2021版TensorFlow深度学习全面实战课程+7个项目实战The Complete Deep Learning Course 2021 With 7+ Real Projects

本课程将指导您如何使用Google的TensorFlow框架创建用于深度学习的人工神经网络！本课程旨在以易于理解的方式为您提供有关Google TensorFlow框架复杂性的易于理解的指南。其他课程和教程倾向于远离纯张量流，而使用使用户更少控制的抽象。在这里，我们提供一门课程，最终可以作为按预期使用TensorFlow框架的完整指南，同时向您展示深度学习中可用的最新技术！

本课程旨在平衡理论和实际实现，提供完整的google colab和Jupiter笔记本代码指南，以及易于参考的幻灯片和注释。我们还将提供大量练习来测试您的新技能！

那里有很多深度学习框架，那么为什么要使用TensorFlow？

TensorFlow是一个开放源代码软件库，用于使用数据流图进行数值计算。图中的节点表示数学运算，而图的边缘表示在它们之间通信的多维数据数组（张量）。灵活的体系结构允许您使用单个API将计算部署到台式机，服务器或移动设备中的一个或多个CPU或GPU。TensorFlow最初是由Google机器智能研究组织内的Google Brain团队的研究人员和工程师开发的，目的是进行机器学习和深度神经网络研究，但该系统具有足够的通用性，可以广泛应用于其他领域，例如好吧。

全世界的主要公司都在使用它，包括Airbnb，Ebay，Dropbox，Snapchat，Twitter，Uber，SAP，Qualcomm，IBM，Intel，当然还有Google！

此外，该课程还包含基于实际示例的实践练习。因此，您不仅将学习理论，而且还将获得一些动手实践来构建自己的模型。有五个关于医疗保健问题的大项目，还有一个要实践的小项目，列表如下：

• Concrete Quality Prediction Using Deep Neural Networks.
• CIFAR-10.
• Classifying clothing images.
• 20 newsgroups.
• Handwritten Digit.
• Denoising autoencoders (DAEs).
• Movie Reviews Sentiment Analysis Using Recurrent Neural Networks.
• Predicting Stock Price
• Iris Flower.

MP4 | Video: h264, 1280×720 | Audio: AAC, 44100 Hz
Language: English | Size: 6.18 GB | Duration: 16h 38

What you’ll learn
Artificial Neural Networks (ANN)
Convolution Neural Network (CNN)
Recurrent Neural Network (RNN)
Generative adversarial network (GAN)
Deep Convolutional Generative adversarial network (DCGAN)
Natural Language Processing (NLP)
Image Processing
Sentiment Analysis
Autoencoder
Restricted Boltzman Machine
Deep Reinforcement Learning – Monte Carlo

Requirements
There will be no Prerequisites.
Basic knowledge of Python will be good.
But everything will be taught from the round up.
Description
Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects

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 understand guide to the complexities of Google’s TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

Deep Learning.

Google Colab

Anaconda

Jupiter Notebook

Activation Function.

Keras.

Pandas.

Seaborn.

Feature scaling.

Matplotlib.

scikit-learn

Sigmoid Function.

Tanh Function.

ReLU Function.

Leaky Relu Function.

Exponential Linear Unit Function.

Swish function.

Corpora.

NLTK.

TensorFlow 2.0

Tokenization.

Spacy.

PoS tagging.

NER.

Stemming and lemmatization.

Semantics and topic modelling.

Sentiment analysis techniques.

Lexicon-based methods.

Rule-based methods.

Statistical methods.

Machine learning methods.

Bernoulli RBMs.

Introduction to RBMs (Restricted Boltzman Machine).

Introduction to BMs (Boltzman Machine).

Learning data representations with RBMs.

Multilayer neural networks.

Latent vector.

Loading data.

Analysing data.

Training model.

Compiling model.

Visualizing data and model.

Implementing multilayer neural networks

Improving the model performance by removing outliers.

Building a Keras deep neural network model

Neural Network Basics.

TensorFlow Basics.

Artificial Neural Networks (ANN).

Densely Connected Networks.

Convolutional Neural Networks (CNN).

Recurrent Neural Networks (RNN).

AutoEncoders.

Generative Adversarial Network (GAN).

Deep Convolutional Generative adversarial network (DCGAN).

Natural Language Processing (NLP).

Image Processing.

Sentiment Analysis.

Restricted Boltzman Machine.

Reinforcement Learning.

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

Concrete Quality Prediction Using Deep Neural Networks.

CIFAR-10.

Classifying clothing images.

20 newsgroups.

Handwritten Digit.

Denoising autoencoders (DAEs).

Movie Reviews Sentiment Analysis Using Recurrent Neural Networks.

Predicting Stock Price

Iris Flower.

Become a machine learning, and deep learning guru today! We’ll see you inside the course!

Who this course is for:
Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want 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 Machine Learning, Deep Learning and Artificial Intelligence.
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 Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
AI experts who want to expand on the field of applications
Data Scientists who want to take their AI Skills to the next level
Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
Anyone passionate about Artificial Intelligence