Are you looking for the Best Advanced Deep Learning Courses?… If yes, then this article is for you. In this article, you will find the Best Advanced Deep Learning Courses for beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Advanced Deep learning Courses and find the best Advanced Deep Learning Courses according to your need.
Advanced Deep Learning with TensorFlow 2 Specialization
This TensorFlow 2 For Deep Learning Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow.
The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models.
The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.
The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library.
This specialization consists of 3 Courses:
- Getting Started with TensorFlow 2
- Customizing your Models with TensorFlow 2
- Probabilistic Deep Learning with TensorFlow 2
Other Benefits get from this course:
- Sharable Certificate
- Self-Paced Learning Option, Course Videos and Recordings
- Practice Quizzes
- Assignments with Feedbacks
- , Quizzes with feedback
- Programming Assignments
Course Duration: 4 months | Best for: Intermediate and Advanced | Provider: Imperial College London
Info: Visit this Course and get Best Financial Aid for this course
Deep Learning in Python – DataCamp
In this Deep Learning in Python you’ll expand your deep learning knowledge and take your machine learning skills to the next level. Working with Keras and PyTorch, you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models.
You’ll then use TensorFlow to build linear regression models and neural networks. Throughout the track, you’ll use machine learning techniques to solve real-world challenges, such as predicting housing prices, building a neural network to predict handwritten numbers, and identify forged banknotes.
By the end of the track, you’ll be ready to use Keras to train and test complex, multi-output networks and dive deeper into deep learning.
Course Duration: 20hrs | Total Courses: 5
Info: Visit this Course and get amazing offers and if you are a student then get 65% off
Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning.
First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
Mostly you should have familiarity with programming on a Python development environment.
Skills you will gain from this Course:
- Deep Learning
- Artificial Neural networks
- Machine Learning
- Reinforcement Learning
- Keras
The concepts learnt by you in this course are:
- Introduction to Neural Networks
- Neural Network Optimizers and Keras
- Convolutional Neural networks
- Recurrent Neural networks and Long-Short term Memory networks
- Deep Learning with Autoencoders
- Deep Learning Applications and Reinforcement Learning
Course Duration: 14hrs | Best for: Intermediate and Advanced | Provider: IBM
Info: Visit this course and get amazing offers on this course
Neural Networks and Deep Learning
In the Neural networks and Deep Learning course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture, and apply deep learning to your own applications.
Skills you will gain from this Neural networks and Deep Learning Course:
- Deep learning
- Artificial Neural Networks
- Back Propagation
- Python Programming
- Neural network Architecture
Concepts you will learn from this course are:
- Introduction to Deep Learning
- Neural Networks
- Shallow Neural networks
- Deep Neural networks
Other Benefits get from this course:
- Sharable Certificate
- Self-Paced Learning Option, Course Videos and Recordings
- Practice Quizzes
- Assignments with Feedbacks
- , Quizzes with feedback
- Programming Assignments
Course Duration: 29hrs | Best for: Intermediate and Advanced | instructor: Andrew NG
Info: Visit this Course and get an amazing audit course for free
Generative Adversarial Networks (GANs) Specialization
The Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.
This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.
Skills you will learn from this course:
- Generator
- Image-to-Image Translation
- glossary of computer graphics
- Discriminator
- Generative Adversarial Networks
- Controllable Generation
- WGANs
- Conditional Generation
- Components of GANs
- StyleGANs
In this GANs Specialization you will learn 3 Courses
- Build Basic Generative Adversarial Networks
- Build Better Generative Adversarial networks
- Apply Generative Adversarial networks
Course Duration: 3 months | Best for: Intermediate and Advanced | Provider: DeepLearning.ai
Info: Visit this Course ang get amazing Financial aid on this Course
Deep Learning: Convolutional Neural Networks in Python – Udemy
Rating: 4.6
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
This course will teach you:
- The basics of machine learning and neurons (just a review to get you warmed up!)
- Neural networks for classification and regression (just a review to get you warmed up!)
- How to model image data in code
- How to model text data for NLP (including preprocessing steps for text)
- How to build an CNN using Tensorflow 2
- How to use batch normalization and dropout regularization in Tensorflow 2
- How to do image classification in Tensorflow 2
- How to do data preprocessing for your own custom image dataset
- How to use Embeddings in Tensorflow 2 for NLP
- How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
This Course is best for
- Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
- Software Engineers and Data Scientists who want to level up their career
Prerequisites:
Those who are having skills on Numpy coding: matrix and vector operations, loading a CSV file
Course Duration: 12 hrs | Best for: Professionals
And here the list ends. So, these are the Best Advanced Deep Learning Courses. I will keep adding more Best Advanced Deep Learning Courses to this list.
Conclusion
I hope these Best Advanced Deep Learning Courses will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.