What You’ll Discover in Modern Deep Learning in Python
Modern Deep Learning in Python
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This course continues my first course. Deep Learning in PythonLeft off. You already know the basics of building an artificial neural net in PythonYou now have a plug-and play script that TensorFlow can use. Machine learning is dominated by neural networks, which are a staple of machine learning. They are always a top choice. in Kaggle contests. This is the course you should take if you want to learn deep learning and neural networks.
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While you may have already been familiar with backpropagation, there were still many questions. What can you do to speed up your training? You will learn about stochastic and batch gradient descent. They are both commonly used techniques that allow for you to train using a small amount of data each iteration. This greatly speeds up training time.
You’ll also learn about momentum. It can help you to get through local minima, and it will prevent you from being too conservative in your learning rate. Learn about adaptive learning rates techniques like RMSprop and Adam, which can speed up your training.
You already know the basics of neural networks so we’ll talk about modern techniques such as dropout regularization or batch normalization which you can implement. in Both TensorFlow as well as Theano. The course is continually being updated. New regularization techniques and methods are constantly added. in In the near future
In my previous course, I gave you a glimpse at TensorFlow. We will begin with the basics, so that you can understand everything. For example, what are TensorFlow variables or expressions? And how do you use these building blocks for creating a neural net? Theano is a library that has been around for a while and is highly popular for deep learning. We will also be looking at the fundamental building blocks of neural networks, such as variables, expressions and functions, with this library. in Theano with conviction
Theano was the predecessor of all modern deep learning libraries. Today we have almost too many options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. We cover them all in this course! Choose the one that you love most.
TensorFlow and Theano have the advantage of using the GPU to speed training. I’ll show you how to create a GPU-instance in AWS and then compare the CPU and GPU speeds for deep neural network training.
We will now examine a dataset, the MNIST dataset (images handwritten digits), and compare it to other benchmarks. This is the dataset that researchers first look at when they ask the question. “does this thing work?”
These images are an integral part of deep learning history. They can still be used for testing today. They are essential for deep learning experts.
This course is about “how to build and understand”It’s not only about “how to use”. An API can be used by anyone. in 15 minutes after reading some documentation. It’s not all about. “remembering facts”It’s all about “seeing for yourself” via experimentation. It will show you how to visualize what’s going on in The model internally. This course will give you a deeper understanding of machine learning models.
“If you can’t implement it, you don’t understand it”
Or, as Richard Feynman, the great physicist, said: “What I cannot create, I do not understand”.
My courses are the only ones where you can learn how to create machine learning algorithms completely from scratch.
Some courses will also teach you how plugging works in Your data is already in a library. Do you really need to know 3 lines code?
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After repeating the process with 10 datasets you realize that you didn’t learn all of them. It was one thing that you learned, but you only repeated the same three lines of code 10 more times.
Recommended Prerequisites
Know about gradient descent
Probability and statistics
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: loading CSV files, matrix and vector operations
Numpy teaches you how to build a neural network.
WHAT ORDER SHOULD YOU TAKE YOUR COURSES?:
You can check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in The FAQ for any of my courses (including the free Numpy course).
Who is this course for?
Professionals and students who wish to improve their machine learning skills
Deep learning is a topic that data scientists are interested in.
Data scientists who are familiar with backpropagation, gradient descent, and would like to improve their understanding using stochastic batch training and momentum.
If you don’t yet have any knowledge about softmax and backpropagation, I recommend that you take my deep learning course. in PythonFirst
 Here’s what you can expect in Modern Deep Learning in Python
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