What you would learn in PyTorch: The Complete Guide 2022 course?
While Google's Deep Learning library Tensorflow has gained massive popularity in the last few months, PyTorchhas have been the preferred library choice for researchers and professionals worldwide for deep learning and artificial intelligence researchers and professionals worldwide.
Is it possible is that Tensorflow is popular because Google has become well-known and has a proven marketing strategy?
What caused Tensorflow to alter so drastically from version one to 2. Was there something seriously flawed that caused it to change? And are there still issues that could be causing problems?
It's not as well known that PyTorch is supported by another Internet giant, Facebook (specifically FAIR, the Facebook AI Research Lab FAIR). Suppose you're looking for an acclaimed deep learning library that's backed by billion-dollar businesses and a wide range of support from the community and support. In that case, you're not going to be disappointed using PyTorch. Perhaps it's an added benefit that the library doesn't erase all your previous code once it is upgraded into the new version. ;)
On the other hand, it's widely known that the best AI stores (ex. OpenAI Apple in addition to JPMorgan Chase) utilize PyTorch. OpenAI recently switched over to PyTorch in 2022, which is a positive sign that PyTorch is gaining momentum.
In this course, you'll learn everything you must know to start using Pytorch. It will cover:
NumPy
Pandas
Tensors using PyTorch
Neural Network Theory
Perceptrons
Networks
Functions of Activation
Cost/Loss Functions
Backpropagation
Gradients
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
and much more!
After this course, you'll be able to develop many different deep learning models to tackle your problems using your own data set.
Who is this course intended for:
- Anyone interested to learn more about Machine Learning.
- Students who possess at least high school level knowledge in math and want to learn more about Machine Learning, Deep Learning, and Artificial Intelligence
- Anyone at an intermediate level who understands the basics about machine learning, including traditional algorithms like logistic regression and linear regression, but wants to understand more about it and learn about the various fields that comprise Machine Learning, Deep Learning, and Artificial Intelligence.
- Anyone who isn't comfortable with programming but is interested in Machine Learning, Deep Learning, Artificial Intelligence and wants to apply it to data quickly.
- Students in the college years who would like to pursue an occupation in Data Science
- Anyone interested in data analysis and wants to improve their skills in Machine Learning, Deep Learning, and Artificial Intelligence.
- People who aren't happy with their work and would like to be Information Scientists.
- Anyone who wishes to add value to their business through the power of Machine Learning, Artificial Intelligence, and Deep Learning tools. Anyone who wants to join the car industry to become a Data Scientist, Machine Learning, Deep Learning, and Artificial Intelligence engineer.
- Anyone who wants to add value to their local hospital uses robust Machine Learning, Artificial Intelligence, and Deep Learning tools.
- Anyone who would like to work in the healthcare industry in Data Science or Machine Learning, Deep Learning, and Artificial Intelligence engineer.
- Anyone who wants to work for a Taxi Company as a Data Scientist, Machine Learning, Deep Learning, and Artificial Intelligence engineer.
Course Content:
- Pandas
- Pytorch
- Numpy
- Artificial Neural Networks (ANN)
- Generative adversarial networks (GAN)
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Google Colab.
- Matplotlib.
- Long Short Time (LSTM)
- Language Model
- Reinforcement Learning
- OpenAI Gym
Download PyTorch: The Complete Guide 2022 from below links NOW!
