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| File Name: | Python For Deep Learning: Build Neural Networks In Python |
| Content Source: | https://www.udemy.com/course/deep-learning-basics-with-python/ |
| Genre / Category: | Other Tutorials |
| File Size : | 785MB |
| Publisher: | udemy |
| Updated and Published: | July 04, 2022 |
What you’ll learn
Learn the fundamentals of the Deep Learning theory
Learn how to use Deep Learning in Python
Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence
Make predictions using linear regression, polynomial regression, and multivariate regression
Build artificial neural networks with Tensorflow and Keras
Requirements
Experience with the basics of coding in Python
Basic mathematical skills
Readiness, flexibility, and passion for learning
Description
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it’s no secret that Python’s best application is in deep learning and artificial intelligence tasks.While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.There are hundreds of machine learning resources available on the internet. However, you’re at risk of learning unnecessary lessons if you don’t filter what you learn. While creating this course, we’ve helped with filtering to isolate the essential basics you’ll need in your deep learning journey.It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.
Overview
Section 1: Introduction to Deep Learning
Lecture 1 What is a Deep Learning ?
Lecture 2 Course Materials
Lecture 3 Why is Deep Learning Important?
Lecture 4 Software and Frameworks
Section 2: Artificial Neural Networks (ANN)
Lecture 5 Introduction
Lecture 6 Anatomy and function of neurons
Lecture 7 An introduction to the neural network
Lecture 8 Architecture of a neural network
Section 3: Propagation of information in ANNs
Lecture 9 Feed-forward and Back Propagation Networks
Lecture 10 Backpropagation In Neural Networks
Lecture 11 Minimizing the cost function using backpropagation
Section 4: Neural Network Architectures
Lecture 12 Single layer perceptron (SLP) model
Lecture 13 Radial Basis Network (RBN)
Lecture 14 Multi-layer perceptron (MLP) Neural Network
Lecture 15 Recurrent neural network (RNN)
Lecture 16 Long Short-Term Memory (LSTM) networks
Lecture 17 Hopfield neural network
Lecture 18 Boltzmann Machine Neural Network
Section 5: Activation Functions
Lecture 19 What is the Activation Function?
Lecture 20 Important Terminologies
Lecture 21 The sigmoid function
Lecture 22 Hyperbolic tangent function
Lecture 23 Softmax function
Lecture 24 Rectified Linear Unit (ReLU) function
Lecture 25 Leaky Rectified Linear Unit function
Section 6: Gradient Descent Algorithm
Lecture 26 What is Gradient Decent?
Lecture 27 What is Stochastic Gradient Decent?
Lecture 28 Gradient Decent vs Stochastic Gradient Decent
Section 7: Summary Overview of Neural Networks
Lecture 29 How artificial neural networks work?
Lecture 30 Advantages of Neural Networks
Lecture 31 Disadvantages of Neural Networks
Lecture 32 Applications of Neural Networks
Section 8: Implementation of ANN in Python
Lecture 33 Introduction
Lecture 34 Exploring the dataset
Lecture 35 Problem Statement
Lecture 36 Data Pre-processing
Lecture 37 Loading the dataset
Lecture 38 Splitting the dataset into independent and dependent variables
Lecture 39 Label encoding using scikit-learn
Lecture 40 One-hot encoding using scikit-learn
Lecture 41 Training and Test Sets: Splitting Data
Lecture 42 Feature scaling
Lecture 43 Building the Artificial Neural Network
Lecture 44 Adding the input layer and the first hidden layer
Lecture 45 Adding the next hidden layer
Lecture 46 Adding the output layer
Lecture 47 Compiling the artificial neural network
Lecture 48 Fitting the ANN model to the training set
Lecture 49 Predicting the test set results
Section 9: Convolutional Neural Networks (CNN)
Lecture 50 Introduction
Lecture 51 Components of convolutional neural networks
Lecture 52 Convolution Layer
Lecture 53 Pooling Layer
Lecture 54 Fully connected Layer
Section 10: Implementation of CNN in Python
Lecture 55 Dataset
Lecture 56 Importing libraries
Lecture 57 Building the CNN model
Lecture 58 Accuracy of the model
Programmers who are looking to add deep learning to their skillset,Professional mathematicians willing to learn how to analyze data programmatically,Any Python programming enthusiast willing to add deep learning proficiency to their portfolio
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