10最好的深入学习教程推荐

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特写 iPhone,显示 Udemy 应用程序和带笔记本的笔记本电脑有数以千计的在线课程和课程可以帮助您提高 深入学习 技能并获得 深入学习 证书。

在这篇博客文章中,我们的专家汇总了 10 个精选列表 最好的 深入学习 课程, 现在在线提供的教程、培训计划、课程和认证。

我们只包括那些符合我们高质量标准的课程。我们花了很多时间和精力来为您收集这些。这些课程适合所有级别的初学者、中级学习者和专家。

以下是这些课程以及它们为您提供的内容!

10最好的深入学习教程推荐

1. Deep Learning A-Z™: Hands-On Artificial Neural Networks 经过 “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy课程 我们的最佳选择

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

截至目前,超过 339115+ 人们已经注册了这门课程,而且已经结束了 40911+ 评论.

课程内容
Welcome to the course
——————— Part 1 – Artificial Neural Networks ———————
ANN Intuition
Building an ANN
——————– Part 2 – Convolutional Neural Networks ——————–
CNN Intuition
Building a CNN
———————- Part 3 – Recurrent Neural Networks ———————-
RNN Intuition
Building a RNN
Evaluating and Improving the RNN
———————— Part 4 – Self Organizing Maps ————————
SOMs Intuition
Building a SOM
Mega Case Study
————————- Part 5 – Boltzmann Machines ————————-
Boltzmann Machine Intuition
Building a Boltzmann Machine
—————————- Part 6 – AutoEncoders —————————-
AutoEncoders Intuition
Building an AutoEncoder
——————- Annex – Get the Machine Learning Basics ——————-
Regression & Classification Intuition
Data Preprocessing Template
Logistic Regression Implementation
Bonus Lectures

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2. Complete Guide to TensorFlow for Deep Learning with Python 经过 Jose Portilla Udemy课程

Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting edge techniques!

截至目前,超过 92744+ 人们已经注册了这门课程,而且已经结束了 16491+ 评论.

课程内容
Introduction
Installation and Setup
What is Machine Learning?
Crash Course Overview
Introduction to Neural Networks
TensorFlow Basics
Convolutional Neural Networks
Recurrent Neural Networks
Miscellaneous Topics
AutoEncoders
Reinforcement Learning with OpenAI Gym
GAN – Generative Adversarial Networks
BONUS

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3. Data Science: Deep Learning and Neural Networks in Python 经过 Lazy Programmer Inc. Udemy课程

“The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code”

截至目前,超过 50178+ 人们已经注册了这门课程,而且已经结束了 8513+ 评论.

课程内容
“Welcome
Review
Preliminaries: From Neurons to Neural Networks
Classifying more than 2 things at a time
Training a neural network
Practical Machine Learning
TensorFlow, exercises, practice, and what to learn next
Project: Facial Expression Recognition
Backpropagation Supplementary Lectures
Higher-Level Discussion
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale”

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4. Tensorflow 2.0: Deep Learning and Artificial Intelligence 经过 “Lazy Programmer Inc., Lazy Programmer Team” Udemy课程

“Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!”

截至目前,超过 38052+ 人们已经注册了这门课程,而且已经结束了 7527+ 评论.

课程内容
“Welcome
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks, Time Series, and Sequence Data
Natural Language Processing (NLP)
Recommender Systems
Transfer Learning for Computer Vision
GANs (Generative Adversarial Networks)
Deep Reinforcement Learning (Theory)
Stock Trading Project with Deep Reinforcement Learning
Advanced Tensorflow Usage
Low-Level Tensorflow
In-Depth: Loss Functions
In-Depth: Gradient Descent
Extras
Setting up your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale”

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5. Advanced AI: Deep Reinforcement Learning in Python 经过 “Lazy Programmer Team, Lazy Programmer Inc.” Udemy课程

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

截至目前,超过 35250+ 人们已经注册了这门课程,而且已经结束了 4567+ 评论.

课程内容
Introduction and Logistics
The Basics of Reinforcement Learning
OpenAI Gym and Basic Reinforcement Learning Techniques
TD Lambda
Policy Gradients
Deep Q-Learning
A3C
Theano and Tensorflow Basics Review
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

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6. Deep Learning: Convolutional Neural Networks in Python 经过 Lazy Programmer Inc. Udemy课程

“Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning”

截至目前,超过 31700+ 人们已经注册了这门课程,而且已经结束了 4622+ 评论.

课程内容
Welcome
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
Convolutional Neural Networks
Natural Language Processing (NLP)
Convolution In-Depth
Convolutional Neural Network Description
Practical Tips
In-Depth: Loss Functions
In-Depth: Gradient Descent
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

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7. “Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)” 经过 Lazy Programmer Inc. Udemy课程

“VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python”

截至目前,超过 30031+ 人们已经注册了这门课程,而且已经结束了 4692+ 评论.

课程内容
Welcome
Machine Learning Basics Review
Artificial Neural Networks (ANN) Review
Convolutional Neural Networks (CNN) Review
VGG and Transfer Learning
ResNet (and Inception)
Object Detection (SSD / RetinaNet)
Neural Style Transfer
Class Activation Maps
GANs (Generative Adversarial Networks)
Object Localization Project
Keras and Tensorflow 2 Basics Review
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

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8. Deep Learning: Recurrent Neural Networks in Python 经过 Lazy Programmer Inc. Udemy课程

“GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence”

截至目前,超过 29522+ 人们已经注册了这门课程,而且已经结束了 3956+ 评论.

课程内容
“Welcome
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
Recurrent Neural Networks, Time Series, and Sequence Data
Natural Language Processing (NLP)
In-Depth: Loss Functions
In-Depth: Gradient Descent
Extras
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale”

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9. Deep Learning: Advanced Natural Language Processing and RNNs 经过 Lazy Programmer Inc. Udemy课程

“Natural Language Processing (NLP) with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!”

截至目前,超过 25470+ 人们已经注册了这门课程,而且已经结束了 4541+ 评论.

课程内容
“Welcome
Recurrent Neural Networks, Convolutional Neural Networks, and Word Embeddings
Bidirectional RNNs
Sequence-to-sequence models (Seq2Seq)
Attention
Memory Networks
Keras and Tensorflow 2 Basics
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale”

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10. Recommender Systems and Deep Learning in Python 经过 Lazy Programmer Inc. Udemy课程

“The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques”

截至目前,超过 20034+ 人们已经注册了这门课程,而且已经结束了 3840+ 评论.

课程内容
Welcome
Simple Recommendation Systems
Collaborative Filtering
Beginner Q&A
Matrix Factorization and Deep Learning
Restricted Boltzmann Machines (RBMs) for Collaborative Filtering
Big Data Matrix Factorization with Spark Cluster on AWS / EC2
Basics Review
Bayesian Ranking (Scary Version)
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

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下面是一些关于学习的常见问题深入学习

学习深入学习需要多长时间?

“学习深入学习需要多长时间”这个问题的答案是。 . .这取决于。每个人都有不同的需求,每个人都在不同的场景下工作,所以一个人的答案可能与另一个人的答案完全不同。

考虑这些问题:你想学习 深入学习 是为了什么?你的出发点在哪里?您是初学者还是有使用 深入学习 的经验?你能练习多少?每天1小时?每周40小时? 查看本课程关于 深入学习.

深入学习 学起来容易还是难?

不,学习 深入学习 对大多数人来说并不难。检查这个 关于如何学习的课程 深入学习 立刻!

如何快速学习深入学习?

学习 深入学习 最快的方法是先得到这个 深入学习 课程, 然后尽可能练习你学到的任何东西。即使每天只有 15 分钟的练习。一致性是关键.

在哪里学习 深入学习?

如果您想探索和学习 深入学习,那么 Udemy 为您提供了学习 深入学习 的最佳平台。查看此 关于如何学习的课程 深入学习 立刻!