Deep Learning Fundamentals 2022

Theory and Python
What you'll learn
Basics of Deep Learning
Artificial Neural Network
Artificial Neural Network with Keras, Python
Regression and Classification with Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
Requirements
None
Description
Welcome to Deep Learning Fundamentals.This course covers the basic theory and Python practice of artificial neural networks. This course is designed for beginners who are interested in deep learning. Having knowledge of undergraduate level mathematics is preferable, but not a must.Artificial intelligence is a technology that makes machines imitate intelligent human behavior and human cognitive functions. Machine learning is a branch of artificial intelligence. It enables systems to learn from data automatically, that is, learn without being explicitly programmed. Deep Learning is a type of machine learning. It uses artificial neural networks to solve complex problems.One reason why deep learning has drawn much attention is that it overcomes the limitations of traditional machine learning. The first limitation is that traditional machine learning cannot handle high dimensional data. Thus, the performance of the traditional machine learning model tends to level off as the data amount increases. The second is that, when we use traditional machine learning techniques, we need to extract features manually. Therefore, when we analyze image data or movie data, traditional machine learning techniques are not suitable because such data contains a great number of features.Deep learning can overcome these limitations of traditional machine learning. An artificial neural network is one of the algorithms of artificial intelligence, and usually, it takes a form of a deep learning model. It simulates the network neurons that make up the human brain. The structure of an artificial neural network enables a deep learning model to solve complex problems that traditional machine learning algorithms can hardly handle.This course has some Python tutorials for developing deep learning models. And this course uses a library named Keras, which enables us to develop deep learning models efficiently. Basic-level Python knowledge is preferable, but Python beginners are also welcome.This course consists of three modules.1. Artificial Neural Networks2. Convolutional Neural Networks3. Recurrent Neural Networks.The first module is the basic of artificial neural network.The second module covers convolutional neural network that is a type of network effective for handling image and movie data.The third module covers recurrent neural network that is effective for time-series analysis and analyzing text data.After completing this course, you will have a fundamental knowledge of deep learning.I'm looking forward to seeing you in this course!
Overview
Section 1: Introduction
Lecture 1 Course Introduction
Lecture 2 Let's Get Started with Python!
Section 2: 1. Artificial Neural Network (Part 1) -Deep Learning Fundamentals
Lecture 3 What is Deep Learning?
Lecture 4 Artificial Neural Network
Lecture 5 Perceptron
Lecture 6 Logic Circuit
Lecture 7 Logic Gate with Python
Lecture 8 Multilayer Perceptron
Lecture 9 Multilayer Perceptron with Python
Section 3: 1. Artificial Neural Network (Part 2) -Basics of Artificial Neural Network
Lecture 10 Neural Network
Lecture 11 Activation Function
Lecture 12 Loss Function
Lecture 13 Training Neural Network
Lecture 14 Gradient Descent Method (Part 1)
Lecture 15 Gradient Descent Method (Part 2)
Lecture 16 Chain Rule
Lecture 17 Backpropagation
Lecture 18 Vanishing Gradient Problem
Lecture 19 Nonsaturating Activation Functions
Lecture 20 Parameter Initialization
Lecture 21 ANN Regression with Keras
Lecture 22 ANN Classification with Keras
Section 4: 1. Artificial Neural Network (Part 3) -Optimization & Regularization Techniques
Lecture 23 Overfitting
Lecture 24 L1 & L2 Regularization
Lecture 25 Dropout
Lecture 26 Regularization with Keras
Lecture 27 Optimizer
Lecture 28 Batch Normalization
Lecture 29 Optimization & Batch Normalization with Keras
Lecture 30 Thank You!
Section 5: 2. Convolutional Neural Network (Part 1) -CNN Basics
Lecture 31 Computer Vision
Lecture 32 Image Data
Lecture 33 What is CNN?
Lecture 34 Convolutional Layer
Lecture 35 Padding
Lecture 36 Pooling
Lecture 37 Fully-Connected Layer
Lecture 38 CNN Training Overview
Lecture 39 Image Data Augmentation
Lecture 40 Binary Image Classification with Keras
Lecture 41 Autoencoder
Section 6: 2. Convolutional Neural Network (Part 2) -Pre-Trained Model
Lecture 42 LeNet
Lecture 43 AlexNet
Lecture 44 Multiclass Classification with LeNet & AlexNet
Lecture 45 VGGNet
Lecture 46 GoogLeNet
Lecture 47 ResNet
Lecture 48 Transfer Learning
Lecture 49 Binary Classification with Transfer Learning
Section 7: 3. Recurrent Neural Network
Lecture 50 What is RNN?
Lecture 51 Structure of RNN
Lecture 52 Variable-Length Input
Lecture 53 Weight & Bias
Lecture 54 Types of RNN
Lecture 55 BPTT
Lecture 56 LSTM
Lecture 57 How LSTM work?
Lecture 58 BPTT in LSTM
Lecture 59 GRU
Lecture 60 RNN, LSTM, and GRU with Keras
Anyone who wants to start studying deep learning
Homepage

https://www.udemy.com/course/deep-learning-artificial-neural-network/

Deep Learning Fundamentals 2022 Fast Download
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