Tensorflow 2.0: Deep Learning and Artificial Intelligence
3.0 /5- Offered byUDEMY
Tensorflow 2.0: Deep Learning and Artificial Intelligence at UDEMY Overview
Duration | 24 hours |
Total fee | ₹599 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Tensorflow 2.0: Deep Learning and Artificial Intelligence at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Learn from Lazy Programmer Inc.
Tensorflow 2.0: Deep Learning and Artificial Intelligence at UDEMY Course details
- Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
- 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
- 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 . Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e. G. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Current projects include: Natural Language Processing (NLP) Recommender Systems Transfer Learning for Computer Vision Generative Adversarial Networks (GANs) Deep Reinforcement Learning Stock Trading Bot Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches). Advanced Tensorflow topics include: Deploying a model with Tensorflow Serving (Tensorflow in the cloud) Deploying a model with Tensorflow Lite (mobile and embedded applications) Distributed Tensorflow training with Distribution Strategies Writing your own custom Tensorflow model Converting Tensorflow 1.x code to Tensorflow 2.0 Constants, Variables, and Tensors Eager execution Gradient tape Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics. Thanks for reading, and I'll see you in class!
Tensorflow 2.0: Deep Learning and Artificial Intelligence at UDEMY Curriculum
Welcome
Introduction
Outline
Where to get the code
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?
Machine Learning and Neurons
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
Feedforward Artificial Neural Networks
Artificial Neural Networks Section Introduction
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
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
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)
Natural Language Processing (NLP)
Embeddings
Code Preparation (NLP)
Text Preprocessing
Text Classification with LSTMs
CNNs for Text
Text Classification with CNNs
Recommender Systems
Recommender Systems with Deep Learning Theory
Recommender Systems with Deep Learning Code
Transfer Learning for Computer Vision
Transfer Learning Theory
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
Large Datasets and Data Generators
2 Approaches to Transfer Learning
Transfer Learning Code (pt 1)
Transfer Learning Code (pt 2)
GANs (Generative Adversarial Networks)
GAN Theory
GAN Code
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
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
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
Low-Level Tensorflow
Differences Between Tensorflow 1.x and Tensorflow 2.x
Constants and Basic Computation
Variables and Gradient Tape
Build Your Own Custom Model
VIP: DeepDream
DeepDream Theory
DeepDream Code Outline (pt 1)
DeepDream Code (pt 1)
DeepDream Code Outline (pt 2)
DeepDream Code (pt 2)
DeepDream Code Outline (pt 3)
DeepDream Code (pt 3)
VIP: Object Localization
Localization Introduction and Outline
Localization Code Outline (pt 1)
Localization Code (pt 1)
Localization Code Outline (pt 2)
Localization Code (pt 2)
Localization Code Outline (pt 3)
Localization Code (pt 3)
Localization Code Outline (pt 4)
Localization Code (pt 4)
Localization Code Outline (pt 5)
Localization Code (pt 5)
Localization Code Outline (pt 6)
Localization Code (pt 6)
Localization Code Outline (pt 7)
Localization Code (pt 7)
In-Depth: Loss Functions
Mean Squared Error
Binary Cross Entropy
Categorical Cross Entropy
In-Depth: Gradient Descent
Gradient Descent
Stochastic Gradient Descent
Momentum
Variable and Adaptive Learning Rates
Adam
Extras
Links to TF2.0 Notebooks
Setting up your Environment
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Windows-Focused Environment Setup 2018
Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
Appendix / FAQ
What is the Appendix?
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
How to Code Yourself (part 1)
How to Code Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
How to Succeed in this Course (Long Version)
Is Theano Dead?
What order should I take your courses in? (part 1)
What order should I take your courses in? (part 2)
BONUS: Where to get discount coupons and FREE deep learning material
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