[2025] Tensorflow 2: Deep Learning & Artificial Intelligence

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

4.67 (13412 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)

Predict Stock Returns

Time Series Forecasting

Computer Vision

How to build a Deep Reinforcement Learning Stock Trading Bot

GANs (Generative Adversarial Networks)

Recommender Systems

Image Recognition

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Use Tensorflow Serving to serve your model using a RESTful API

Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices

Use Tensorflow's Distribution Strategies to parallelize learning

Low-level Tensorflow, gradient tape, and how to build your own custom models

Natural Language Processing (NLP) with Deep Learning

Demonstrate Moore's Law using Code

Transfer Learning to create state-of-the-art image classifiers

Earn the Tensorflow Developer Certificate

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

Course Gallery

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Comidoc Review

Our Verdict

This course is an excellent starting point for those interested in deep learning, offering substantial content and hands-on experience. Be prepared for a journey filled with challenges as the curriculum covers complex topics at a gradual pace. While it might take more time investment than you initially thought, the end result—a solid understanding of Tensorflow and Keras—is worth the effort.

What We Liked

Comprehensive curriculum covering various deep learning concepts
Hands-on approach with multiple projects to enhance practical skills
Gradual pace, making complex topics accessible for beginners
Opportunities for portfolio building and gaining in-depth knowledge of Tensorflow and Keras

Potential Drawbacks

Can be quite challenging due to math and coding involved
Requires a significant time commitment (suggested to take multiple months)
Lack of Architectural perspective may lead students to join additional courses