PyTorch: Deep Learning and Artificial Intelligence

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Embark on a transformative learning adventure with our comprehensive free video course, “PyTorch: Deep Learning and Artificial Intelligence,” hosted on our website howtofree.org. Immerse yourself in over 30 hours of in-depth video content, carefully crafted to provide a rich and engaging learning experience.

Discover the dynamic realm of Deep Learning and Artificial Intelligence as you explore PyTorch, a robust framework designed for building and training neural networks. Unravel the mysteries behind advanced algorithms and techniques, gaining valuable insights into the intricacies of cutting-edge technology.

This educational journey goes beyond the basics, offering a deep dive into the foundations of PyTorch, empowering you to harness its capabilities for creating intelligent systems. Whether you are a novice or an experienced practitioner, our course caters to all levels, providing a learning environment that fosters growth and expertise.

Come along on this educational journey with us to gain a deeper grasp of artificial intelligence and deep learning. Expand your knowledge, keep on top of trends, and discover PyTorch’s ability to influence technology in the future. Enroll right away to take advantage of this course’s endless potential.

What You’ll Learn Deep Learning and Artificial Intelligence:

  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs):
  • Master the principles and applications of Artificial Neural Networks and Deep Neural Networks.
  • Predict Stock Returns:
  • Acquire skills to predict stock returns using advanced techniques.
  • Time Series Forecasting:
  • Explore methods for accurate time series forecasting.
  • Computer Vision:
  • Gain expertise in Computer Vision for image analysis and understanding.
  • Building a Deep Reinforcement Learning Stock Trading Bot:
  • Learn how to construct a Deep Reinforcement Learning-based stock trading bot.
  • GANs (Generative Adversarial Networks):
  • Understand and implement Generative Adversarial Networks for creative applications.
  • Recommender Systems:
  • Master the development of effective Recommender Systems for personalized content suggestions.
  • Image Recognition:
  • Explore the world of Image Recognition using cutting-edge technologies.
  • Convolutional Neural Networks (CNNs):
  • Delve into Convolutional Neural Networks and their applications.
  • Recurrent Neural Networks (RNNs):
  • Grasp the concepts and applications of Recurrent Neural Networks.
  • Natural Language Processing (NLP) with Deep Learning:
  • Gain proficiency in Natural Language Processing using Deep Learning techniques.
  • Demonstrate Moore’s Law using Code:
  • Showcase Moore’s Law through practical coding demonstrations.
  • Transfer Learning for State-of-the-Art Image Classifiers:
  • Harness Transfer Learning techniques to create state-of-the-art image classifiers.
  • Foundations for OpenAI Technologies:
  • Understand important foundations for OpenAI technologies, including ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

Course Content

1. Introduction

  • Welcome
  • Overview and Outline

2. Getting Set Up

  • Get Your Hands Dirty, Practical Coding Experience, Data Links
  • How to use Github & Extra Coding Tips (Optional)
  • Where to get the code, notebooks, and data
  • How to Succeed in This Course
  • Temporary 403 Errors

3. Google Colab Deep Learning and Artificial Intelligence

  • Intro to Google Colab, how to use a GPU or T PU for free
  • Uploading your own data to Google Colab
  • Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

4. Machine Learning and Neurons

  • What is Machine Learning?
  • Regression Basics
  • Regression Code Preparation
  • Regression Notebook
  • Moore’s Law
  • Moore’s Law Notebook
  • Linear Classification Basics
  • Classification Code Preparation
  • Classification Notebook
  • 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

5. Feedforward Artificial Neural Networks

  • Artificial Neural Networks Section Introduction
  • Forward Propagation
  • The Geometrical Picture
  • Activation Functions
  • Multiclass Classification
  • How to Represent Images
  • Color Mixing Clarification
  • Code Preparation (ANN)
  • ANN for Image Classification
  • ANN for Regression
  • How to Choose Hyperparameters

6. Convolutional Neural Networks

  • What is Convolution? (part 1)
  • What is Convolution? (part 2)
  • What is Convolution? (part 3)
  • Convolution on Color Images
  • CNN Architecture
  • CNN Code Preparation (part 1)
  • CNN Code Preparation (part 2)
  • CNN Code Preparation (part 3)
  • CNN for Fashion MNIST
  • CNN for CIFAR-IO
  • Data Augmentation
  • Batch Normalization
  • Improving CIFAR-IO Results

7. 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
  • GRIJ and LSTM (pt 1)
  • GRU and LSTM (pt 2)
  • A More Challenging Sequence
  • RNN for Image Classification (Theory)
  • RNN for Image Classification (Code)
  • Stock Return Predictions using LSTMs (pt 1)
  • Stock Return Predictions using LSTMs (pt 2)
  • Stock Return Predictions using LSTMs (pt 3)
  • Other Ways to Forecast

8. Natural Language Processing (NLP)

  • Embeddings
  • Neural Networks with Embeddings
  • Text Preprocessing Concepts
  • Beginner Blues – PyTorch NLP Version
  • (Legacy) Text Preprocessing Code Preparation
  • (Legacy) Text Preprocessing Code Example
  • Text Classification with LSTMs (V2)
  • CNNs for Text
  • Text Classification with CNNs (V2)
  • (Legacy) VIP: Making Predictions with a Trained NLP Model
  • VIP: Making Predictions with a Trained NLP Model (V2)

9. Recommender Systems

  • Recommender Systems with Deep Learning Theory
  • Recommender Systems with Deep Learning Code Preparation
  • Recommender Systems with Deep Learning Code (pt 1)
  • Recommender Systems with Deep Learning Code (pt 2)
  • VIP: Making Predictions with a Trained Recommender Model

10. Transfer Learning for Computer Vision

  • Transfer Learning Theory
  • Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
  • Large Datasets
  • 2 Approaches to Transfer Learning
  • Transfer Learning Code (pt 1)
  • Transfer Learning Code (pt 2)

11. GANs (Generative Adversarial Networks)

  • GAN Theory
  • GAN Code Preparation
  • GAN Code

12. 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 (pt 1)
  • Solving the Bellman Equation with Reinforcement Learning (pt 2)
  • Epsilon-Greedy
  • a-Learning
  • Deep a-Learning / DQN (pt 1)
  • Deep a-Learning / DQN (pt 2)
  • How to Learn Reinforcement Learning
  • 14 lecturi

13. Stock Trading Project with Deep Reinforcement Learning

  • Reinforcement Learning Stock Trader Introduction
  • Data and Environment
  • Replay Buffer
  • Program Design and Layout
  • Code pt 1
  • Code pt 2
  • Code pt 3
  • Code pt 4
  • Reinforcement Learning Stock Trader Discussion

14. VIP: Uncertainty Estimation

  • Custom Loss and Estimating Prediction IJncertainty
  • Estimating Prediction Uncertainty Code

15. 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

16. In-Depth: Loss Functions

  • Mean Squared Error
  • Binary Cross Entropy
  • Categorical Cross Entropy

17. In-Depth: Gradient Descent

  • Gradient Descent
  • Stochastic Gradient Descent
  • Momentum
  • Variable and Adaptive Learning Rates
  • Adam (pt 1)
  • Adam (pt 2)

18. Extras

  • Where Are The Exercises?

19. Setting up your Environment (FAQ by Student Request)

  • Pre-lnstallation Check
  • 4 lectul
  • 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

20. Extra Help With Python Coding for Beginners (FAQ by Student Request)

  • Beginner’s Coding Tips
  • How to Code Yourself (part 1)
  • How to Code Yourself (part 2)
  • Proof that using Jupyter Notebook is the same as not using it

21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)

  • How to Succeed in this Course (Long Version)
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Machine Learning and A1 Prerequisite Roadmap (pt 1)
  • Machine Learning and A1 Prerequisite Roadmap (pt 2)

22. Appendix / FAQ Finale

  • What is the Appendix?
  • BONUS

Requirements of Deep Learning and Artificial Intelligence:

  • Proficiency in Python and Numpy:
  • Essential coding skills in Python and Numpy are prerequisites for this course. Ensure you have a solid foundation in these languages to fully engage with the content.
  • Optional Theoretical Understanding:
  • For those interested in delving into theoretical aspects, a grasp of derivatives and probability is recommended. While optional, this knowledge will enhance your comprehension of certain course components.

Who Should Take This Course:

  • Beginners to Advanced Students:
  • Ideal for individuals ranging from beginners to advanced students, this course caters to diverse skill levels.
  • Aspiring Learners of Deep Learning and AI:
  • Tailored for those eager to explore the realms of deep learning and artificial intelligence using PyTorch.
  • Students Seeking Comprehensive Knowledge:
  • Whether you’re just starting or looking to advance your understanding, this course provides a comprehensive learning experience in PyTorch, deep learning, and AI.

How long is the course?

The course spans over 30 hours, providing a comprehensive exploration of Deep Learning and Artificial Intelligence using PyTorch.

Is there a beginner’s course for this?

Definitely! We created our lessons with learners of all skill levels in mind, even those who are unfamiliar with AI and deep learning.

How do I access the course?

Simply visit our website howtofree.org, navigate to the course section, and start your educational journey today Deep Learning and Artificial Intelligence.

Free on this educational journey with us and unlock the vast potential of PyTorch in the captivating realms of Deep Learning and Artificial Intelligence. Start learning today HowToFree.ORG!

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