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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/
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课程名称：数据科学：Python中的现代深度学习 课程概述：想知道诸如OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何工作的？在本课程中，您将学习这些突破性应用的基础知识。该课程是我之前课程《Python中的深度学习》的延续。您已经知道如何在Python中构建人工神经网络，并拥有一个可用于TensorFlow的即插即用脚本。神经网络是机器学习的基础之一，并且在Kaggle竞赛中一直是主要选择。 本课程旨在提升您在神经网络和深度学习方面的技能。您已了解反向传播，但仍有很多未解答的问题。您将学习如何通过批量和随机梯度下降这两种常用技术，提高训练速度。这些方法允许您每次仅对数据的小样本进行训练，从而显著加快训练时间。 此外，您还将学习动量技巧，这能帮助您通过局部最小值，避免在学习率上过于保守。我们也会介绍自适应学习率技术，如AdaGrad、RMSprop和Adam，这些也可以加快您的训练速度。 由于您已掌握神经网络的基础，我们将讨论更现代的技术，如dropout正则化和批量归一化，并在TensorFlow和Theano中实现这些技术。该课程将不断更新，未来会引入更多先进的正则化技术。 在上一个课程中，我只是想让您初步了解TensorFlow。在本课程中，我们将从基础开始，让您了解TensorFlow变量和表达式是什么，如何使用这些构建块创建神经网络。我们也将探讨已经存在更长时间且在深度学习中非常受欢迎的库Theano，并在该库中查看变量、表达式和函数的基本构建块，以便让您在Theano中自信地构建神经网络。 Theano是所有现代深度学习库的前身，如今我们有太多选择：Keras、PyTorch、CNTK（微软）、MXNet（亚马逊/Apache）等。本课程将涵盖所有这些，让您选择自己最喜欢的库。 鉴于TensorFlow和Theano的主要优势是能够使用GPU加速训练，我将教您如何在AWS上设置GPU实例，并比较CPU与GPU在训练深度神经网络时的速度差异。有了这些额外的速度，我们将查看一个真实的数据集——著名的MNIST数据集（手写数字的图像），并与各种基准进行比较。这个数据集是研究人员在询问“这个东西是否有效？”时首先会查看的数据集。 这些图像是深度学习历史的重要部分，至今仍用于测试。每个深度学习专家都应该对此有充分了解。该课程专注于“如何构建和理解”，而不仅仅是“如何使用”。任何人都可以在15分钟内阅读一些文档后学会使用API。它关乎“亲自观察”，而不是“记住事实”，通过实验教您如何可视化模型的内部运行。如果您想了解比表面更深入的机器学习模型，这门课程适合您。 “如果你不能实现它，你就不理解它。”伟大的物理学家理查德·费曼曾说：“我无法创造的东西，我就不理解。” 我的课程是唯一能教您从零开始实现机器学习算法的课程。其他课程可能教您如何将数据插入库，但您真的需要帮助写3行代码吗？在10个数据集上做同样的事情后，您会意识到您并没有学到10件事，而只是重复了同3行代码10次... 推荐先修知识： - 了解梯度下降 - 概率与统计 - Python编程：if/else、循环、列表、字典、集合 - Numpy编程：矩阵和向量操作，加载CSV文件 - 知道如何用Numpy编写神经网络 请查看“机器学习和人工智能先修课程路线图”的讲座（可以在任何课程的常见问题中找到，包括免费的Numpy课程），以了解应按什么顺序学习这些课程。
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano. With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence.Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:Know about gradient descentProbability and statisticsPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileKnow how to write a neural network with NumpyWHAT 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)
