| 001 Introduction.en.srt | 7 KB | ||
| 001 Introduction.mp4 | 72 MB | ||
| 001 Linear regression and MSE loss.en.srt | 11.4 KB | ||
| 001 Linear regression and MSE loss.mp4 | 18 MB | ||
| 001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.en.srt | 7.8 KB | ||
| 001 Setting up a coding environment using Anaconda and Jupyter Notebook in Vscode.mp4 | 33.6 MB | ||
| 001 The back propagation algorithm.en.srt | 8.4 KB | ||
| 001 The back propagation algorithm.mp4 | 14.4 MB | ||
| 001 Vanishing gradient problem.en.srt | 21.7 KB | ||
| 001 Vanishing gradient problem.mp4 | 38.3 MB | ||
| 002 Calculus detour.en.srt | 18.4 KB | ||
| 002 Calculus detour.mp4 | 37.1 MB | ||
| 002 Numerical analysis - a.k.a. “trial-and-error”.en.srt | 10.9 KB | ||
| 002 Numerical analysis - a.k.a. “trial-and-error”.mp4 | 18.8 MB | ||
| 002 Train an MNIST model from scratch in plain PyTorch I.en.srt | 20.4 KB | ||
| 002 Train an MNIST model from scratch in plain PyTorch I.mp4 | 96.2 MB | ||
| 002 Vanishing gradient solutions I.en.srt | 18 KB | ||
| 002 Vanishing gradient solutions I.mp4 | 22.4 MB | ||
| 002 What is Machine Learning exactly_.en.srt | 8.3 KB | ||
| 002 What is Machine Learning exactly_.mp4 | 12.4 MB | ||
| 002 lecture1.pdf | 351 KB | ||
| 003 Calculus detour II.en.srt | 10.5 KB | ||
| 003 Calculus detour II.mp4 | 15.9 MB | ||
| 003 Different types of machine learning_ supervised, unsupervised, and reinforcement.en.srt | 17.8 KB | ||
| 003 Different types of machine learning_ supervised, unsupervised, and reinforcement.mp4 | 25.9 MB | ||
| 003 Network view.en.srt | 17.8 KB | ||
| 003 Network view.mp4 | 45.3 MB | ||
| 003 Train an MNIST model from scratch in plain PyTorch II.en.srt | 17.4 KB | ||
| 003 Train an MNIST model from scratch in plain PyTorch II.mp4 | 96.3 MB | ||
| 003 Vanishing gradient solutions II.en.srt | 10.3 KB | ||
| 003 Vanishing gradient solutions II.mp4 | 17.5 MB | ||
| 003 lecture2.pdf | 1.3 MB | ||
| 004 Gradient descent.en.srt | 24.5 KB | ||
| 004 Gradient descent.mp4 | 101 MB | ||
| 004 Perceptrons.en.srt | 10.2 KB | ||
| 004 Perceptrons.mp4 | 15.5 MB | ||
| 004 Stochastic and mini-batch gradient descent.en.srt | 22.7 KB | ||
| 004 Stochastic and mini-batch gradient descent.mp4 | 39.6 MB | ||
| 004 The big picture.en.srt | 7.4 KB | ||
| 004 The big picture.mp4 | 24.3 MB | ||
| 004 Train an MNIST model from scratch in plain PyTorch III.en.srt | 23.5 KB | ||
| 004 Train an MNIST model from scratch in plain PyTorch III.mp4 | 102 MB | ||
| 004 lecture2_2.pdf | 88.9 KB | ||
| 005 Calculus detour - partial derivatives and gradient descent.en.srt | 11.7 KB | ||
| 005 Calculus detour - partial derivatives and gradient descent.mp4 | 42.1 MB | ||
| 005 Deep neural network as features and weights.en.srt | 12 KB | ||
| 005 Deep neural network as features and weights.mp4 | 32.7 MB | ||
| 005 Other optimizers I.en.srt | 13.7 KB | ||
| 005 Other optimizers I.mp4 | 33.3 MB | ||
| 005 The “Deep” in deep learning.en.srt | 12 KB | ||
| 005 The “Deep” in deep learning.mp4 | 25.1 MB | ||
| 005 Train an MNIST model from scratch in plain PyTorch IV.en.srt | 23 KB | ||
| 005 Train an MNIST model from scratch in plain PyTorch IV.mp4 | 75.6 MB | ||
| 005 lecture2_3.pdf | 486.3 KB | ||
| 006 Activation Function.en.srt | 12 KB | ||
| 006 Activation Function.mp4 | 17.5 MB | ||
| 006 Calculus detour - the Chain Rule.en.srt | 21 KB | ||
| 006 Calculus detour - the Chain Rule.mp4 | 38.2 MB | ||
| 006 Loss functions and training vs inference.en.srt | 12.3 KB | ||
| 006 Loss functions and training vs inference.mp4 | 35.8 MB | ||
| 006 Other optimizers II.en.srt | 7.8 KB | ||
| 006 Other optimizers II.mp4 | 11.6 MB | ||
| 006 Train an MNIST model using PyTorch's nn module I.en.srt | 22.1 KB | ||
| 006 Train an MNIST model using PyTorch's nn module I.mp4 | 84.9 MB | ||
| 007 Calculus detour - the Chain Rule II.en.srt | 22 KB | ||
| 007 Calculus detour - the Chain Rule II.mp4 | 36.4 MB | ||
| 007 Hyperparameter tuning strategies.en.srt | 12.5 KB | ||
| 007 Hyperparameter tuning strategies.mp4 | 27.8 MB | ||
| 007 Overparameterization and overfitting.en.srt | 10.9 KB | ||
| 007 Overparameterization and overfitting.mp4 | 20 MB | ||
| 007 Train an MNIST model using PyTorch's nn module II.en.srt | 23.6 KB | ||
| 007 Train an MNIST model using PyTorch's nn module II.mp4 | 102.1 MB | ||
| 007 Why deep learning is unintuitive and how to get good at it.en.srt | 10.6 KB | ||
| 007 Why deep learning is unintuitive and how to get good at it.mp4 | 14.1 MB | ||
| 007 lecture2_5.pdf | 760.1 KB | ||
| 008 Batch normalization.en.srt | 14 KB | ||
| 008 Batch normalization.mp4 | 43.9 MB | ||
| 008 Computational graph I - forward pass.en.srt | 9 KB | ||
| 008 Computational graph I - forward pass.mp4 | 15.1 MB | ||
| 008 How to make neural networks feel intuitive.en.srt | 8.6 KB | ||
| 008 How to make neural networks feel intuitive.mp4 | 18.3 MB | ||
| 008 Linear Algebra detour.en.srt | 19.7 KB | ||
| 008 Linear Algebra detour.mp4 | 33.1 MB | ||
| 008 Train an MNIST model using PyTorch Lightning I.en.srt | 16.9 KB | ||
| 008 Train an MNIST model using PyTorch Lightning I.mp4 | 83 MB | ||
| 008 lecture2_6.pdf | 1.5 MB | ||
| 009 Computational graph II - backward pass.en.srt | 14.1 KB | ||
| 009 Computational graph II - backward pass.mp4 | 48.1 MB | ||
| 009 Course overview.en.srt | 10 KB | ||
| 009 Course overview.mp4 | 13.8 MB | ||
| 009 Overfitting I - problem and solution overview.en.srt | 17.9 KB | ||
| 009 Overfitting I - problem and solution overview.mp4 | 31.2 MB | ||
| 009 Train an MNIST model using PyTorch Lightning II.en.srt | 23.5 KB | ||
| 009 Train an MNIST model using PyTorch Lightning II.mp4 | 118.4 MB | ||
| 009 Vectorization (= parallelization).en.srt | 14.3 KB | ||
| 009 Vectorization (= parallelization).mp4 | 29.3 MB | ||
| 009 lecture2_7.pdf | 931.7 KB | ||
| 010 Computational graph III - backward pass II.en.srt | 15.1 KB | ||
| 010 Computational graph III - backward pass II.mp4 | 63.5 MB | ||
| 010 Next steps.en.srt | 29.5 KB | ||
| 010 Next steps.mp4 | 110.6 MB | ||
| 010 Overfitting II - regularization and drop out.en.srt | 15 KB | ||
| 010 Overfitting II - regularization and drop out.mp4 | 25.4 MB | ||
| 010 Scalability and emergent properties.en.srt | 13.3 KB | ||
| 010 Scalability and emergent properties.mp4 | 25.4 MB | ||
| 010 lecture3.pdf | 1.3 MB | ||
| 011 Computational graph IV - backward pass III.en.srt | 24.4 KB | ||
| 011 Computational graph IV - backward pass III.mp4 | 82.7 MB | ||
| 011 Recap of the forward pass and brief introduction to backward pass.en.srt | 6.7 KB | ||
| 011 Recap of the forward pass and brief introduction to backward pass.mp4 | 11.3 MB | ||
| 011 Softmax activation.en.srt | 13.4 KB | ||
| 011 Softmax activation.mp4 | 28.8 MB | ||
| 011 lecture4.pdf | 751.9 KB | ||
| 012 Forward and backward pass recap and wrap up.en.srt | 13.6 KB | ||
| 012 Forward and backward pass recap and wrap up.mp4 | 46 MB | ||
| 012 Loss functions.en.srt | 8.7 KB | ||
| 012 Loss functions.mp4 | 11.6 MB | ||
| 012 lecture5.pdf | 863.8 KB | ||
| 013 Cross entropy loss.en.srt | 15.8 KB | ||
| 013 Cross entropy loss.mp4 | 26 MB | ||
| 013 lecture6.pdf | 934.7 KB | ||
| 014 lecture7.pdf | 1.2 MB | ||
| 015 lecture8.pdf | 899.8 KB | ||
| 016 lecture9.pdf | 988.9 KB | ||
| 017 lecture10.pdf | 838.4 KB | ||
| 019 lecture12.pdf | 846.3 KB | ||
| 020 lecture13.pdf | 525.4 KB | ||
| 022 lecture15.pdf | 1.3 MB | ||
| 023 lecture15_2.pdf | 844.1 KB | ||
| 024 lecture16.pdf | 929.5 KB | ||
| 025 lecture17.pdf | 1.3 MB | ||
| 026 lecture18.pdf | 1.4 MB | ||
| 027 lecture18_2.pdf | 1.2 MB | ||
| 028 lecture19.pdf | 503.6 KB | ||
| 029 lecture20.pdf | 592.6 KB | ||
| 030 lecture20_2.pdf | 578.1 KB | ||
| 031 lecture21.pdf | 1.1 MB | ||
| 032 lecture22.pdf | 1.2 MB | ||
| 033 lecture23.pdf | 1.6 MB | ||
| 034 lecture24.pdf | 1.1 MB | ||
| 035 lecture24_2.pdf | 764.2 KB | ||
| 036 lecture25.pdf | 1.2 MB | ||
| 037 lecture26.pdf | 537.4 KB | ||
| 038 lecture26_2.pdf | 305.2 KB | ||
| 039 lecture27.pdf | 720.8 KB | ||
| 040 lecture28.pdf | 952.4 KB | ||
| 041 lecture29.pdf | 1.5 MB | ||
| 042 lecture30.pdf | 1.4 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 150 total files | |||
Finally "GET" Deep Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 55 lectures (9h 39m) | Size: 1.9 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 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
Description
Are you afraid of getting started with Deep Learning because it sounds too technical?
Have you been watching Deep Learning videos, but still don’t feel like you “get” it?
Download More Courses Visit and Support Us -->> https://FreeCourseWeb.com
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 958.9 MB | freecoursewb | 5 years | 0 | 0 | |
|
[ FreeCourseWeb ] Udemy - Web Scraping for Data Science - Python & Selenium - Basics Posted by
freecoursewb in Other
|
1.5 GB | freecoursewb | 5 years | 0 | 2 |
|
[ FreeCourseWeb ] Udemy - Ultimate JavaScript Arrays plus One To-Do List Project Posted by
freecoursewb in Other
|
506 MB | freecoursewb | 5 years | 0 | 0 |
| 615 MB | freecoursewb | 5 years | 0 | 1 | |
|
[ FreeCourseWeb ] Udemy - The Complete Google Docs Course - Google Docs Tricks & Tips Posted by
freecoursewb in Other
|
1.8 GB | freecoursewb | 5 years | 0 | 1 |
All Comments