Mastering Advanced Deep Learning Pro Certification™

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Mastering Advanced Deep Learning Pro Certification™
Published 1/2025
Created by Dr. Noble Arya Full-Stack Data Scientist, Ai Researcher End to End
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 49 Lectures ( 13h 26m ) | Size: 7.44 GB​

Unlock the Full Potential of Deep Learning with Hands-On Expertise in AI Solutions and Advanced Techniques
What you'll learn
Introduction to Deep Learning: Understand the definition, role, and components of Deep Learning in AI.
Applications of Deep Learning: Explore real-world applications like healthcare, finance, retail, and autonomous systems.
Artificial Neural Networks (ANN): Learn the structure and functioning of ANNs with input, hidden, and output layers.
Backpropagation: Master how backpropagation optimizes neural networks through gradient descent.
Applications of ANN: Apply ANN to tasks like image classification, NLP, and predictive modeling.
Convolutional Neural Networks (CNN): Dive into CNN architecture for analyzing image data effectively.
Applications of CNN: Use CNNs for face recognition, medical imaging, and autonomous vehicle systems.
Advanced CNN Concepts: Study techniques like padding, stride, and dropout to enhance CNN performance.
Recurrent Neural Networks (RNN): Understand RNNs for modeling sequential data with temporal dependencies.
Vanishing and Exploding Gradients: Learn solutions to gradient problems, like LSTMs and GRUs.
Applications of RNN: Use RNNs in language modeling, time-series forecasting, and speech recognition.
Long Short-Term Memory (LSTM): Discover how LSTMs solve sequential learning challenges using memory gates.
Applications of LSTM: Apply LSTMs to tasks like sentiment analysis and predictive maintenance.
Gated Recurrent Unit (GRU): Understand GRUs for simpler and efficient sequential data modeling.
GANs (Generative Adversarial Networks): Explore GANs for generating synthetic data and creative applications.
Transfer Learning: Reduce training time by leveraging pre-trained models for specific tasks.
Pre-trained Models: Use models like VGG and ResNet for feature extraction and fine-tuning.
Evaluation Metrics: Evaluate models using metrics like accuracy, precision, recall, and F1-score.
Loss Functions: Learn loss functions like cross-entropy for classification and MSE for regression.
Computer Vision Basics: Study how AI processes and analyzes visual data for insights.
Deep Learning in Computer Vision: Implement CNNs for tasks like image segmentation and detection.
Object Detection: Apply YOLO, SSD, and Faster R-CNN for real-time object detection.
Facial Recognition: Explore algorithms for face detection, analysis, and recognition systems.
Motion Analysis and Tracking: Track objects and analyze motion using techniques like optical flow.
3D Vision: Reconstruct 3D structures and enable depth perception from 2D images.
Applications of Computer Vision: Implement vision solutions in healthcare, retail, security, and AR/VR.
Requirements
This masterclass is designed for everyone-no prior experience is required, as the concepts are explained in a simple and accessible manner.
Description
Mastering Advanced Deep Learning Pro Certification™ is the ultimate program designed to equip you with cutting-edge knowledge and practical skills in Deep Learning and Computer Vision. From foundational concepts like Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) to advanced topics such as Transfer Learning, GANs, and 3D Vision, this course covers everything you need to become a leader in AI innovation.Explore real-world applications across healthcare, finance, retail, autonomous systems, and more. Master tools and techniques for image processing, object detection, facial recognition, optical character recognition (OCR), and motion analysis. Learn hands-on approaches to solve industry-relevant challenges with supervised, unsupervised, and reinforcement learning methods.With deep dives into state-of-the-art architectures like ResNet, VGG, and Mask R-CNN, this program also emphasizes key evaluation metrics, optimization strategies, and best practices for building AI models with impact. Packed with case studies, this course bridges theory and implementation, making it perfect for aspiring Deep Learning Engineers, Data Scientists, AI Researchers, and beyond.Join us to unlock the full potential of Deep Learning and shape the future of AI solutions.1. Introduction to Deep LearningDefinition of Deep Learning and its role in Artificial Intelligence (AI).Key differences between Deep Learning and traditional Machine Learning.Overview of 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 techniques.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 (NLP) tasks like sentiment analysis.Predictive modeling in business and research.6. Convolutional Neural Networks (CNN) ExplainedWhat is a CNN, and why is it essential?Architecture: Convolutional layers, pooling layers, and fully connected layers.Benefits of CNNs for image data processing.7. Applications of CNN in Real-World AIFace Recognition: Biometrics and authentication systems.Medical Imaging: Disease detection and diagnostics.Autonomous Vehicles: Vision systems for navigation.8. Advanced Concepts in CNNTechniques like padding, stride, and receptive fields.Popular CNN architectures: LeNet, AlexNet, and VGG.Performance improvement techniques: Dropout and data augmentation.9. Recurrent Neural Networks (RNN) - Handling Sequential DataIntroduction to RNNs and their differences from ANNs.Use cases for sequential data (time-series, text).Challenges: Vanishing and exploding gradients.10. Solving the Vanishing Gradient ProblemUnderstanding vanishing and exploding gradients.Solutions: LSTMs, GRUs, and gradient clipping.11. Applications of RNN in Real-World AILanguage modeling and text generation.Time-series forecasting.Speech recognition and video analysis.12. Long Short-Term Memory (LSTM) NetworksArchitecture: Memory cells, input gates, forget gates, and output gates.How LSTMs address the vanishing gradient problem.13. Applications of LSTMsSentiment analysis and opinion mining.Machine translation.Predictive maintenance in industries.14. Gated Recurrent Unit (GRU) NetworksIntroduction to GRUs and their simplified structure.Differences between GRUs and LSTMs.15. Applications of GRUsChatbots and conversational AI.Real-time anomaly detection.16. Generative Adversarial Networks (GANs)How GANs work: Generator and discriminator concepts.Applications: Synthetic data, image generation, and style transfer.17. Transfer Learning and Pre-Trained ModelsOverview of Transfer Learning and its advantages.Pre-trained models: VGG, ResNet, Inception.Fine-tuning models for specific applications.18. Evaluation Metrics in Deep LearningClassification metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC.Regression metrics: Mean Squared Error (MSE), R-Squared.19. Loss Functions in Deep LearningCross-Entropy Loss for classification problems.Mean Squared Error Loss for regression problems.Computer Vision Topics20. Introduction to Computer VisionOverview of Computer Vision and its significance in AI.Understanding how computers interpret and analyze visual data.21. Deep Learning Models for Computer VisionIntroduction to Convolutional Neural Networks (CNNs) and their role in Computer Vision.Key models like AlexNet, VGG, ResNet, and EfficientNet.22. Image Processing with Deep LearningTechniques for preprocessing images (e.g., normalization, resizing, augmentation).Importance of image filtering and transformations.23. Computer Vision Image Segmentation ExplainedExplanation of image segmentation and its use in dividing images into meaningful regions.Differences between semantic and instance segmentation.24. Image Features and Detection for Computer VisionUnderstanding feature extraction (edges, corners, blobs).Techniques for feature detection and matching.25. SIFT (Scale-Invariant Feature Transform) ExplainedExplanation of SIFT and its role in identifying key points and matching across images.Applications of SIFT in image stitching and object recognition.26. Object Detection in Computer VisionKey algorithms: YOLO, SSD, Faster R-CNN.Techniques for detecting objects in real-time.27. Datasets and Benchmarks in Computer VisionOverview of popular datasets (e.g., COCO, ImageNet, Open Images).Importance of benchmarks in evaluating models.28. Segmentation in Computer VisionExplanation of segmentation techniques (e.g., region-based and clustering-based methods).Importance of accurate segmentation for downstream tasks.29. Supervised Segmentation Methods in Computer VisionOverview of deep learning methods like U-Net and Mask R-CNN.Supervised learning approaches for segmentation tasks.30. Unlocking the Power of Optical Character Recognition (OCR)Explanation of OCR and its role in text recognition from images.Applications in document processing, ID verification, and automation.31. Handwriting Recognition vs. Printed TextDifferences in recognizing handwriting and printed text.Challenges and deep learning techniques for each.32. Facial Recognition and Analysis in Computer VisionApplications of facial recognition (e.g., authentication, surveillance).Understanding face detection and facial analysis methods.33. Facial Recognition Algorithms and TechniquesPopular algorithms like Eigenfaces, Fisherfaces, and deep learning models.Role of embeddings and feature vectors in facial recognition.34. Camera Models and Calibrations in Computer VisionOverview of camera models and intrinsic/extrinsic parameters.Basics of lens distortion and its correction.35. Camera Calibration Process in Computer VisionSteps for calibrating a camera and improving image accuracy.Tools and libraries for camera calibration.36. Motion Analysis and Tracking in Computer VisionTechniques for motion detection and object tracking (e.g., optical flow, Kalman filters).Applications in surveillance and autonomous vehicles.37. Segmentation and Grouping Moving ObjectsMethods for segmenting and grouping moving objects in videos.Applications in traffic monitoring and video analytics.38. 3D Vision and Reconstruction in Computer VisionIntroduction to 3D vision and its importance in depth perception.Methods for reconstructing 3D structures from 2D images.39. Stereoscopic Vision and Depth Perception in Computer VisionExplanation of stereoscopic vision and its use in 3D mapping.Applications in robotics, AR/VR, and 3D modeling.40. Applications of Computer VisionBroad applications in healthcare, agriculture, retail, and security.Real-world examples of AI-driven visual solutions.42. Applications of Image Segmentation in Computer VisionUse cases in medical imaging, self-driving cars, and satellite imagery.How segmentation helps in data analysis and decision-making.43. Real-Time Case Study Applications of Computer VisionEnd-to-end case studies in self-driving cars, facial recognition, and augmented reality.Practical insights into implementing Computer Vision solutions in real-time scenarios.This course offers a comprehensive journey into the world of Deep Learning and Computer Vision, ensuring you're equipped to excel as an AI professional.
Who this course is for
This course is ideal for anyone aspiring to learn future-ready skills and pursue careers such as Deep Learning Engineer, Data Scientist, Senior Data Scientist, AI Scientist, AI Engineer, AI Researcher, or AI Expert.

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