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

Welcome to Tensorflow 2.0!

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people’s understanding by simplifying many compl

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哪些人適合這堂課？

• Deep Learning Engineers who want to learn Tensorflow 2.0
• Artificial Intelligence Engineers who want to expand their Deep Learning stack skills
• Computer Scientists who want to enter the exciting area of Deep Learning and Artificial Intelligence
• Data Scientists who want to take their AI Skills to the next level
• AI experts who want to expand on the field of applications
• Python Developers who want to enter the exciting area of Deep Learning and Artificial Intelligence
• Engineers who work in technology and automation
• Businessmen and companies who want to get ahead of the game
• Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
• Anyone passionate about Artificial Intelligence

學習目標

• How to use Tensorflow 2.0 in Data Science
• Important differences between Tensorflow 1.x and Tensorflow 2.0
• How to implement Artificial Neural Networks in Tensorflow 2.0
• How to implement Convolutional Neural Networks in Tensorflow 2.0
• How to implement Recurrent Neural Networks in Tensorflow 2.0
• How to build your own Transfer Learning application in Tensorflow 2.0
• How to build a stock market trading bot using Reinforcement Learning (Deep-Q Network)
• How to build Machine Learning Pipeline in Tensorflow 2.0
• How to conduct Data Validation and Dataset Preprocessing using TensorFlow Data Validation and TensorFlow Transform.
• Putting a TensorFlow 2.0 model into production
• How to create a Fashion API with Flask and TensorFlow 2.0
• How to serve a TensorFlow model with RESTful API

課程介紹

This course will guide you through how to use Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow 2 framewo

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哪些人適合這堂課？

• Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence

學習目標

• Learn to use TensorFlow 2.0 for Deep Learning
• Leverage the Keras API to quickly build models that run on Tensorflow 2
• Perform Image Classification with Convolutional Neural Networks
• Use Deep Learning for medical imaging
• Forecast Time Series data with Recurrent Neural Networks
• Use Generative Adversarial Networks (GANs) to generate images
• Use deep learning for style transfer
• Generate text with RNNs and Natural Language Processing
• Serve Tensorflow Models through an API
• Use GPUs for accelerated deep learning

課程介紹

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?

They are all masters of deep learning.

We often hear about AI, or self-driving cars, or the ‘algor

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哪些人適合這堂課？

• Aspiring data scientists
• People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence,
• Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch

學習目標

• Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework
• Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
• Set Yourself Apart with Hands-on Deep and Machine Learning Experience
• Grasp the Mathematics Behind Deep Learning Algorithms
• Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
• Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
• Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

課程介紹

Just launched with all modern best practices for working with TensorFlow and passing the TensorFlow Developer Certificate exam! Join a live online community of over 500,000+ students and a course taught by a TensorFlow certified expert. This course w

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哪些人適合這堂課？

• Anyone who wants to pass the TensorFlow Developer exam so they can join Google’s Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
• Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
• Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
• Anyone looking to master building ML models with the latest version of TensorFlow

學習目標

• Learn to pass Google’s official TensorFlow Developer Certificate exam (and add it to your resume)
• Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
• Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
• Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam
• Understand how to integrate Machine Learning into tools and applications
• Learn to build all types of Machine Learning Models using the latest TensorFlow 2
• Build image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
• Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
• Applying Deep Learning for Time Series Forecasting
• Gain the skills you need to become a TensorFlow Certified Developer
• Be recognized as a top candidate for recruiters seeking TensorFlow developers

課程介紹

Google has recently released TensorFlow 2.0 which is Google’s most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of supe

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哪些人適合這堂課？

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers

學習目標

• Build, train, test and deploy Advanced Artificial Neural Networks (ANNs) models using Google’s newly released TensorFlow 2.0.
• Understand the underlying theory and mathematics behind Generative Adversarial Neural Networks (GANs).
• Apply revolutionary GANs to generate brand new images using Keras API in TF 2.0.
• Understand the underlying theory and mathematics behind Auto encoders and Variational Auto Encoders (VAEs).
• Train and test Auto-Encoders to perform image compression and de-noising using Keras API in TF 2.0.
• Understand the underlying theory and mathematics behind DeepDream algorithm. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0!
• Understand the intuition behind Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs).
• Train Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text using Keras API in TF 2.0!
• Apply transfer learning to transfer knowledge from pre-trained MobileNet and ResNet networks to classify new images using TensorFlow 2.0 Hub.
• Develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs.
• Deploy AI models in practice using TensorFlow 2.0 Serving.

課程介紹

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.

AI technolo

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哪些人適合這堂課？

• Data Scientists who want to apply their knowledge on Real World Case Studies
• AI Developers
• AI Researchers

學習目標

• Master Google’s newly released TensorFlow 2.0 to build, train, test and deploy Artificial Neural Networks (ANNs) models.
• Learn how to develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs.
• Deploy ANNs models in practice using TensorFlow 2.0 Serving.
• Learn how to visualize models graph and assess their performance during training using Tensorboard.
• Understand the underlying theory and mathematics behind Artificial Neural Networks and Convolutional Neural Networks (CNNs).
• Learn how to train network weights and biases and select the proper transfer functions.
• Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods.
• Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.
• Apply ANNs to perform regression tasks such as house prices predictions and sales/revenue predictions.
• Assess the performance of trained ANN models for regression tasks using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error, R-Squared, and Adjusted R-Squared.
• Assess the performance of trained ANN models for classification tasks using KPI such as accuracy, precision and recall.
• Apply Convolutional Neural Networks to classify images.
• Sample real-world, practical projects:
• Project #1: Train Simple ANN to convert Celsius temperature reading to Fahrenheit
• Project #2 (Exercise): Train Feedforward ANN to predict Revenue/sales
• Project #3: As a real-estate consultant, predict house prices using ANNs (Regression Task)
• Project #4 (Exercise): As a business owner, predict Bike rental usage (Regression Task)
• Project #5: Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection (Classification task)
• Project #6: Develop AI models to perform sentiment analysis and analyze online customer reviews.
• Project #7: Train LeNet Deep Learning models to perform traffic signs classification.
• Project #8: Train CNN to perform fashion classification
• Project #9: Train CNN to perform image classification using Cifar-10 dataset
• Project #10: Deploy deep learning image classification model using TF serving

課程介紹

课程主要包括两大模块（原理和实战），首先会通俗讲解深度学习中各大经典网络架构并基于tensorflow2版本进行实例演示，详解网络模型训练方法与策略。项目实战全部基于真实数据集与实际任务进行展开，零基础入门深度学习与TF框架并进行进阶提升！

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哪些人適合這堂課？

• 人工智能深度学习方向的同学们

學習目標

• Tensorflow基础操作
• Tensorflow核心Api
• 数据集制作方法
• 图像数据与文本数据预处理实战
• 图像识别模型构建
• 文本分类模型构建
• 神经网络基础
• 卷积神经网络原理
• 递归神经网络
• 对抗生成网络架构及其实战
• CycleGan图像融合
• 基于Tensorflow构建各大经典网络模型
• 基于TF的开源项目实战

課程介紹

在當今的企業環境中，機器學習可以說是變得越來越重要了。它使用了監督式和非監督式的演算法，來解決各式各樣的商業問題。在非監督式學習中，AI人工智慧系統嘗試根據數據之間的「相似性」和「差異性」，來對「未標記」和「未分類」的數據進行分類。在這種情況下，和以「監督式學習」為基礎的功能相比，以模型為基礎的「非監督式學習方法」，其功能可以處理更複雜且更困難的問題。在本課程中，我們將研究不同的非監督式學習方法，並使用TensorFlow平台解決實際的問題。此外，使用TensorFlow解決現實世界中的問題，這

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哪些人適合這堂課？

• 資料科學家
• 資料分析師
• 機器學習工程師
• IT專案經理
• 人工智慧從業人員

學習目標

• TensorFlow 2.0
• 實作各種非監督式學習演算法
• 分群法Clustering
• PCA主成分分析
• DBN深度信念網絡
• GAN生成對抗網路