Deep Learning | Tensor Flow | RBM | Auto Encoders | GAN
Master Basic and Advanced Concepts | Learn Boltzmann Machines, Auto Encoders and Adversarial Networks
Ratings 4.82 / 5.00
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
Description
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!
Who Should Attend!
- Machine Learning Enthusiasts
- Students
- Machine Learning Engineers
