PyTorch: Deep Learning and Artificial Intelligence
- Offered byUDEMY
PyTorch: Deep Learning and Artificial Intelligence at UDEMY Overview
Duration | 17 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
PyTorch: Deep Learning and Artificial Intelligence at UDEMY Highlights
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore's Law using Code
- Transfer Learning to create state-of-the-art image classifiers
PyTorch: Deep Learning and Artificial Intelligence at UDEMY Course details
- Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch
- PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
- This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.
- In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:
- 'NumPy
- 'Pandas
- 'Machine Learning Theory
- 'Test/Train/Validation Data Splits
- 'Model Evaluation - Regression and Classification Tasks
- 'Unsupervised Learning Tasks
- 'Tensors with PyTorch
- 'Neural Network Theory
- ?Perceptrons
- ?Networks
- ?Activation Functions
- ?Cost/Loss Functions
- ?Backpropagation
- ?Gradients
- 'Artificial Neural Networks
- 'Convolutional Neural Networks
- 'Recurrent Neural Networks
- 'and much more!
- By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.
PyTorch: Deep Learning and Artificial Intelligence at UDEMY Curriculum
Introduction
Welcome
Preview
Overview and Outline
Preview
Where to get the Code
Google Colab
Intro to Google Colab, how to use a GPU or TPU for free
Uploading your own data to Google Colab
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
Machine Learning and Neurons
What is Machine Learning?
Regression Basics
Regression Code Preparation
Regression Notebook
Moore's Law
Moore's Law Notebook
Exercise Real Estate Predictions
Linear Classification Basics
Classification Code Preparation
Classification Notebook
Exercise Predicting Diabetes Onset
Saving and Loading a Model
A Short Neuroscience Primer
How does a model "learn"?
Model With Logits
Train Sets vs. Validation Sets vs. Test Sets
Suggestion Box
Feedforward Artificial Neural Networks
Artificial Neural Networks Section Introduction
Forward Propagation
The Geometrical Picture
Activation Functions
Multiclass Classification
How to Represent Images
Code Preparation (ANN)
ANN for Image Classification
ANN for Regression
Exercise E. Coli Protein Localization Sites
Convolutional Neural Networks
What is Convolution?
Convolution on Color Images
CNN Architecture
CNN Code Preparation
CNN for Fashion MNIST
CNN for CIFAR-
Data Augmentation
Batch Normalization
Improving CIFAR- Results
Exercise Facial Expression Recognition
Recurrent Neural Networks, Time Series, and Sequence Data
Sequence Data
Forecasting
Autoregressive Linear Model for Time Series Prediction
Proof that the Linear Model Works
Recurrent Neural Networks
RNN Code Preparation
RNN for Time Series Prediction
Paying Attention to Shapes
GRU and LSTM
A More Challenging Sequence
RNN for Image Classification (Theory)
RNN for Image Classification (Code)
Stock Return Predictions using LSTMs
Other Ways to Forecast
Exercise More Forecasting
Natural Language Processing (NLP)
Embeddings
Neural Networks with Embeddings
Text Preprocessing
Text Classification with LSTMs
CNNs for Text
Text Classification with CNNs
VIP Making Predictions with a Trained NLP Model
Exercise Sentiment Analysis
Recommender Systems
Recommender Systems with Deep Learning Theory
Recommender Systems with Deep Learning Code Preparation
Recommender Systems with Deep Learning Code
VIP Making Predictions with a Trained Recommender Model
Exercise Book Recommendations
Transfer Learning for Computer Vision
Transfer Learning Theory
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
Large Datasets
Approaches to Transfer Learning
Transfer Learning Code
Exercise Transfer Learning
GANs (Generative Adversarial Networks)
GAN Theory
GAN Code Preparation
GAN Code
Exercise DCGAN (Deep Convolutional GAN)
Deep Reinforcement Learning (Theory)
Deep Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to 'learn'?
Solving the Bellman Equation with Reinforcement Learning
Epsilon-Greedy
Q-Learning
Deep Q-Learning / DQN
How to Learn Reinforcement Learning
Stock Trading Project with Deep Reinforcement Learning
Reinforcement Learning Stock Trader Introduction
Data and Environment
Replay Buffer
Program Design and Layout
Reinforcement Learning Stock Trader Discussion
Exercise Personalized Stock Trading Bot
VIP Uncertainty Estimation
Custom Loss and Estimating Prediction Uncertainty
Estimating Prediction Uncertainty Code
VIP Facial Recognition
Facial Recognition Section Introduction
Siamese Networks
Code Outline
Loading in the data
Splitting the data into train and test
Converting the data into pairs
Generating Generators
Creating the model and loss
Accuracy and imbalanced classes
Facial Recognition Section Summary
In-Depth Loss Functions
Mean Squared Error
Binary Cross Entropy
Categorical Cross Entropy
In-Depth Gradient Descent
Gradient Descent
Stochastic Gradient Descent
Momentum
Variable and Adaptive Learning Rates
Extras
Links To Colab Notebooks
Links to VIP Notebooks
Setting up your Environment (FAQ by Student Request)
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Anaconda Environment Setup
Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
Extra Help With Python Coding for Beginners (FAQ by Student Request)
How to Code Yourself
Proof that using Jupyter Notebook is the same as not using it
Effective Learning Strategies for Machine Learning (FAQ by Student Request) hr
How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Machine Learning and AI Prerequisite Roadmap
Appendix / FAQ Finale
What is the Appendix?
BONUS Where to get discount coupons and FREE deep learning material
Other courses offered by UDEMY
Student Forum
Anything you would want to ask experts?