اطلاعات آموزش
در اینجا میتوانید قسمت های مختلف آموزش را انتخاب کنید.
ورود به سیستم
برای دریافت کامل این آموزش ابتدا باید وارد شوید.
نمایه آموزش
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Tensorflow 2.0: Deep Learning and Artificial Intelligence یک دوره آموزشی از سایت Udemy می باشد که بر روی هوش مصنوعی و یادگیری عمیق تمرکز دارد و موضوعاتی نظیر شبکه های عصبی برای بینایی رایانهای، پیشبینی سری زمانی، NLP ،GAN، یادگیری تقویتی، و Tensorflow را به شما آموزش می دهد. تنسورفلو یک کتابخانه گوگل برای هوش مصنوعی و یادگیری عمیق می باشد که نسخه دوم آن به تازگی منتشر شده است. یادگیری عمیق اخیرا به دستاوردهای مهمی در زمینه اتومبیل های بدون سرنشین، تشخیص گفتار، GAN و NLP رسیده است و یادگیری آن می تواند آینده شما را کاملا تغییر دهد. اگر شما می خواهید از علم یادگیری عمیق به شکل حرفه ای استفاده کنید نیاز دارید تا با Tensorflow آشنا شوید. در طول این دوره آموزشی، ساختارهای مهم یادگیری عمیق شامل شبکه عصبی عمیق، شبکه عصبی پیچشی، و شبکه عصبی بازگشتی مورد بحث و آموزش قرار می گیرد و نحوه پردازش تصاویر و توالی یابی داده ها توضیح داده می شود. مدرس دوره همچنین شما را با کتابخانه Tensorflow آشنا می کند و روش توزیع مدل، استفاده از متغیرها، و سایر کاربردهای تنسورفلو را آموزش می دهد. مواردی که در این دوره آموزش داده میشود آشنایی با شبکه های عصبی مصنوعی و عمیق پیشبینی بازده سهام بینایی رایانهای پیشبینی سری زمانی NLP و GAN تشخیص صدا و تصاویر پردازش زبان های طبیعی آشنایی کامل با Tensorflow ساخت مدل DeepDream و محلی سازی اشیاء
آموزش دهنده
شرکت
Udemy
مدت زمان
22 ساعت و 8 دقیقه
- Welcome
-
Google Colab
- Intro to Google Colab, how to use a GPU or TPU for free
- Tensorflow 2.0 in Google Colab
- Uploading your own data to Google Colab
- Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn
- How to Succeed in this Course
- Intro to Google Colab, how to use a GPU or TPU for free
- Tensorflow 2.0 in Google Colab
- Uploading your own data to Google Colab
- Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn
- How to Succeed in this Course
-
Machine Learning and Neurons
- Saving and Loading a Model
- Why Keras
- Suggestion Box
- What is Machine Learning
- Code Preparation (Classification Theory)
- Classification Notebook
- Code Preparation (Regression Theory)
- Regression Notebook
- The Neuron
- How does a model learn
- Making Predictions
- Saving and Loading a Model
- Why Keras
- Suggestion Box
- What is Machine Learning
- Code Preparation (Classification Theory)
- Classification Notebook
- Code Preparation (Regression Theory)
- Regression Notebook
- The Neuron
- How does a model learn
- Making Predictions
-
Feedforward Artificial Neural Networks
- Artificial Neural Networks Section Introduction
- Artificial Neural Networks Section Introduction
- Beginners Rejoice The Math in This Course is Optional
- Forward Propagation
- The Geometrical Picture
- Activation Functions
- Multiclass Classification
- How to Represent Images
- Code Preparation (ANN)
- ANN for Image Classification
- ANN for Regression
- Beginners Rejoice The Math in This Course is Optional
- Forward Propagation
- The Geometrical Picture
- Activation Functions
- Multiclass Classification
- How to Represent Images
- Code Preparation (ANN)
- ANN for Image Classification
- ANN for Regression
-
Convolutional Neural Networks
- Convolution on Color Images
- CNN Architecture
- What is Convolution (part 1)
- What is Convolution (part 2)
- What is Convolution (part 3)
- Convolution on Color Images
- CNN Architecture
- CNN Code Preparation
- CNN for Fashion MNIST
- CNN for CIFAR-10
- Data Augmentation
- Batch Normalization
- Improving CIFAR-10 Results
- CNN Code Preparation
- CNN for Fashion MNIST
- CNN for CIFAR-10
- Data Augmentation
- What is Convolution (part 1)
- What is Convolution (part 2)
- What is Convolution (part 3)
- Batch Normalization
- Improving CIFAR-10 Results
-
Recurrent Neural Networks, Time Series, and Sequence Data
- Sequence Data
- Forecasting
- Autoregressive Linear Model for Time Series Prediction
- Proof that the Linear Model Works
- Recurrent Neural Networks
- RNN Code Preparation
- RNN for Time Series Prediction
- Paying Attention to Shapes
- GRU and LSTM (pt 1)
- GRU and LSTM (pt 2)
- A More Challenging Sequence
- Demo of the Long Distance Problem
- RNN for Image Classification (Theory)
- RNN for Image Classification (Code)
- Stock Return Predictions using LSTMs (pt 1)
- Stock Return Predictions using LSTMs (pt 2)
- Stock Return Predictions using LSTMs (pt 3)
- Other Ways to Forecast
- Sequence Data
- Forecasting
- Autoregressive Linear Model for Time Series Prediction
- Proof that the Linear Model Works
- Recurrent Neural Networks
- RNN Code Preparation
- RNN for Time Series Prediction
- Paying Attention to Shapes
- GRU and LSTM (pt 1)
- GRU and LSTM (pt 2)
- A More Challenging Sequence
- Demo of the Long Distance Problem
- RNN for Image Classification (Theory)
- RNN for Image Classification (Code)
- Stock Return Predictions using LSTMs (pt 1)
- Stock Return Predictions using LSTMs (pt 2)
- Stock Return Predictions using LSTMs (pt 3)
- Other Ways to Forecast
- Natural Language Processing (NLP)
- Recommender Systems
-
Transfer Learning for Computer Vision
- Transfer Learning Theory
- Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
- Large Datasets and Data Generators
- Approaches to Transfer Learning
- Transfer Learning Code (pt 1)
- Transfer Learning Code (pt 2)
- Transfer Learning Theory
- Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
- Large Datasets and Data Generators
- Approaches to Transfer Learning
- Transfer Learning Code (pt 1)
- Transfer Learning Code (pt 2)
- GANs (Generative Adversarial Networks)
-
Deep Reinforcement Learning (Theory)
- Deep Reinforcement Learning Section Introduction
- Elements of a Reinforcement Learning Problem
- States, Actions, Rewards, Policies
- Markov Decision Processes (MDPs)
- The Return
- Value Functions and the Bellman Equation
- What does it mean to “learn”
- Solving the Bellman Equation with Reinforcement Learning (pt 1)
- Solving the Bellman Equation with Reinforcement Learning (pt 2)
- Epsilon-Greedy
- Q-Learning
- Deep Q-Learning DQN (pt 1)
- Deep Q-Learning DQN (pt 2)
- How to Learn Reinforcement Learning
- Deep Reinforcement Learning Section Introduction
- Elements of a Reinforcement Learning Problem
- States, Actions, Rewards, Policies
- Markov Decision Processes (MDPs)
- The Return
- Value Functions and the Bellman Equation
- What does it mean to “learn”
- Solving the Bellman Equation with Reinforcement Learning (pt 1)
- Solving the Bellman Equation with Reinforcement Learning (pt 2)
- Epsilon-Greedy
- Q-Learning
- Deep Q-Learning DQN (pt 1)
- Deep Q-Learning DQN (pt 2)
- How to Learn Reinforcement Learning
-
Stock Trading Project with Deep Reinforcement Learning
- Reinforcement Learning Stock Trader Introduction
- Data and Environment
- Replay Buffer
- Program Design and Layout
- Code pt 1
- Code pt 2
- Code pt 3
- Code pt 4
- Reinforcement Learning Stock Trader Discussion
- Help! Why is the code slower on my machine
- Reinforcement Learning Stock Trader Introduction
- Data and Environment
- Replay Buffer
- Program Design and Layout
- Code pt 1
- Code pt 2
- Code pt 3
- Code pt 4
- Reinforcement Learning Stock Trader Discussion
- Help! Why is the code slower on my machine
-
Advanced Tensorflow Usage
- What is a Web Service (Tensorflow Serving pt 1)
- Tensorflow Serving pt 2
- Tensorflow Lite (TFLite)
- Why is Google the King of Distributed Computing
- Training with Distributed Strategies
- Using the TPU
- What is a Web Service (Tensorflow Serving pt 1)
- Tensorflow Serving pt 2
- Tensorflow Lite (TFLite)
- Why is Google the King of Distributed Computing
- Training with Distributed Strategies
- Using the TPU
- Low-Level Tensorflow
- In-Depth Loss Functions
- In-Depth Gradient Descent
- Extras
-
Setting up your Environment (FAQ by Student Request)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Anaconda Environment Setup
- Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
- Anaconda Environment Setup
- Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
-
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- Beginner's Coding Tips
- How to Code Yourself (part 1)
- How to Code Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Is Theano Dead
- Beginner's Coding Tips
- How to Code Yourself (part 1)
- How to Code Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Is Theano Dead
-
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts Academic or Practical Fast or slow-paced
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts Academic or Practical Fast or slow-paced
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
- Appendix FAQ Finale