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You're reading from Deep Learning - Crash Course 2023 Go from beginner to expert with building intuition on single neuron till deep neural networks in Keras and TensorFlow

Product type Video

Published in Apr 2023

Publisher Packt

ISBN-13 9781837632558

Length 9hrs 11mins

Edition 1st Edition

Languages

Tools

Anaconda

Concepts

Deep Learning

Author (1):

Manifold AI Learning

View More author details

Table of Contents (17) Chapters

1. Welcome on Board

Welcome
Course Introduction

2. Getting the Basics Right FREE CHAPTER

Artificial Neural Networks
Activation Function
Bias
Data
Applications of Data
Models
Loss Functions
Learning Algorithms and Model Performance

3. Python Crash Course on Basics

Installing Jupyter Notebook
Accessing Google Colab
Python Basics - Data Types
Python Basics - Containers in Python
Control Statements Python ifâ€¦else
Python Control statements - While and For
Functions and Classes in Python

4. Python for Data Science - Crash Course

NumPy Part 1
NumPy Part 2
NumPy Part 3
Pandas in Python - Pandas Series
Pandas Data Frame
Cleaning and Examining the data
Plotting with Matplotlib
Contour Plots

5. MP Neuron Model

MP Neuron Introduction
Intuition of Data
Loss and Finding Parameters
Mathematical Intuition

6. MP Neuron in Python

MP Neuron - Data Import
Train Test Split
Modify Data
MP Neuron in Python
MP Neuron Class

7. Summary of MP Neuron

Summary

8. Perceptron

Perceptron
Perceptron Model and Its Representation
Loss Function and Parameter Update
Why Update Rule Works
Update Rule in Programs

9. Perceptron in Python

Perceptron in Python
Visualize the Accuracy with Epochs

10. Sigmoid Neuron

Perceptron Limitations
Sigmoid Neuron Introduction
Sigmoid Neuron Data
Sigmoid Intuition
Manual Fitting of Data
Gradient Descent
Program Overview
Program in Python

11. Sigmoid Neuron Implement with Python

Download Dataset
Data Standardization - 1
Data Standardization - 2
Class Sigmoid

12. Basic Probability

Introduction to Probability and Random Variables
Why Random Variable Is Important
Random Variable - Types
Probability Distribution Table
Why Do We Require Entropy Loss

13. Deep Neural Networks

Why Deep Neural Networks
Linear Separation of Data

14. Universal Approximation Theorem

Understanding Universal Approximation Theorem
Confirming Universal Approximation Theorem Works
Going Deep into Neural Networks
Challenges in Creating Deep Neural Networks from Scratch

15. Deep Learning with TensorFlow 2.x

Quick Recap on Deep Learning
Introducing TensorFlow
Building a Neural Network with TensorFlow
Create First Neural Network with TensorFlow
Training the Neural Network
Training Evaluation
Summary

16. Activation Functions in Deep Learning Neural Networks

Activation Functions in Deep Learning Neural Networks - Introduction
Various Activation Functions
Summary on Activation Functions
Common Network Configuration

17. Applying Deep Learning

Moving from Shallow Learning to Deep Learning
Keras Basics
Types of Problems
ReLU, SoftMax, and Cross Entropy
Implementing Multi-Class Classification Using Keras
Regression Problem
TensorFlow Advanced Tricks - Ways to Create Neural Networks
TensorFlow - Subclassing Methods

You're reading from Deep Learning - Crash Course 2023 Go from beginner to expert with building intuition on single neuron till deep neural networks in Keras and TensorFlow

Table of Contents (17) Chapters

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You're reading from Deep Learning - Crash Course 2023 Go from beginner to expert with building intuition on single neuron till deep neural networks in Keras and TensorFlow

Table of Contents (17) Chapters

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