{"id":3422,"date":"2026-08-10T09:04:49","date_gmt":"2026-08-10T09:04:49","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-data-science-modern-deep-learning-in-python-2025-11\/"},"modified":"2026-08-10T09:04:49","modified_gmt":"2026-08-10T09:04:49","slug":"udemy-data-science-modern-deep-learning-in-python-2025-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-data-science-modern-deep-learning-in-python-2025-11\/","title":{"rendered":"Udemy \u2013 Data Science: Modern Deep Learning in Python 2025-11"},"content":{"rendered":"
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Descriptions<\/h2>\n

Data Science: Modern Deep Learning in Python, This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time. You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate.<\/p>\n

You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training. Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future. In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what\u2019s going on \u2013 what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that\u2019s been around much longer and is very popular for deep learning \u2013 Theano. With this library we will also examine the basic building blocks \u2013 variables, expressions, and functions \u2013 so that you can build neural networks in Theano with confidence.<\/p>\n

What you\u2019ll learn<\/h3>\n