Data Science: Deep Learning and Neural Networks in Python

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

4.60 (10106 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

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

Course Gallery

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Our Verdict

This Udemy course by the Lazy Programmer offers a detailed exploration of deep learning concepts, delving into neural network theory and practical implementations. The syllabus is designed to build students' understanding from fundamental principles through a mix of theoretical instruction, coding exercises, and TensorFlow applications. However, those who are completely new to the subject matter may struggle with the pacing and depth initially, as some elements require background knowledge or further external resources. Despite these minor drawbacks, this course remains a valuable resource for anyone seeking to develop their deep learning expertise using Python, offering detailed tutorials and up-to-date content on this rapidly evolving field.

What We Liked

In-depth look at neural network theory with both pure Python and Tensorflow code
Covers derivation of backpropagation rule from first principles
Promotes understanding by having students implement a neural network from scratch in Python and numpy
Codes a neural network using Google's TensorFlow

Potential Drawbacks

Equations lack clear explanation of variables and their derivation, which might be challenging for learners new to the topic
Instructions can sometimes appear brusque or overly critical, potentially demotivating some students
Some prerequisites could benefit from brief revisiting within the course for those who may be rusty or less experienced
Occasional repetition in high-level discussions and use of certain sections across different courses