The Complete Neural Networks Bootcamp: Theory, Applications

Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!

4.51 (2682 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand How Neural Networks Work (Theory and Applications)

Understand How Convolutional Networks Work (Theory and Applications)

Understand How Recurrent Networks and LSTMs work (Theory and Applications)

Learn how to use PyTorch in depth

Understand how the Backpropagation algorithm works

Understand Loss Functions in Neural Networks

Understand Weight Initialization and Regularization Techniques

Code-up a Neural Network from Scratch using Numpy

Apply Transfer Learning to CNNs

CNN Visualization

Learn the CNN Architectures that are widely used nowadays

Understand Residual Networks in Depth

Understand YOLO Object Detection in Depth

Visualize the Learning Process of Neural Networks

Learn how to Save and Load trained models

Learn Sequence Modeling with Attention Mechanisms

Build a Chatbot with Attention

Transformers

Build a Chatbot with Transformers

BERT

Build an Image Captioning Model

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Our Verdict

The Complete Neural Networks Bootcamp offers a wealth of information on deep learning topics while balancing theoretical concepts and practical applications. The instructor's ability to explain complex ideas sets this course apart, making it an excellent choice for AI professionals looking to expand their knowledge. However, expect varying audio quality and inconsistent explanations that may require additional resources to fully grasp every topic.

What We Liked

Instructor excels at explaining complex topics from scratch with examples and clear narratives
Thorough coverage of neural networks, convolutional networks, recurrent networks, PyTorch, backpropagation, loss functions, and regularization techniques
Updated regularly to include the latest state-of-the-art models like Transformers and BERT
Comprehensive content for AI professionals seeking in-depth knowledge of deep learning topics

Potential Drawbacks

Initial videos have lower audio quality, with instructors sometimes sounding unprepared or using excessive jargon
Lacks clear structure and explanation of some concepts, such as loss function definition for multiple samples
Course can be too theoretical at times and may require additional introductory material before starting
Audio quality varies significantly, with inconsistent volume levels and occasional echo