| 1. Introduction.mp4 | 72 MB | ||
| 1. Introduction.srt | 6.7 KB | ||
| 1. Linear regression and MSE loss.mp4 | 18 MB | ||
| 1. Linear regression and MSE loss.srt | 11 KB | ||
| 1. Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4 | 33.6 MB | ||
| 1. Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.srt | 7.5 KB | ||
| 1. The back propagation algorithm.mp4 | 14.4 MB | ||
| 1. The back propagation algorithm.srt | 8.1 KB | ||
| 1. Vanishing gradient problem.mp4 | 38.3 MB | ||
| 1. Vanishing gradient problem.srt | 20.9 KB | ||
| 1.1 lecture23.pdf | 1.6 MB | ||
| 1.1 lecture3.pdf | 1.3 MB | ||
| 10. Computational graph III - backward pass II.mp4 | 63.5 MB | ||
| 10. Computational graph III - backward pass II.srt | 14.5 KB | ||
| 10. Next steps.mp4 | 110.6 MB | ||
| 10. Next steps.srt | 28.3 KB | ||
| 10. Overfitting II - regularization and drop out.mp4 | 25.4 MB | ||
| 10. Overfitting II - regularization and drop out.srt | 14.4 KB | ||
| 10. Scalability and emergent properties.mp4 | 25.4 MB | ||
| 10. Scalability and emergent properties.srt | 12.8 KB | ||
| 10.1 lecture12.pdf | 846.3 KB | ||
| 10.1 lecture20_2.pdf | 578.1 KB | ||
| 10.1 lecture30.pdf | 1.4 MB | ||
| 11. Computational graph IV - backward pass III.mp4 | 82.7 MB | ||
| 11. Computational graph IV - backward pass III.srt | 23.4 KB | ||
| 11. Recap of the forward pass and brief introduction to backward pass.mp4 | 11.3 MB | ||
| 11. Recap of the forward pass and brief introduction to backward pass.srt | 6.5 KB | ||
| 11. Softmax activation.mp4 | 28.8 MB | ||
| 11. Softmax activation.srt | 12.9 KB | ||
| 11.1 lecture13.pdf | 525.4 KB | ||
| 11.1 lecture21.pdf | 1.1 MB | ||
| 12. Forward and backward pass recap and wrap up.mp4 | 46 MB | ||
| 12. Forward and backward pass recap and wrap up.srt | 13.1 KB | ||
| 12. Loss functions.mp4 | 11.6 MB | ||
| 12. Loss functions.srt | 8.4 KB | ||
| 12.1 lecture22.pdf | 1.2 MB | ||
| 13. Cross entropy loss.mp4 | 26 MB | ||
| 13. Cross entropy loss.srt | 15.2 KB | ||
| 2. Calculus detour.mp4 | 37.1 MB | ||
| 2. Calculus detour.srt | 17.6 KB | ||
| 2. Numerical analysis - a.k.a. “trial-and-error”.mp4 | 18.8 MB | ||
| 2. Numerical analysis - a.k.a. “trial-and-error”.srt | 10.5 KB | ||
| 2. Train an MNIST model from scratch in plain PyTorch I.mp4 | 96.2 MB | ||
| 2. Train an MNIST model from scratch in plain PyTorch I.srt | 19.5 KB | ||
| 2. Vanishing gradient solutions I.mp4 | 22.4 MB | ||
| 2. Vanishing gradient solutions I.srt | 17.4 KB | ||
| 2. What is Machine Learning exactly.mp4 | 12.4 MB | ||
| 2. What is Machine Learning exactly.srt | 8 KB | ||
| 2.1 lecture1.pdf | 351 KB | ||
| 2.1 lecture15.pdf | 1.3 MB | ||
| 2.1 lecture24.pdf | 1.1 MB | ||
| 2.1 lecture4.pdf | 751.9 KB | ||
| 3. Calculus detour II.mp4 | 15.9 MB | ||
| 3. Calculus detour II.srt | 10.1 KB | ||
| 3. Different types of machine learning supervised, unsupervised, and reinforcement.mp4 | 25.9 MB | ||
| 3. Different types of machine learning supervised, unsupervised, and reinforcement.srt | 17.1 KB | ||
| 3. Network view.mp4 | 45.3 MB | ||
| 3. Network view.srt | 17.1 KB | ||
| 3. Train an MNIST model from scratch in plain PyTorch II.mp4 | 96.3 MB | ||
| 3. Train an MNIST model from scratch in plain PyTorch II.srt | 16.7 KB | ||
| 3. Vanishing gradient solutions II.mp4 | 17.5 MB | ||
| 3. Vanishing gradient solutions II.srt | 9.9 KB | ||
| 3.1 lecture15_2.pdf | 844.1 KB | ||
| 3.1 lecture2.pdf | 1.3 MB | ||
| 3.1 lecture24_2.pdf | 764.2 KB | ||
| 3.1 lecture5.pdf | 863.8 KB | ||
| 4. Gradient descent.mp4 | 101 MB | ||
| 4. Gradient descent.srt | 23.6 KB | ||
| 4. Perceptrons.mp4 | 15.5 MB | ||
| 4. Perceptrons.srt | 9.8 KB | ||
| 4. Stochastic and mini-batch gradient descent.mp4 | 39.6 MB | ||
| 4. Stochastic and mini-batch gradient descent.srt | 21.9 KB | ||
| 4. The big picture.mp4 | 24.3 MB | ||
| 4. The big picture.srt | 7.1 KB | ||
| 4. Train an MNIST model from scratch in plain PyTorch III.mp4 | 102 MB | ||
| 4. Train an MNIST model from scratch in plain PyTorch III.srt | 22.4 KB | ||
| 4.1 lecture16.pdf | 929.5 KB | ||
| 4.1 lecture25.pdf | 1.2 MB | ||
| 4.1 lecture2_2.pdf | 88.9 KB | ||
| 4.1 lecture6.pdf | 934.7 KB | ||
| 5. Calculus detour - partial derivatives and gradient descent.mp4 | 42.2 MB | ||
| 5. Calculus detour - partial derivatives and gradient descent.srt | 11.2 KB | ||
| 5. Deep neural network as features and weights.mp4 | 32.7 MB | ||
| 5. Deep neural network as features and weights.srt | 11.5 KB | ||
| 5. Other optimizers I.mp4 | 33.3 MB | ||
| 5. Other optimizers I.srt | 13.2 KB | ||
| 5. The “Deep” in deep learning.mp4 | 25.1 MB | ||
| 5. The “Deep” in deep learning.srt | 11.6 KB | ||
| 5. Train an MNIST model from scratch in plain PyTorch IV.mp4 | 75.6 MB | ||
| 5. Train an MNIST model from scratch in plain PyTorch IV.srt | 22 KB | ||
| 5.1 lecture17.pdf | 1.3 MB | ||
| 5.1 lecture26.pdf | 537.4 KB | ||
| 5.1 lecture2_3.pdf | 486.3 KB | ||
| 5.1 lecture7.pdf | 1.2 MB | ||
| 6. Activation Function.mp4 | 17.5 MB | ||
| 6. Activation Function.srt | 11.6 KB | ||
| 6. Calculus detour - the Chain Rule.mp4 | 38.2 MB | ||
| 6. Calculus detour - the Chain Rule.srt | 20.1 KB | ||
| 6. Loss functions and training vs inference.mp4 | 35.8 MB | ||
| 6. Loss functions and training vs inference.srt | 11.9 KB | ||
| 6. Other optimizers II.mp4 | 11.6 MB | ||
| 6. Other optimizers II.srt | 7.5 KB | ||
| 6. Train an MNIST model using PyTorch's nn module I.mp4 | 84.9 MB | ||
| 6. Train an MNIST model using PyTorch's nn module I.srt | 21.2 KB | ||
| 6.1 lecture18.pdf | 1.4 MB | ||
| 6.1 lecture26_2.pdf | 305.2 KB | ||
| 6.1 lecture8.pdf | 899.8 KB | ||
| 7. Calculus detour - the Chain Rule II.mp4 | 36.4 MB | ||
| 7. Calculus detour - the Chain Rule II.srt | 21.1 KB | ||
| 7. Hyperparameter tuning strategies.mp4 | 27.8 MB | ||
| 7. Hyperparameter tuning strategies.srt | 12 KB | ||
| 7. Overparameterization and overfitting.mp4 | 20 MB | ||
| 7. Overparameterization and overfitting.srt | 10.5 KB | ||
| 7. Train an MNIST model using PyTorch's nn module II.mp4 | 102.1 MB | ||
| 7. Train an MNIST model using PyTorch's nn module II.srt | 22.6 KB | ||
| 7. Why deep learning is unintuitive and how to get good at it.mp4 | 14.1 MB | ||
| 7. Why deep learning is unintuitive and how to get good at it.srt | 10.2 KB | ||
| 7.1 lecture18_2.pdf | 1.2 MB | ||
| 7.1 lecture27.pdf | 720.8 KB | ||
| 7.1 lecture2_5.pdf | 760.1 KB | ||
| 7.1 lecture9.pdf | 988.9 KB | ||
| 8. Batch normalization.mp4 | 43.9 MB | ||
| 8. Batch normalization.srt | 13.4 KB | ||
| 8. Computational graph I - forward pass.mp4 | 15.1 MB | ||
| 8. Computational graph I - forward pass.srt | 8.6 KB | ||
| 8. How to make neural networks feel intuitive.mp4 | 18.3 MB | ||
| 8. How to make neural networks feel intuitive.srt | 8.3 KB | ||
| 8. Linear Algebra detour.mp4 | 33.1 MB | ||
| 8. Linear Algebra detour.srt | 18.9 KB | ||
| 8. Train an MNIST model using PyTorch Lightning I.mp4 | 83 MB | ||
| 8. Train an MNIST model using PyTorch Lightning I.srt | 16.1 KB | ||
| 8.1 lecture10.pdf | 838.4 KB | ||
| 8.1 lecture19.pdf | 503.6 KB | ||
| 8.1 lecture28.pdf | 952.4 KB | ||
| 8.1 lecture2_6.pdf | 1.5 MB | ||
| 9. Computational graph II - backward pass.mp4 | 48.1 MB | ||
| 9. Computational graph II - backward pass.srt | 13.5 KB | ||
| 9. Course overview.mp4 | 13.8 MB | ||
| 9. Course overview.srt | 9.7 KB | ||
| 9. Overfitting I - problem and solution overview.mp4 | 31.2 MB | ||
| 9. Overfitting I - problem and solution overview.srt | 17.3 KB | ||
| 9. Train an MNIST model using PyTorch Lightning II.mp4 | 118.4 MB | ||
| 9. Train an MNIST model using PyTorch Lightning II.srt | 22.5 KB | ||
| 9. Vectorization (= parallelization).mp4 | 29.3 MB | ||
| 9. Vectorization (= parallelization).srt | 13.7 KB | ||
| 9.1 lecture20.pdf | 592.6 KB | ||
| 9.1 lecture29.pdf | 1.5 MB | ||
| 9.1 lecture2_7.pdf | 931.7 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 150 total files | |||
Fundamentals of Deep Learning: Core Concepts and PyTorch
https://DevCourseWeb.com
Last Updated 05/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 55 lectures (9h 39m) | Size: 2.35 GB
Get An Intuitive Understanding of Deep Learning
What you'll learn
Develop an intuitive understanding of Deep Learning
Visual and intuitive understanding of core math concepts behind Deep Learning
Detailed view of how exactly deep neural networks work beneath the hood
Computational graphs (which libraries like PyTorch and Tensorflow are built on)
Build neural networks from scratch using PyTorch and PyTorch Lightening
You’ll be ready to explore the cutting edge of AI and more advanced neural networks like CNNs, RNNs and Transformers
You'll be able to understand what deep learning experts are talking about in articles and interviews
You’ll be able to start experimenting with your own AI projects using PyTorch
Requirements
Basic Python programming knowledge
Highschool math
A strong desire to learn Deep Learning
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 3.1 GB | freecoursewb | 1 week | 16 | 9 | |
| 3.3 GB | freecoursewb | 1 week | 18 | 8 | |
| 302.8 MB | freecoursewb | 2 weeks | 15 | 2 | |
| 321.2 MB | freecoursewb | 4 weeks | 0 | 0 | |
| 1.2 GB | freecoursewb | 1 month | 4 | 8 |
All Comments