Deep Learning A-Z 2026: Neural Networks, AI, AWS & LLM Prize

Students : 485,891
Duration : 22.5 hours & 34 articles & 3 downloadable resources
Avg Rating : 4.5 (46,751 ratings)
Original Price : $119.99
Discount Price : $12
Instructor / Provider : Kirill Eremenko, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

What You’ll Learn

• Artificial Neural Networks (ANNs):
  • Understand the core concepts and intuition behind ANNs.
  • Apply ANNs to solve real-world problems with hands-on implementation.
• Convolutional Neural Networks (CNNs):
  • Grasp the principles of CNNs, the cornerstone of computer vision.
  • Use CNNs to tackle image classification, detection, and other visual tasks.
• Recurrent Neural Networks (RNNs):
  • Learn the intuition behind RNNs and their focus on sequential data.
  • Apply RNNs to natural language processing (NLP) tasks such as language modeling and sentiment analysis.
• Self-Organizing Maps (SOMs):
  • Understand the intuition and application of SOMs for clustering and visualization tasks.
• Boltzmann Machines:
  • Explore the theory and practical applications of Boltzmann Machines for feature learning and data generation.
• Autoencoders:
  • Dive into the concept of Autoencoders for dimensionality reduction and unsupervised learning tasks.

Advanced Applications

1. AI-Powered Chat Systems:
   • Build advanced systems with ChatGPT to automate customer interactions, answer queries, and collect user data.
   • Leverage proprietary algorithms like Natural Language Understanding (NLU) and Natural Language Generation (NLG) to ensure context-aware and accurate responses.
2. Software 2.0 Development:
   • Use AI models to enhance business workflows by integrating Transfer Learning and customized formulations.
   • Automate customer support at scale with AI-driven chatbots for text and voice, ensuring exceptional customer service while reducing operational costs.
3. Real-World Impact:
   • Solve complex problems in engineering simulations, seismic imaging, and other industries with AI-driven insights.

By the end of this course, you will:

• Master the fundamentals of Deep Learning and its major architectures.
• Implement Deep Learning models using Python and TensorFlow.
• Train and deploy models for various tasks such as image recognition, text generation, and time-series prediction.
• Use CNNs to solve computer vision challenges effectively.
• Apply RNNs for NLP tasks like text classification, translation, and sentiment analysis.
• Develop AI-powered systems using ChatGPT for autonomous conversation management.

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