Data Science: Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

4.62 (3627 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Apply momentum to backpropagation to train neural networks

Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks

Understand the basic building blocks of TensorFlow

Build a neural network in TensorFlow

Write a neural network using Keras

Write a neural network using PyTorch

Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent

Understand and implement dropout regularization

Understand and implement batch normalization

Understand the basic building blocks of Theano

Build a neural network in Theano

Write a neural network using CNTK

Write a neural network using MXNet

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

Course Gallery

Data Science: Modern Deep Learning in Python – Screenshot 1 — Screenshot 1 – Data Science: Modern Deep Learning in Python

Data Science: Modern Deep Learning in Python – Screenshot 2 — Screenshot 2 – Data Science: Modern Deep Learning in Python

Data Science: Modern Deep Learning in Python – Screenshot 3 — Screenshot 3 – Data Science: Modern Deep Learning in Python

Data Science: Modern Deep Learning in Python – Screenshot 4 — Screenshot 4 – Data Science: Modern Deep Learning in Python

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Rating & Reviews

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Comidoc Review

Our Verdict

The Data Science: Modern Deep Learning in Python course offers a comprehensive overview of modern deep learning techniques and libraries, with an emphasis on hands-on practice through in-depth explanations and practical examples. While setup processes can initially pose challenges, learners will find the course rewarding as they develop their understanding of key concepts and delve into contemporary AI applications.

What We Liked

Holistic approach to deep learning, covering modern libraries like TensorFlow, Theano, Keras, PyTorch, CNTK, and MXNet
In-depth explanations of key concepts such as momentum, adaptive learning rate procedures, dropout regularization, and batch normalization
Practical examples and assignments that encourage learners to derive formulas and write their own code
Covers foundational knowledge for popular AI models like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Potential Drawbacks

Some users may find the initial setup process slightly frustrating due to reliance on Linux and limited guidance in code setup
Explanations for some algorithms could be more detailed, with clearer connections between formulas and their implementations
Outdated programming examples using Theano and TensorFlow 1.x; users would benefit from updated content using the latest libraries and APIs