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Practical Deep Learning with Keras and Python
Introduction
About the Instructor (1:37)
Dive into Machine Learning (13:10)
Making Predictions (7:01)
A Bit of Theory
Machine Learning Pipeline (9:13)
Regression (13:01)
Binary and Multi-class Classification (14:29)
Recap and a Link to More Theory (2:43)
Installation and Setup
Environment Setup for Windows (and some issues with it) (6:55)
Environment Setup for Mac and Linux (3:41)
Getting Started with Keras
Data Preparation (10:10)
Training and Testing (10:32)
Real World Case Study: Predicting Protein Functions
Problem Description and Data View (8:32)
Pre-processing the Data (15:51)
Loading Data and Getting the Shapes Right (7:45)
Train, Test Split (3:11)
Shapes in Depth (or how avoid headaches) (4:32)
Sequential Model (8:58)
Functional API (5:25)
Convolutional Neural Networks (CNN)
Basics and Rationale (10:13)
CNN in Keras (or why Keras is better than your ML tool) (8:30)
Pooling (and why it's not that important) (4:25)
Dropout (and why you should always consider it) (3:51)
Graph Based Models
Functional API for CNN (4:27)
Inception Module (9:36)
Residual Connections (5:08)
Finishing Touches
Saving and Loading Model Weights (6:30)
Parting Words (3:55)
Source Code
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Recap and a Link to More Theory
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