Data Science: Modern Deep Learning in Python

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

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

What Will I 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

Requirements

• Be comfortable with Python, Numpy, and Matplotlib
• If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, Deep Learning in Python, and then return to this course.

Target audience

• Students and professionals who want to deepen their machine learning knowledge
• Data scientists who want to learn more about deep learning
• Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
• Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first