課程介紹

*** As seen on Kickstarter ***

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s

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哪些人適合這堂課？

• Anyone interested in Deep Learning
• Students who have at least high school knowledge in math and who want to start learning Deep Learning
• Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
• Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Deep Learning
• Any people who are not satisfied with their job and who want to become a Data Scientist
• Any people who want to create added value to their business by using powerful Deep Learning tools
• Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
• Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms

學習目標

• Understand the intuition behind Artificial Neural Networks
• Apply Artificial Neural Networks in practice
• Understand the intuition behind Convolutional Neural Networks
• Apply Convolutional Neural Networks in practice
• Understand the intuition behind Recurrent Neural Networks
• Apply Recurrent Neural Networks in practice
• Understand the intuition behind Self-Organizing Maps
• Apply Self-Organizing Maps in practice
• Understand the intuition behind Boltzmann Machines
• Apply Boltzmann Machines in practice
• Understand the intuition behind AutoEncoders
• Apply AutoEncoders in practice

課程介紹

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!

This course will guide you through how to use Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to u

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哪些人適合這堂課？

• Python students eager to learn the latest Deep Learning Techniques with TensorFlow

學習目標

• Understand how Neural Networks Work
• Build your own Neural Network from Scratch with Python
• Use TensorFlow for Classification and Regression Tasks
• Use TensorFlow for Image Classification with Convolutional Neural Networks
• Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
• Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
• Learn how to conduct Reinforcement Learning with OpenAI Gym
• Create Generative Adversarial Networks with TensorFlow
• Become a Deep Learning Guru!

課程介紹

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks rig

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哪些人適合這堂課？

• Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course
• Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

學習目標

• Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
• Learn how a neural network is built from basic building blocks (the neuron)
• Code a neural network from scratch in Python and numpy
• Code a neural network using Google’s TensorFlow
• Describe different types of neural networks and the different types of problems they are used for
• Derive the backpropagation rule from first principles
• Create a neural network with an output that has K > 2 classes using softmax
• Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
• Install TensorFlow

課程介紹

In this course we are going to look at NLP (natural language processing) with deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and si

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哪些人適合這堂課？

• Students and professionals who want to create word vector representations for various NLP tasks
• Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
• SHOULD NOT: Anyone who is not comfortable with the prerequisites.

學習目標

• Understand and implement word2vec
• Understand the CBOW method in word2vec
• Understand the skip-gram method in word2vec
• Understand the negative sampling optimization in word2vec
• Understand and implement GloVe using gradient descent and alternating least squares
• Use recurrent neural networks for parts-of-speech tagging
• Use recurrent neural networks for named entity recognition
• Understand and implement recursive neural networks for sentiment analysis
• Understand and implement recursive neural tensor networks for sentiment analysis
• Use Gensim to obtain pretrained word vectors and compute similarities and analogies

課程介紹

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with

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哪些人適合這堂課？

• Professionals and students with strong technical backgrounds who wish to learn state-of-the-art AI techniques

學習目標

• Build various deep learning agents (including DQN and A3C)
• Apply a variety of advanced reinforcement learning algorithms to any problem
• Q-Learning with Deep Neural Networks
• Policy Gradient Methods with Neural Networks
• Reinforcement Learning with RBF Networks
• Use Convolutional Neural Networks with Deep Q-Learning

課程介紹

Welcome to Tensorflow 2.0!

What an exciting time. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google’s library for deep learning and artificial intelligence.

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哪些人適合這堂課？

• Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

學習目標

• Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
• Predict Stock Returns
• Time Series Forecasting
• Computer Vision
• How to build a Deep Reinforcement Learning Stock Trading Bot
• GANs (Generative Adversarial Networks)
• Recommender Systems
• Image Recognition
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Use Tensorflow Serving to serve your model using a RESTful API
• Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
• Use Tensorflow’s Distribution Strategies to parallelize learning
• Low-level Tensorflow, gradient tape, and how to build your own custom models
• Natural Language Processing (NLP) with Deep Learning
• Demonstrate Moore’s Law using Code
• Transfer Learning to create state-of-the-art image classifiers

課程介紹

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

Learn about one of the most powerful Deep Learning architectures yet!

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection

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哪些人適合這堂課？

• Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
• Software Engineers and Data Scientists who want to level up their career

學習目標

• Understand convolution and why it’s useful for Deep Learning
• Understand and explain the architecture of a convolutional neural network (CNN)
• Implement a CNN in TensorFlow 2
• Apply CNNs to challenging Image Recognition tasks
• Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)

課程介紹

Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.

This is one of the most exciting cour

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哪些人適合這堂課？

• Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
• Anyone who wants to learn about object detection algorithms like SSD and YOLO
• Anyone who wants to learn how to write code for neural style transfer
• Anyone who wants to use transfer learning
• Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast

學習目標

• Understand and apply transfer learning
• Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
• Understand and use object detection algorithms like SSD
• Understand and apply neural style transfer
• Understand state-of-the-art computer vision topics
• Class Activation Maps
• GANs (Generative Adversarial Networks)
• Object Localization Implementation Project

課程介紹

It’s hard to believe it’s been been over a year since I released my first course on Deep Learning with NLP (natural language processing).

A lot of cool stuff has happened since then, and I’ve been deep in the trenches learning, researching, and accu

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哪些人適合這堂課？

• Students in machine learning, deep learning, artificial intelligence, and data science
• Professionals in machine learning, deep learning, artificial intelligence, and data science
• Anyone interested in state-of-the-art natural language processing

學習目標

• Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
• Build a neural machine translation system (can also be used for chatbots and question answering)
• Build a sequence-to-sequence (seq2seq) model
• Build an attention model
• Build a memory network (for question answering based on stories)

課程介紹

*** 如Kickstarter所示 ***

人工智能正呈指数级增长。这点毫无疑问。自动驾驶汽车的驾驶里程已经能够达到数百万英里，IBM Watson对患者的诊断比许多医生都好，谷歌Deepmind的AlphaGo在Go上击败了世界围棋冠军 – 围棋是一种直觉起着关键作用的游戏。

但是，随着人工智能的进一步发展，需要解决的问题就越复杂。只有深度学习可以解决这些复杂的问题，这就是为什么它是人工智能的核心。

— 为什么选择Deep Learning A-Z？—

以下是我们认为Deep

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哪些人適合這堂課？

• 对深度学习感兴趣的人
• 至少具有高中数学知识并希望开始进行深度学习的学生
• 任何了解机器学习或深度学习基础知识（包括线性回归或逻辑回归等经典算法以及人工神经网络等更高级的主题），但希望了解更多知识并探索深度学习所有不同领域的中级水平人士
• 任何对编码不甚熟悉但对深度学习感兴趣并希望在数据集上轻松应用深度学习的人士
• 任何想要开始从事数据科学职业的在校大学生
• 任何想要在深度学习中更上一层楼的数据分析师
• 任何对自己的工作不满意并希望成为数据科学家的人士
• 任何想要通过使用强大的深度学习工具为其业务创造附加价值的人士
• 任何想要了解如何在其企业中利用深度学习的指数技术的企业主
• 任何希望利用最前沿的深度学习算法颠覆行业认知的企业家

學習目標

• 理解人工神经网络背后的直觉
• 在实践中应用人工神经网络
• 理解卷积神经网络背后的直觉
• 在实践中应用卷积神经网络
• 理解递归神经网络背后的直觉
• 在实践中应用递归神经网络
• 理解自组织映射背后的直觉
• 在实践中应用自组织映射
• 理解玻尔兹曼机背后的直觉
• 在实践中应用玻尔兹曼机
• 理解自编码器背后的直觉
• 在实践中应用自编码器