Python Deep Learning Online Course
Python Deep Learning Online Course
This is a comprehensive course which will guide you how to use the power of Python to train your machine such that your machine starts learning just like humans, and based on that learning, your machine starts making predictions as well. You will also learn various steps of data preprocessing, which allows us to make data ready for machine learning algorithms.
By the end of this course, you will be able to understand the methodology of DNNs with deep learning using real-world datasets.
Who is this course for?
This course is designed for anyone who is interested in data science or interested in taking their career to a higher level. Students who want to build a career in Data Science and related filed.
Note: You need to have a background in deep learning to get the best out of this course.
What you will learn
- Learn the basics of machine learning and neural networks
- Understand the architecture of neural networks
- Learn the basics of DNN
- Learn how to implement a complete DNN using NumPy
- Learn to create a complete structure for DNN from scratch using Python
- Work on a projects
Course Curriculum
Introduction
- Course Promo
- Introduction to Instructor
- Introduction to Course
Basics of Deep Learning
- Problem to Solve Part 1
- Problem to Solve Part 2
- Problem to Solve Part 3
- Linear Equation
- Linear Equation Vectorized
- 3D Feature Space
- N-Dimensional Space
- Theory of Perceptron
- Implementing Basic Perceptron
- Logical Gates for Perceptrons
- Perceptron Training Part 1
- Perceptron Training Part 2
- Learning Rate
- Perceptron Training Part 3
- Perceptron Algorithm
- Coding Perceptron Algo (Data Reading and Visualization)
- Coding Perceptron Algo (Perceptron Step)
- Coding Perceptron Algo (Training Perceptron)
- Coding Perceptron Algo (Visualizing the Results)
- Problem with Linear Solutions
- Solution to Problem
- Error Functions
- Discrete Versus Continuous Error Function
- Sigmoid Function
- Multi-Class Problem
- Problem of Negative Scores
- Need of SoftMax
- Coding SoftMax
- One-Hot Encoding
- Maximum Likelihood Part 1
- Maximum Likelihood Part 2
- Cross Entropy
- Cross Entropy Formulation
- Multi-Class Cross Entropy
- Cross Entropy Implementation
- Sigmoid Function Implementation
- Output Function Implementation
Deep Learning
- Introduction to Gradient Descent
- Convex Functions
- Use of Derivatives
- How Gradient Descent Works
- Gradient Step
- Logistic Regression Algorithm
- Data Visualization and Reading
- Updating Weights in Python
- Implementing Logistic Regression
- Visualization and Results
- Gradient Descent Versus Perceptron
- Linear to Non-Linear Boundaries
- Combining Probabilities
- Weighted Sums
- Neural Network Architecture
- Layers and DEEP Networks
- Multi-Class Classification
- Basics of Feed Forward
- Feed Forward for DEEP Net
- Deep Learning Algo Overview
- Basics of Backpropagation
- Updating Weights
- Chain Rule for Backpropagation
- Sigma Prime
- Data Analysis NN (Neural Networks) Implementation
- One-Hot Encoding (NN Implementation)
- Scaling the Data (NN Implementation)
- Splitting the Data (NN Implementation)
- Helper Functions (NN Implementation)
- Training (NN Implementation)
- Testing (NN Implementation)
Optimizations
- Underfitting vs Overfitting
- Early Stopping
- Quiz
- Solution and Regularization
- L1 and L2 Regularization
- Dropout
- Local Minima Problem
- Random Restart Solution
- Vanishing Gradient Problem
- Other Activation Functions
Final Project
- Final Project Part 1
- Final Project Part 2
- Final Project Part 3
- Final Project Part 4
- Final Project Part 5
