If you're looking to expand your deep learning skills, the Data Science: Modern Deep Learning in Python course is just the thing for you. This course is a continuation from the previous course offered, Deep Learning in Python. In this course, you will have the opportunity to learn about the modern libraries used in the field of deep learning, including Theano, Keras, PyTorch, CNTK, MXNet and Tensorflow.
The Focus
The course is more about teaching you how to understand the fundamentals, which helps you in implementing these techniques from scratch. It's not just about coding but about understanding the importance of each line of code and what they mean.
The course emphasizes on building artificial neural networks as they are a staple in the industry, and are the go-to technique for any machine learning problem, whether it's recognition, classification, or regression. The course structure is designed to help you learn the fundamentals while giving you a real-world look and practical knowledge helping you to build your skillset.
Best features
The course is best known for its approach in creating familiarity and real understanding of the foundations of Deep Learning, and unlike other courses, you will get a chance to see real-life projects and examples of how to implement these neural networks in Python. The course boasts a rating aggregate of 4.61668 and over 3000 reviews from students who found the course very helpful.
You must have some prerequisites before starting this course, such as previous knowledge and experience with gradient descent, probability and statistics, Python coding: if/else, loops, lists, dicts, sets, Numpy coding/matrix and vector operations and how to write a neural network with Numpy. Once you have these prerequisites, you will have a good time learning from this course.
Course Structure
The course starts by giving you a brief introduction and connects right away to the previous course. This is necessary to get you back into the framework of things if a break has been taken between the courses. Once you're settled, the course moves to the architecture of the neural network, and teaches you how you can enhance the models, project architecture, and its implementation.
The course proceeds to teach the two commonly used techniques; batch and stochastic gradient descent. These two techniques are widely used across the industry because they permit training on a small sample of data, greatly reducing training time.
Other optimization techniques including momentum, adaptive learning rate techniques like AdaGrad, RMSprop, and Adam are also included, which can also help speed up training.
Deep Learning Libraries and GPUs
Theano was the first-ever deep learning library and is an excellent introduction to the basics of deep learning. The course also delves into other modern libraries like Tensorflow, Keras, CNTK(Microsoft) and MXNet(Amazon/Apache). The best part is learning the techniques through various libraries that you can choose from; whichever library you are comfortable with.
The course sets up a GPU-instance on AWS and offers a comprehensive comparison between CPU vs GPU for training a deep neural network. As most of the operations in deep learning are matrix-based, accelerated computations on the GPU make training much faster. This module provides you with the opportunity to learn about the software-hardware interaction and how to use them in conjunction to achieve optimized performance.
After setting up powerful hardware, the course moves to the point where we can finally look at real datasets. The famous MNIST dataset, which contains images of handwritten digits is covered. This dataset was the go-to for classification in the early days of deep learning and, upto today, is used by researchers around the world to test their models.
Who should take this course?
The course is suitable for those who already have some experience with deep learning and are familiar with artificial neural networks. It is an exciting course to learn practical knowledge that allows you to implement these neural networks in Python.
Completing the course means you will be awarded a certificate of completion which you can show to employers to demonstrate your skill level. Advances in deep learning have created many opportunities in the job market, and learning this course could help you upskill and seize new opportunities.
Verdict
The course is an excellent investment for anyone who wants to grow their deep learning skills. It is more about building fundamental knowledge, taking a hands-on approach, and understanding the concepts instead of just memorizing them. The modern libraries taught in the course will help you with any project you choose to do in the future.
The instructor in the course is a well-known name in the field of machine learning, and he is an excellent teacher. He is a great communicator and makes the course very easy to follow. The course will teach you a step-by-step process of building artificial neural networks and, most importantly, how to understand them.
The best part about this course is that it's constantly being updated, and it will always keep you in the loop. So if you're looking to upskill yourself, then we highly recommend you take this course for an extensive, hands-on learning experience.
