Data Science: Deep Learning and Neural Networks in Python
The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
What you’ll 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
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
Who this course is for:
- 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.
