About this courseSkip About this course
We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
What you'll learnSkip What you'll learn
- Explain and apply knowledge of Deep Neural Networks and related machine learning methods;
- Know how to use Python, and Python libraries such as Numpy and Pandas along with the PyTorch library for Deep Learning applications;
- Build Deep Neural Networks using PyTorch.
- What’s Deep Learning and why Pytorch
- 1-D Tensors and useful Pytoch Functions
- 2-D Tensors and useful functions
- Derivatives and Graphs in Pytorch
- Data Loader
Module 2 – Linear Regression
- Prediction 1D regression
- Training 1D regression
- Stochastic gradient descent, mini-batch gradient descent
- Train, test, split and early stopping
- Pytorch way
- Multiple Linear Regression
Module 3 - Classification
- Logistic Regression
- Training Logistic Regressions Part 1
- Training Logistic Regressions Part 2
- Softmax Regression
Module 4 - Neural Networks
- Introduction to Networks
- Network Shape Depth vs Width
- Back Propagation
- Activation functions
Module 5 - Deep Networks
- Batch normalization
- Other optimization methods
Module 6 - Computer Vision Networks
- Max Polling
- Convolutional Networks
- Pre-trained Networks
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