Description
Deep Learning Specialization: Advanced AI, Hands on Lab. This course is a comprehensive program to master advanced AI techniques through hands-on experience. The course begins with the basics of neural networks, including activation functions, loss functions, and optimization, and moves on to convolutional neural networks (CNNs) and the implementation of architectures such as LeNet, VGG, and ResNet in image classification and object recognition projects. It then explores sequential models such as RNN, LSTM, and GRU, along with attention mechanisms for applications such as time series prediction and text generation. A significant section is devoted to transformers and natural language processing (NLP), where the concepts of self-attention, mini-transformer models, and working with pre-trained models such as BERT and GPT are taught. The course then explores generative models including autoencoders, VAEs, GANs, and propagation models for creative applications. Reinforcement learning is complemented by practice on Q-learning, DQNs, and policy gradient methods in simulated environments. Finally, essential topics of model deployment with Flask or FastAPI and Docker, explainability with SHAP/LIME, as well as ethical and fairness considerations in AI systems are covered. Combining theory and weekly exercises, this specialization provides the skills necessary to design, train, deploy, and evaluate AI systems in real-world situations.
What you will learn
- Design, train, and optimize advanced deep learning models including CNNs, RNNs, Transformers, GANs, and Diffusion Models for real-world applications.
- Applying reinforcement learning techniques such as Q-Learning, Deep Q-Networks, and Policy Gradient methods.
- Deploy deep learning models in production environments using Flask, FastAPI, Docker, and cloud platforms (AWS, GCP, Azure).
- Responsible interpretation and evaluation of AI models using Explainable Artificial Intelligence (XAI) methods such as SHAP, LIME, and attention visualization.
- Analysis of emerging AI trends including multi-faceted systems, generative AI, and the path to artificial general intelligence (AGI).
This course is suitable for people who:
- Data scientists and aspiring machine learning engineers.
- Artificial intelligence enthusiasts and researchers.
- Software developers and engineers.
- Students and professionals in STEM (Science, Technology, Engineering, and Mathematics) fields.
- Entrepreneurs and innovators.
Course Details: Deep Learning Specialization: Advanced AI, Hands on Lab
- Publisher: Udemy
- Instructor: Data Science Academy , School of AI
- Training level: Beginner to advanced
- Training duration: 4 hours and 31 minutes
- Number of lessons: 39
Course headings
Prerequisites for the Deep Learning Specialization: Advanced AI, Hands on Lab course
- Basic Knowledge of Python
- Fundamental Understanding of Machine Learning
- Linear Algebra & Probability Basics
- Deep Learning Frameworks (Optional but Helpful)
- Tools & Setup
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: English
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
2.2 GB
