Deep Learning - Deep Neural Network for Beginners Using Python [Video]
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-
Free ChapterIntroduction
-
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
-
Final Project
About this
video
Are you ready to start your path to becoming a deep learning expert? Then this course is for you.
This course is step-by-step. In every new tutorial, we build on what we have already learned and move one extra step forward, and then we assign you a small task that is solved at the beginning of the next video. We start by teaching the theoretical part of the concept, and then implement everything as it is practically using Python.
This comprehensive course will be your guide to learning 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!
We will be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine learning. Python will be taught from the elementary level up to an advanced level so that any machine learning concept can be implemented.
You will also learn various steps of data preprocessing, which allows us to make data ready for machine learning algorithms.
You will learn the general concepts of machine learning overall, which will be followed by the implementation of one of the essential ML algorithms, “Deep Neural Networks”. Each concept of DNNs will be taught theoretically and will be implemented using Python.
By the end of this course, you will be able to understand the methodology of DNNs with deep learning using real-world datasets.
All the resources for this course are available at: https://github.com/PacktPublishing/Deep-Learning---Deep-Neural-Network-for-Beginners-Using-Python
- Publication date:
- January 2023
- Publisher
- Packt
- Duration
- 6 hours 26 minutes
- ISBN
- 9781837633579