Modern Deep Learning in Python

This course continues from my first course. Deep Learning in PythonLeft off. You already know the basics of building an artificial neural net in PythonFile Size: 1.44 GB

Modern Deep Learning in Python

This course continues from my first course. Deep Learning in PythonLeft off. You already know the basics of building an artificial neural net in PythonNow you have a plug and play script you can use with TensorFlow. Neural networks are a key component of machine-learning, and they are often a top choice. in Kaggle contests. This is the course you should take if you want to learn deep learning and neural networks.

Although you have learned a lot about backpropagation already, there are still many unanswered 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.

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You’ll also learn about momentum. It can be useful for helping you to get through local minima. Learning adaptive learning rate techniques such as RMSprop, AdaGrad and Adam can help you speed up your training.

We will be discussing modern techniques like batch normalization and dropout regularization. in Both TensorFlow & Theano. The course is continually being updated. New regularization techniques and methods are constantly added. in The near future

I am going to show you TensorFlow in a brief overview of my previous course. 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 confidence

Theano was the precursor to all modern deep-learning libraries. We have nearly TOO many options today. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. This course covers all of them! Choose the one that you love most.

TensorFlow/Theano has the ability to use the GPU for faster training. This tutorial will show you how I set up a GPU instance on AWS. It will also compare the performance of the GPU vs CPU to train deep neural networks.

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 important part of deep-learning history. They are still used today for testing. These images are important for any deep learning expert.

This course is about “how to build and understand”Not just “how to use”. Anyone can learn how to use an API in 15 minutes after reading some documentation. It’s not about. “remembering facts”It’s about “seeing for yourself” via experimentation. It will show you how to visualize what is happening in Internally, the model. This course will give you a deeper understanding of machine learning models.

“If you can’t implement it, you don’t understand it”

Or, Richard Feynman, a great physicist said: “What I cannot create, I do not understand”.

My courses are unique in that you learn how to implement machine-learning algorithms from scratch.

Some courses will also teach you how plugging works in You have put your data in a library. But do you really need 3 lines of code to help?

After repeating the process with 10 datasets you realize you didn’t learn all of them. You only learned one thing and then you just repeated the exact same 3 lines 10 times.

Prerequisites suggested:

Know about gradient descent

Probability and statistics

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: loading CSV files, matrix and vector operations

Learn how to create a neural network using Numpy

WHAT ORDER SHOULD YOU TAKE YOUR COURSES IN?:

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).

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Who is this course for?

Professionals and students looking to expand their knowledge in machine learning.
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.
My earlier course, deep learning, is for those who don’t know anything about softmax or backpropagation. in PythonFirst

Here’s what you’ll get in Modern Deep Learning in Python

Modern Deep Learning in Python : Sample