role in AI.Difference between Deep Learning and traditional Machine Learning.Key components: Neural Networks, learning algorithms, and data.2. Applications of Deep Learning in Real-World ScenariosHealthcare: Disease diagnosis and medical imaging.Finance: Fraud detection and stock market prediction.Retail: Personalized recommendations and inventory management.Autonomous systems: Self-driving cars and robotics.3. Artificial Neural Networks (ANN) - The Backbone of Deep LearningWhat are Artificial Neural Networks?Structure: Input layer, hidden layers, and output layer.Activation functions: Sigmoid, ReLU, and Softmax.4. Backpropagation - The Heart of Artificial Neural NetworksHow backpropagation works: Forward pass and backward pass.Gradient descent optimization.Importance of backpropagation in training Deep Learning models.5. Applications of Artificial Neural Networks (ANN) in Real-World ScenariosImage classification and object detection.Natural language processing tasks like sentiment analysis.Predictive modeling in business and research.6. Convolutional Neural Networks (CNN) ExplainedWhat is a CNN?CNN architecture: Convolutional layers, pooling layers, and fully connected layers.Advantages of CNNs for image data.7. Applications of Convolutional Neural Networks (CNN) in Real-World AIFace recognition and biometric systems.Medical imaging for disease detection.Autonomous vehicle vision systems.8. Convolutional Neural Network (CNN) Deep DiveAdvanced concepts: Padding, stride, and receptive fields.Popular CNN architectures: LeNet, AlexNet, and VGG.Techniques to improve CNN performance: Dropout and data augmentation.9. Introduction to Recurrent Neural Networks (RNN)What are RNNs and how they differ from ANNs?Use of sequential data in RNNs.Challenges: Vanishing and exploding gradients.10. Vanishing and Exploding Gradient Problem in Deep LearningExplanation of vanishing and exploding gradients.Impact on model training.Solutions: LSTMs, GRUs, and gradient clipping.11. Applications of Recurrent Neural Networks (RNN) in Real-World AILanguage modeling and text generation.Time series forecasting.Speech recognition and video analysis.12. Long Short-Term Memory (LSTM) NetworksIntroduction to LSTM architecture.Memory cells, input gates, forget gates, and output gates.Why LSTMs solve vanishing gradient problems.13. Applications of LSTMSentiment analysis and opinion mining.Machine translation.Predictive maintenance in industries.14. Application: Short-Term Memory LSTM in AIMLSpecific AIML applications using LSTM for real-time predictions.15. Gated Recurrent Unit (GRU) SimplifiedWhat are GRUs?Differences between GRUs and LSTMs.Simplicity and efficiency of GRUs in modeling sequential data.16. Gating Mechanisms in GRUExplanation of update and reset gates.Role of gating mechanisms in learning temporal dependencies.17. Applications of Gated Recurrent Unit (GRU) NetworksChatbots and conversational AI.Music generation and composition.Real-time anomaly detection.18. GANs - The Future of Data GenerationWhat are GANs?How GANs work: Generator and discriminator.Applications in creating synthetic data.19. Applications of GANs - Revolutionizing AIImage generation and super-resolution.Style transfer and artistic applications.Synthetic data for training AI models.20. What is Transfer Learning?Introduction to transfer learning.Advantages: Reduced training time and improved accuracy.Scenarios where transfer learning is useful.21. Pre-trained Models (VGG, ResNet, Inception)Overview of pre-trained models and their architectures.Use cases: Image classification and feature extraction.How to fine-tune pre-trained models for specific tasks.22. Classification Metrics (Accuracy, Precision, Recall, F1-Score, AUC-ROC)Explanation of each metric and its importance.When to use specific metrics for classification problems.23. Regression Metrics (Mean Squared Error, R-Squared)Definition and calculation of regression metrics.Importance in evaluating regression models.24. Loss Functions (Cross-Entropy, Mean Squared Error)Overview of loss functions used in Deep Learning.Cross-Entropy Loss for classification problems.Mean Squared Error Loss for regression problems.Take the next step in your AI journey and become a Deep Learning expert. Enroll today to build innovative solutions for the challenges of tomorrow

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Applications of Deep Learning in Real-World Scenarios

Lecture 2 Applications of Deep Learning in Real-World Scenarios

Section 3: Artificial Neural Networks- The Backbone of Deep Learning

Lecture 3 Artificial Neural Networks- The Backbone of Deep Learning

Section 4: Backpropagation- The Heart of Artificial Neural Networks in Deep Learning

Lecture 4 Backpropagation- The Heart of Artificial Neural Networks in Deep Learning

Section 5: Applications of Artificial Neural Networks (ANN) in Real-World Scenarios

Lecture 5 Applications of Artificial Neural Networks (ANN) in Real-World Scenarios

Section 6: Convolutional Neural Networks (CNN) Explained

Lecture 6 Convolutional Neural Networks (CNN) Explained

Section 7: Applications of Convolutional Neural Networks (CNN) in Real-World AI

Lecture 7 Applications of Convolutional Neural Networks (CNN) in Real-World AI

Section 8: Convolutional Neural Network (CNN) Deep Dive

Lecture 8 Convolutional Neural Network (CNN) Deep Dive

Section 9: Introduction to Recurrent Neural Networks (RNN)

Lecture 9 Introduction to Recurrent Neural Networks (RNN)

Section 10: Vanishing and Exploding Gradient Problem in Deep Learning

Lecture 10 Vanishing and Exploding Gradient Problem in Deep Learning

Section 11: Applications of Recurrent Neural Networks (RNN) in Real-World AI

Lecture 11 Applications of Recurrent Neural Networks (RNN) in Real-World AI

Section 12: Long Short-Term Memory Networks

Lecture 12 Long Short-Term Memory Networks

Section 13: Applications of LSTM

Lecture 13 Applications of LSTM

Section 14: Applcation Short Term Memory LSTM

Lecture 14 Applcation Short Term Memory LSTM

Section 15: Gated Recurrent Unit (GRU)

Lecture 15 Gated Recurrent Unit (GRU)

Section 16: Gating Mechanisms In GRU

Lecture 16 Gating Mechanisms In GRU

Section 17: Applications of Gated Recurrent Unit (GRU) Networks

Lecture 17 Applications of Gated Recurrent Unit (GRU) Networks

Section 18: GANs The Future of Data Generation

Lecture 18 GANs The Future of Data Generation

Section 19: Applications of GANs- Revolutionizing AI

Lecture 19 Applications of GANs- Revolutionizing AI

Section 20: Generative Adversarial Networks (GANs)- The Generator Explained

Lecture 20 Generative Adversarial Networks (GANs)- The Generator Explained

Section 21: Transfer Learning AIML

Lecture 21 Transfer Learning AIML

Section 22: Choosing the Right Pre-Trained Model

Lecture 22 Choosing the Right Pre-Trained Model

Section 23: Applications of Transfer Learning in AI

Lecture 23 Applications of Transfer Learning in AI

Anyone who wants to learn future skills and become Deep Learning Engineer, Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.