Deep Learning: Convolutional Neural Networks in Python

Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning

4.75 (6301 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand convolution and why it's useful for Deep Learning

Understand and explain the architecture of a convolutional neural network (CNN)

Implement a CNN in TensorFlow 2

Apply CNNs to challenging Image Recognition tasks

Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course Gallery

Deep Learning: Convolutional Neural Networks in Python – Screenshot 1 — Screenshot 1 – Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python – Screenshot 2 — Screenshot 2 – Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python – Screenshot 3 — Screenshot 3 – Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python – Screenshot 4 — Screenshot 4 – Deep Learning: Convolutional Neural Networks in Python

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Comidoc Review

Our Verdict

Deep Learning: Convolutional Neural Networks in Python, with its comprehensive approach to computer vision and natural language processing using CNNs, proves to be an excellent resource for those pursuing a deep understanding of the field. Despite minor shortcomings related to pacing and hyperparameter optimization, the course offers immense value through its in-depth explanations and real-world practical applications. Ideally suited for those with some exposure to computer science or data science concepts.

What We Liked

Covers a wide range of topics from image recognition to natural language processing with CNNs
Thorough explanations of mathematical concepts underpinning neural networks and deep learning
Hands-on coding exercises at every step, enabling better retention and understanding
Excellent coverage of real-world datasets providing valuable practical experience

Potential Drawbacks

Some students might find the initial pace challenging; it caters more towards those with some prior knowledge in computer science or data science domains
Pacing can be inconsistent, occasionally creating a need for external resources to fill gaps
Limited in-depth exploration of hyperparameter selection and network optimization techniques
Feedback suggests some instances of unresolved queries regarding course materials