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Generative AI & Deep Learning : All Models With Projects

Master Generative AI, Deep Learning Models, LLMs, CNN, RNN & Build Real-World AI Projects from Scratch

  1. Topics
  2. Development
  3. Deep Learning (DL)

Generative AI & Deep Learning : All Models With Projects

InstructorARUNNACHALAM SHANMUGARAAJAN
Duration1h 26m
Students3,985
Rating4.5 (36)
Sponsored
Price
$12.99
Coupon
None
No active coupon currently available
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Open on UdemyCurrent Udemy price

More Related Topics

  • Advanced Neural Networks171
  • Computer Vision (CV)271
  • Natural Language Processing343
  • Generative AI for Video267
  • TensorFlow Framework141
  • Machine Learning (ML)1550
  • PyTorch Framework102
  • Keras API53
  • Convolutional Neural Nets36
  • Artificial Intelligence Basics1796

Coupon history

Comidoc has tracked 12 coupons for this course since 2025, last checked 29d ago. On average, a new coupon appears roughly every 29 days.

Coupon codeDiscountAddedStatusLifetime
E01A12C4A7716AA7A967100% offAug 22, 202601:44 PM UTCFully redeemed1h 43m
99503F6728F79AAF8D8A100% offAug 10, 202605:20 PM UTCExpired4d 23h
5C2F5B01E3C3C1705A95100% offJul 30, 202604:28 PM UTCExpired30d 23h
22BE89078D8EDAA705A6100% offJun 14, 202611:21 PM UTCExpired~31 daysRan full term
0ADC19671EB9E41B3D70100% offApr 6, 202601:17 PM UTCExpired~31 daysRan full term
09592E169A955895A799100% offMar 19, 202605:12 PM UTCExpired~31 daysRan full term
Comidoc Analysis

Neural architecture survey spanning CNNs, RNNs, and Generative AI

Strengths

Diverse Architecture Coverage

The curriculum spans multiple neural network types including CNNs, RNNs, LSTMs, Transformers, and GANs, as well as object detection models like YOLO and ResNet.

Editorial course preview

What the public course preview actually shows

These 2 complementary views highlight concrete, legible examples from the course presentation.

Preview 1 of 2

This visual overview presents a structured grid of key neural network architectures, including DNN, CNN, GPT, GAN, YOLO, RCNN, and LSTM, each accompanied by its full name.

Preview 2 of 2

This educational slide defines deep learning as a type of machine learning using artificial neural networks inspired by the human brain, accompanied by a structural diagram.

Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.

Conceptual Reinforcement

Instructional elements assist in distinguishing model characteristics and understanding essential training processes like regularization and optimizers.

Limitations

Limited Instructional Duration

The total duration of approximately 86 minutes suggests a high-level survey rather than an exhaustive technical deep dive.

Best suited to

  • Students exploring Artificial Intelligence
  • Developers seeking a high-level overview of modern architectures

Less suited to

  • Learners requiring deep technical specialization
  • Those seeking long-sform, intensive project work

Comidoc Score

6.7/10

Worth considering
Defined audience

Comidoc verdict

Instruction moves through a wide variety of neural network architectures, ranging from traditional ANN and CNN models to modern Transformers and Generative Adversarial Networks. The inclusion of object detection modules like YOLO provides a practical look at computer vision applications.

The breadth of topics is balanced by the brevity of the instruction. Essential concepts such as prompt engineering and generative workflows are covered within a relatively short total time investment for the number of distinct architectures presented. This makes the path an introductory survey rather than a specialized training program.

This profile suits students or developers looking for a broad conceptual map of the current AI landscape.

Score breakdown

Curriculum depth
8.0

The curriculum covers a wide array of distinct model types including CNNs, RNNs, Transformers, and GANs.

Applied learning
6.5

Includes specific project-led components such as an LLM project and a final Generative AI project.

Clarity & experience
5.8

One signal suggests the content helps in distinguishing between various model characteristics.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
6.5

The curriculum aligns with the stated goals of exploring AI and modern architectures for students and developers.

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