PyTorch: Deep Learning and Artificial Intelligence [Updated 11 2023]

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PyTorch: Deep Learning and Artificial Intelligence [Updated 11.2023]
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 24h 18m | 7.83 GB
Created by Lazy Programmer Team​

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

What you'll learn

• 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

Requirements

• Know how to code in Python and Numpy
• For the theoretical parts (optional), understand derivatives and probability

Description

Welcome to PyTorch: Deep Learning and Artificial Intelligence!

Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.

Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?

Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?

It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version.

On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.

If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.

Deep Learning has been responsible for some amazing achievements recently, such as:

• Generating beautiful, photo-realistic images of people and things that never existed (GANs)
• Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
• Self-driving cars (Computer Vision)
• Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
• Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

• Natural Language Processing (NLP)
• Recommender Systems
• Transfer Learning for Computer Vision
• Generative Adversarial Networks (GANs)
• Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this! In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here.

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I'll see you in class!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

• Every line of code explained in detail - email me any time if you disagree
• No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
• Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

Beginners to advanced students who want to learn about deep learning and AI in PyTorch

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7.84 GB | 20min 20s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 -Welcome.mp4 (35.67 MB)
2 -Overview and Outline.mp4 (79.64 MB)
1 -Where to get the code, notebooks, and data.mp4 (26.91 MB)
2 -How to Succeed in This Course.mp4 (16.23 MB)
3 -Temporary 403 Errors.mp4 (21.98 MB)
1 -Intro to Google Colab, how to use a GPU or TPU for free.mp4 (60.44 MB)
2 -Uploading your own data to Google Colab.mp4 (90.51 MB)
3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 (56.99 MB)
1 -What is Machine Learning.mp4 (70.47 MB)
2 -Regression Basics.mp4 (73.01 MB)
3 -Regression Code Preparation.mp4 (45.5 MB)
4 -Regression Notebook.mp4 (71.87 MB)
5 -Moore's Law.mp4 (30.56 MB)
6 -Moore's Law Notebook.mp4 (78.92 MB)
7 -Linear Classification Basics.mp4 (65.85 MB)
8 -Classification Code Preparation.mp4 (26.55 MB)
9 -Classification Notebook.mp4 (78.34 MB)
10 -Saving and Loading a Model.mp4 (28.76 MB)
11 -A Short Neuroscience Primer.mp4 (44.63 MB)
12 -How does a model learn.mp4 (50.07 MB)
13 -Model With Logits.mp4 (27.22 MB)
14 -Train Sets vs Validation Sets vs Test Sets.mp4 (52.17 MB)
15 -Suggestion Box.mp4 (27.15 MB)
1 -Artificial Neural Networks Section Introduction.mp4 (33.42 MB)
2 -Forward Propagation.mp4 (47 MB)
3 -The Geometrical Picture.mp4 (56.44 MB)
4 -Activation Functions.mp4 (89.24 MB)
5 -Multiclass Classification.mp4 (48.65 MB)
6 -How to Represent Images.mp4 (75.44 MB)
7 -Color Mixing Clarification.mp4 (4.85 MB)
8 -Code Preparation (ANN).mp4 (66.07 MB)
9 -ANN for Image Classification.mp4 (106.37 MB)
10 -ANN for Regression.mp4 (80.08 MB)
11 -How to Choose Hyperparameters.mp4 (39.47 MB)
1 -What is Convolution (part 1).mp4 (79.71 MB)
2 -What is Convolution (part 2).mp4 (24.06 MB)
3 -What is Convolution (part 3).mp4 (29.81 MB)
4 -Convolution on Color Images.mp4 (75.65 MB)
5 -CNN Architecture.mp4 (89.46 MB)
6 -CNN Code Preparation (part 1).mp4 (79.91 MB)
7 -CNN Code Preparation (part 2).mp4 (36.67 MB)
8 -CNN Code Preparation (part 3).mp4 (33.65 MB)
9 -CNN for Fashion MNIST.mp4 (73.75 MB)
10 -CNN for CIFAR-10.mp4 (55.28 MB)
11 -Data Augmentation.mp4 (44.44 MB)
12 -Batch Normalization.mp4 (23.41 MB)
13 -Improving CIFAR-10 Results.mp4 (75.68 MB)
1 -Sequence Data.mp4 (114.18 MB)
2 -Forecasting.mp4 (48.39 MB)
3 -Autoregressive Linear Model for Time Series Prediction.mp4 (81.23 MB)
4 -Proof that the Linear Model Works.mp4 (17.83 MB)
5 -Recurrent Neural Networks.mp4 (92.6 MB)
6 -RNN Code Preparation.mp4 (55.26 MB)
7 -RNN for Time Series Prediction.mp4 (71.73 MB)
8 -Paying Attention to Shapes.mp4 (56.32 MB)
9 -GRU and LSTM (pt 1).mp4 (79.78 MB)
10 -GRU and LSTM (pt 2).mp4 (50.41 MB)
11 -A More Challenging Sequence.mp4 (87.18 MB)
12 -RNN for Image Classification (Theory).mp4 (32.24 MB)
13 -RNN for Image Classification (Code).mp4 (20.48 MB)
14 -Stock Return Predictions using LSTMs (pt 1).mp4 (77.75 MB)
15 -Stock Return Predictions using LSTMs (pt 2).mp4 (43.24 MB)
16 -Stock Return Predictions using LSTMs (pt 3).mp4 (71.1 MB)
17 -Other Ways to Forecast.mp4 (28.29 MB)
1 -Embeddings.mp4 (59.91 MB)
2 -Neural Networks with Embeddings.mp4 (15.6 MB)
3 -Text Preprocessing Concepts.mp4 (52.3 MB)
4 -Beginner Blues - PyTorch NLP Version.mp4 (64.1 MB)
5 -(Legacy) Text Preprocessing Code Preparation.mp4 (44.34 MB)
6 -(Legacy) Text Preprocessing Code Example.mp4 (47.78 MB)
7 -Text Classification with LSTMs (V2).mp4 (117.18 MB)
8 -CNNs for Text.mp4 (58.4 MB)
9 -Text Classification with CNNs (V2).mp4 (54.46 MB)
10 -(Legacy) VIP Making Predictions with a Trained NLP Model.mp4 (48.81 MB)
11 -VIP Making Predictions with a Trained NLP Model (V2).mp4 (25.46 MB)
1 -Recommender Systems with Deep Learning Theory.mp4 (64.74 MB)
2 -Recommender Systems with Deep Learning Code Preparation.mp4 (40.07 MB)
3 -Recommender Systems with Deep Learning Code (pt 1).mp4 (69.53 MB)
4 -Recommender Systems with Deep Learning Code (pt 2).mp4 (76.79 MB)
5 -VIP Making Predictions with a Trained Recommender Model.mp4 (32.7 MB)
1 -Transfer Learning Theory.mp4 (58.1 MB)
2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 (21.6 MB)
3 -Large Datasets.mp4 (41.23 MB)
4 -2 Approaches to Transfer Learning.mp4 (21.75 MB)
5 -Transfer Learning Code (pt 1).mp4 (77.78 MB)
6 -Transfer Learning Code (pt 2).mp4 (56.26 MB)
1 -GAN Theory.mp4 (92.01 MB)
2 -GAN Code Preparation.mp4 (28.1 MB)
3 -GAN Code.mp4 (61.47 MB)
1 -Deep Reinforcement Learning Section Introduction.mp4 (40.72 MB)
2 -Elements of a Reinforcement Learning Problem.mp4 (104.97 MB)
3 -States, Actions, Rewards, Policies.mp4 (44.06 MB)
4 -Markov Decision Processes (MDPs).mp4 (50.45 MB)
5 -The Return.mp4 (23.37 MB)
6 -Value Functions and the Bellman Equation.mp4 (47.69 MB)
7 -What does it mean to "learn".mp4 (31.64 MB)
8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 (45.84 MB)
9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 (55.51 MB)
10 -Epsilon-Greedy.mp4 (41.46 MB)
11 -Q-Learning.mp4 (66.73 MB)
12 -Deep Q-Learning DQN (pt 1).mp4 (60.13 MB)
13 -Deep Q-Learning DQN (pt 2).mp4 (52.1 MB)
14 -How to Learn Reinforcement Learning.mp4 (40.27 MB)
1 -Reinforcement Learning Stock Trader Introduction.mp4 (28.78 MB)
2 -Data and Environment.mp4 (55.69 MB)
3 -Replay Buffer.mp4 (24.89 MB)
4 -Program Design and Layout.mp4 (26.85 MB)
5 -Code pt 1.mp4 (66.34 MB)
6 -Code pt 2.mp4 (70 MB)
7 -Code pt 3.mp4 (58.58 MB)
8 -Code pt 4.mp4 (52.59 MB)
9 -Reinforcement Learning Stock Trader Discussion.mp4 (17.17 MB)
1 -Custom Loss and Estimating Prediction Uncertainty.mp4 (43.47 MB)
2 -Estimating Prediction Uncertainty Code.mp4 (42.66 MB)
1 -Facial Recognition Section Introduction.mp4 (24.31 MB)
2 -Siamese Networks.mp4 (50.41 MB)
3 -Code Outline.mp4 (23.86 MB)
4 -Loading in the data.mp4 (35 MB)
5 -Splitting the data into train and test.mp4 (26.26 MB)
6 -Converting the data into pairs.mp4 (30.37 MB)
7 -Generating Generators.mp4 (32.46 MB)
8 -Creating the model and loss.mp4 (29.35 MB)
9 -Accuracy and imbalanced classes.mp4 (51.15 MB)
10 -Facial Recognition Section Summary.mp4 (18.31 MB)
1 -Mean Squared Error.mp4 (33.78 MB)
2 -Binary Cross Entropy.mp4 (23.61 MB)
3 -Categorical Cross Entropy.mp4 (31.72 MB)
1 -Gradient Descent.mp4 (34.92 MB)
2 -Stochastic Gradient Descent.mp4 (22.97 MB)
3 -Momentum.mp4 (34.26 MB)
4 -Variable and Adaptive Learning Rates.mp4 (34.84 MB)
5 -Adam (pt 1).mp4 (55.11 MB)
6 -Adam (pt 2).mp4 (52.77 MB)
1 -Where Are The Exercises.mp4 (25.94 MB)
1 -Pre-Installation Check.mp4 (22.74 MB)
2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (150.72 MB)
3 -Anaconda Environment Setup.mp4 (180.83 MB)
4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 (167.24 MB)
1 -Beginner's Coding Tips.mp4 (75.68 MB)
2 -How to Code Yourself (part 1).mp4 (71.83 MB)
3 -How to Code Yourself (part 2).mp4 (49.13 MB)
4 -Proof that using Jupyter Notebook is the same as not using it.mp4 (69.44 MB)
5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 (43.56 MB)
6 -How to use Github & Extra Coding Tips (Optional).mp4 (63.91 MB)
1 -How to Succeed in this Course (Long Version).mp4 (35.21 MB)
2 -Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (105.56 MB)
3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (79.77 MB)
4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (108.14 MB)
1 -What is the Appendix.mp4 (16.36 MB)
2 -BONUS.mp4 (40.43 MB)

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