Description
Mastering Advanced Deep Learning Pro Certification™. This course is the ultimate, comprehensive training program designed to transform you into an AI expert. It provides you with advanced theoretical knowledge and essential practical skills in the fields of deep learning and computer vision. Course topics range from basic fundamentals such as artificial neural networks and convolutional neural networks to advanced topics such as transfer learning, adversarial networks, and 3D vision. You will experience practical applications of these techniques in industries including healthcare, finance, retail, and autonomous systems, and gain proficiency in applied tools and techniques such as image processing, object recognition, facial recognition, optical character recognition, and motion analysis. This program provides a hands-on approach to solving real-world industry challenges using supervised, unsupervised, and reinforcement learning methods. Also, exploring complex architectures such as ResNet, VGG, and Mask R-CNN, with an emphasis on evaluation criteria, optimization strategies, and best practices for building powerful AI models, are other key features of this course. Combining theory and implementation through multiple case studies, this course is ideal for deep learning engineers, data scientists, and AI researchers seeking world-class skills, preparing you to push the boundaries of innovation and shape the future of AI solutions.
What you will learn
- Introduction to deep learning:
- Understand the definition, role, and components of deep learning in artificial intelligence.
- Exploring real-world applications such as healthcare, finance, retail, and autonomous systems.
- Artificial Neural Networks (ANN):
- Structure and operation of ANN with input, hidden, and output layers.
- Mastering the Backpropagation method for optimizing neural networks via Gradient Descent.
- Application of ANN for tasks such as image classification, natural language processing (NLP), and predictive modeling.
- Convolutional Neural Networks (CNN):
- CNN architecture for effective analysis of image data.
- Using CNNs for facial recognition, medical imaging, and autonomous vehicle systems.
- Study advanced CNN techniques such as Padding, Stride, and Dropout to improve performance.
- Recurrent Neural Networks (RNN):
- Understanding RNNs for modeling sequential data with temporal dependencies.
- Solutions to Gradient Vanishing and Exploding problems, such as LSTM and GRU.
- Application of RNN in language modeling, time series prediction, and speech recognition.
- Advanced networks:
- Long Short-Term Memory (LSTM) networks and how to solve sequential learning challenges using memory gates.
- Gated Recurrent Unit (GRU) networks for simpler and more efficient modeling of sequential data.
- Generative adversarial networks (GANs) for synthetic data generation and creative applications.
- Transfer learning and pre-trained models:
- Reduce training time by using pre-trained models such as VGG and ResNet for feature extraction and fine-tuning.
- Evaluation and loss functions:
- Evaluate models using metrics such as Accuracy, Precision, Recall, and F1-Score.
- Learning loss functions such as Cross-Entropy Loss for classification and Mean Squared Error (MSE) for regression.
- Computer Vision:
- Fundamentals of computer vision and how artificial intelligence processes and analyzes visual data.
- Implementing CNNs for tasks such as image segmentation and recognition.
- Object detection using algorithms such as YOLO, SSD, and Faster R-CNN.
- Facial recognition, motion analysis and tracking, and 3D vision for reconstructing structures.
- Implementing vision solutions in healthcare, retail, security, and augmented reality/virtual reality (AR/VR).
This course is suitable for people who:
-
This course is ideal for anyone who aspires to learn future-oriented skills and pursue careers such as Deep Learning Engineer, Data Scientist, Senior Data Scientist, AI Scientist, AI Engineer, AI Researcher, or AI Specialist.
Course details
- Publisher: Udemy
- Lecturer: Dr. Noble Arya Full-Stack Data Scientist, AI/ML/DL Researcher
- Training level: Beginner to advanced
- Training duration: 13 hours and 26 minutes
- Number of lessons: 49
Course headings
Mastering Advanced Deep Learning Pro Certification™ Course Prerequisites
- This masterclass is designed for everyone, no prior experience is required, as the concepts are explained in a simple and accessible manner.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
6.9 GB
