PirateBay Download | deep understanding deep learning python intro

deep understanding deep learning python intro

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Files:: 759
Size:: 21.3 GB
Uploaded On:: Aug. 21, 2021, 3:52 a.m.
Added By:: tuts756
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Info Hash:: 423A996F5A41B9A7A5512244B26E1CC6CAAB5C12

Description Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data. Deep learning is now used in most areas of technology, business, and entertainment. And it’s becoming more important every year. How does deep learning work? Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data. Is it really that simple? mmm OK, it’s actually a tiny bit more complicated than that but that’s the core idea, and everything else — literally everything else in deep learning — is just clever ways of putting together these fundamental building blocks. That doesn’t mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks. Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning — I mean, really learn deep learning, not just have superficial knowledge from a youtube video — by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport! What is this course all about? Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future. Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems. You can learn everything about deep learning in this course. In this course, you will learn Theory: Why are deep learning models built the way they are? Math: What are the formulas and mechanisms of deep learning? Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)? Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc. Python: If you’re completely new to Python, go through the 8+ hour coding tutorial appendix. If you’re already a knowledgeable coder, then you’ll still learn some new tricks and code optimizations. Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google’s cloud services. No need to install anything on your computer. Unique aspects of this course Clear and comprehensible explanations of concepts in deep learning. Several distinct explanations of the same ideas, which is a proven technique for learning. Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks. LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself! Active Q&A forum where you can ask questions, get feedback, and contribute to the community. 8+ hour Python tutorial. That means you don’t need to master Python before enrolling in this course. So what are you waiting for?? Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up. I hope to see you soon in the course! Mike Who this course is for: Students in a deep learning course Machine-learning enthusiasts Anyone interested in mechanisms of AI (artificial intelligence) Data scientists who want to expend their library of skills Aspiring data scientists Scientists and researchers interested in deep learning Requirements Interest in learning about deep learning! Python/Pytorch skills are taught in the course A Google account (google-colab is used as the Python IDE) Last Updated 8/2021

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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/19 Understand and design CNNs/005 Examine feature map activations.mp4 260.6 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/02 Download all course materials/003 DUDL_PythonCode.zip 700.8 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/02 Download all course materials/002 My policy on code-sharing.en.srt 2.5 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.en.srt 23.1 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/27 Python intro_ Data types/008 Dictionaries.en.srt 17.1 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.en.srt 17.0 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/22 Style transfer/002 The Gram matrix (feature activation covariance).en.srt 16.8 KB
[TutsNode.com] - A deep understanding of deep learning (with Python intro)/24 Ethics of deep learning/005 Accountability and making ethical AI.en.srt 16.7 KB
[TutsNode.com] - A deep understanding of deep learning (with Python intro)/10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.en.srt 16.6 KB
[TutsNode.com] - A deep understanding of deep learning (with Python intro)/19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.en.srt 16.5 KB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/05 Math, numpy, PyTorch/004 Converting reality to numbers.mp4 33.2 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/30 Python intro_ Flow control/005 Continue.mp4 33.0 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.mp4 32.7 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/08 Overfitting and cross-validation/003 Generalization.mp4 32.4 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/06 Gradient descent/009 Vanishing and exploding gradients.mp4 30.2 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/29 Python intro_ Functions/001 Inputs and outputs.mp4 29.5 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/15 Weight inits and investigations/010 Use default inits or apply your own_.mp4 28.1 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/07 ANNs/012 Why multilayer linear models don't exist.mp4 26.5 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/20 CNN milestone projects/004 Project 3_ FMNIST.mp4 26.5 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/11 FFNs/001 What are fully-connected and feedforward networks_.mp4 25.5 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/29 Python intro_ Functions/007 Copies and referents of variables.mp4 23.8 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/04 About the Python tutorial/001 Should you watch the Python tutorial_.mp4 23.8 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/06 Gradient descent/010 Tangent_ Notebook revision history.mp4 22.2 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/27 Python intro_ Data types/001 How to learn from the Python tutorial.mp4 22.0 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.mp4 21.0 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/21 Transfer learning/006 CodeChallenge_ VGG-16.mp4 20.3 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/05 Math, numpy, PyTorch/001 Introduction to this section.mp4 11.1 MB
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[TutsNode.com] - A deep understanding of deep learning (with Python intro)/02 Download all course materials/002 My policy on code-sharing.mp4 10.2 MB

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