Deep Learning | Tensor Flow | RBM | Auto Encoders | GAN
Deep Learning | Tensor Flow | RBM | Auto Encoders | GAN, available at $49.99, has an average rating of 4.28, with 18 lectures, based on 9 reviews, and has 1052 subscribers.
You will learn about Why we need neural networks? What is a tensor in tensorflow? Math behind neural networks Artificial Neural Network Convolutional Neural Network Recurrent Neural Network Long Short Term Memory This course is ideal for individuals who are Machine Learning Enthusiasts or Students or Machine Learning Engineers It is particularly useful for Machine Learning Enthusiasts or Students or Machine Learning Engineers.
Enroll now: Deep Learning | Tensor Flow | RBM | Auto Encoders | GAN
Summary
Title: Deep Learning | Tensor Flow | RBM | Auto Encoders | GAN
Price: $49.99
Average Rating: 4.28
Number of Lectures: 18
Number of Published Lectures: 18
Number of Curriculum Items: 18
Number of Published Curriculum Objects: 18
Original Price: $59.99
Quality Status: approved
Status: Live
What You Will Learn
- Why we need neural networks?
- What is a tensor in tensorflow?
- Math behind neural networks
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Long Short Term Memory
Who Should Attend
- Machine Learning Enthusiasts
- Students
- Machine Learning Engineers
Target Audiences
- Machine Learning Enthusiasts
- Students
- Machine Learning Engineers
Deep learning is a rapidly growing field of artificial intelligence that has revolutionized the way we approach and solve complex problems. In this course, we will dive into the fundamentals of deep learning, covering the most important concepts and techniques used in the field.
We will focus on the three major types of deep neural networks
Artificial Neural Networks (ANNs),
Convolutional Neural Networks (CNNs), and
Recurrent Neural Networks (RNNs) and
the three unsupervised deep learning networks like
Boltzmann Machines,
Auto Encoders and
Adversarial Networks.
Using TensorFlow, one of the most popular and widely used deep learning libraries, we will explore the architecture and functioning of each type of network, and learn how to build, train, and evaluate them. From recognizing objects in images to processing sequences of data with higher accuracy, deep learning is finding applications across multiple areas in real-world.
The program covers both concepts as well as coding related to the neural networks.
By the end of this course, you will have a solid understanding of deep learning, and be able to apply these techniques to your own projects. Whether you are a beginner to the field of AI or a seasoned practitioner, this course will equip you with the tools and knowledge you need to advance your skills in deep learning. So, let’s get started on our journey to mastering deep learning!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Tensor Intro
Lecture 1: Tensor Intro
Lecture 2: Tensor Computations
Chapter 3: Understanding Deep Learning
Lecture 1: Understanding Deep Learning in Simple Terms
Lecture 2: Activation Function
Lecture 3: Convex Optimization
Lecture 4: ANN
Chapter 4: Convolution
Lecture 1: Understanding Convolution in CNN
Lecture 2: Deploying a CNN Model
Chapter 5: RNN
Lecture 1: Why RNNs?
Lecture 2: Math Behind RNN
Lecture 3: LSTM
Lecture 4: Spam Detection – RNN & LSTM
Chapter 6: ANN Vs CNN Vs RNN
Lecture 1: ANN Vs CNN Vs RNN
Chapter 7: Unsupervised (Deep) Learning
Lecture 1: Generative Adversarial Network
Lecture 2: Restricted Boltzmann Machines and Deep Belief Networks
Lecture 3: Auto Encoder
Lecture 4: Building The Models
Instructors
-
SeaportAi .
Artificial Intelligence and Business Transformation Experts
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 1 votes
- 5 stars: 6 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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