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

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

What Will I 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

Requirements

• Basic math (calculus derivatives, matrix arithmetic, probability)
• Install Numpy and Python
• Don't worry about installing TensorFlow, we will do that in the lectures.
• Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Target audience

• Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
• Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.