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Deep Learning Prerequisites: Logistic Regression in Python Course

Deep Learning Prerequisites: Logistic Regression in Python Course

Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python

What you’ll learn

Deep Learning Prerequisites: Logistic Regression in Python Course

  • program logistic regression from scratch in Python
  • describe how logistic regression is useful in data science
  • derive the error and update rule for logistic regression
  • understand how logistic regression works as an analogy for the biological neuron
  • use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
  • understand why regularization is used in machine learning

Requirements

  • Derivatives, matrix arithmetic, probability
  • You should know some basic Python coding with the Numpy Stack

Description

This course is a lead-in to deep learning and neural networks – it covers a popular and fundamental technique used in machine learning, data science, and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.



This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we’ll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Deep Learning Prerequisites: Logistic Regression in Python Course

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone’s emotions just based on a picture!

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you won’t use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.





This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

  • calculus (taking derivatives)
  • matrix arithmetic
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):



  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don’t, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

  • Adult learners who want to get into the field of data science and big data
  • Students who are thinking of pursuing machine learning or data science
  • Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python
  • People who know some machine learning but want to be able to relate it to artificial intelligence
  • People who are interested in bridging the gap between computational neuroscience and machine learning

Deep Learning Prerequisites: Logistic Regression in Python Course




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SEE MORE COURSE: Learning MongoDB – A Training Video From Infinite Skills Course Catalog

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Python for Financial Analysis and Algorithmic Trading Course Catalog

Python for Financial Analysis and Algorithmic Trading Course Catalog

Learn NumPy, pandas, matplotlib, Quantopian, finance, and more for algorithmic trading with Python!

What you’ll learn

Python for Financial Analysis and Algorithmic Trading Course Catalog

  • Use NumPy to quickly work with Numerical Data
  • Use Pandas for Analyze and Visualize Data
  • Learn how to use Matplotlib to create custom plots
  • Learn how to use statsmodels for Time Series Analysis
  • Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
  • Use Exponentially Weighted Moving Averages
  • Use ARIMA models on Time Series Data
  • Calculate the Sharpe Ratio
  • Optimize Portfolio Allocations
  • Understand the Capital Asset Pricing Model
  • Learn about the Efficient Market Hypothesis
  • Conduct algorithmic Trading on Quantopian

Requirements

  • Some knowledge of programming (preferably Python)
  • Ability to Download Anaconda (Python) to your computer
  • Basic Statistics and Linear Algebra will be helpful

Description

Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!




This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We’ll start by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, NumPy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!

 We’ll cover the following topics used by financial professionals:

  • Python Fundamentals
  • NumPy for High-Speed Numerical Processing
  • Pandas for Efficient Data Analysis
  • Matplotlib for Data Visualization
  • Using pandas-DataReader and Quandl for data ingestion
  • Pandas Time Series Analysis Techniques
  • Stock Returns Analysis
  • Cumulative Daily Returns
  • Volatility and Securities Risk
  • EWMA (Exponentially Weighted Moving Average)
  • Statsmodels
  • ETS (Error-Trend-Seasonality)
  • ARIMA (Auto-regressive Integrated Moving Averages)
  • Auto Correlation Plots and Partial Auto Correlation Plots
  • Sharpe Ratio
  • Portfolio Allocation Optimization
  • Efficient Frontier and Markowitz Optimization
  • Types of Funds
  • Order Books
  • Short Selling
  • Capital Asset Pricing Model
  • Stock Splits and Dividends
  • Efficient Market Hypothesis
  • Algorithmic Trading with Quantopian
  • Futures Trading

Who this course is for:

Python for Financial Analysis and Algorithmic Trading Course Catalog




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Python for Finance: Investment Fundamentals & Data Analytics Course Catalog

Python for Finance: Investment Fundamentals & Data Analytics Course Catalog

Learn Python Programming and Conduct Real-World Financial Analysis in Python – Complete Python Training

What you’ll learn

Python for Finance: Investment Fundamentals & Data Analytics Course Catalog

  • Learn how to code in Python
  • Take your career to the next level
  • Work with Python’s conditional statements, functions, sequences, and loops
  • Work with scientific packages, like NumPy
  • Understand how to use the data analysis toolkit, Pandas
  • Plot graphs with Matplotlib
  • Use Python to solve real-world tasks
  • Get a job as a data scientist with Python
  • Acquire solid financial acumen
  • Carry out in-depth investment analysis
  • Build investment portfolios
  • Calculate the risk and return of individual securities
  • Calculate risk and return of investment portfolios
  • Apply best practices when working with financial data
  • Use univariate and multivariate regression analysis
  • Understand the Capital Asset Pricing Model
  • Compare securities in terms of their Sharpe ratio
  • Perform Monte Carlo simulations
  • Learn how to price options by applying the Black Scholes formula
  • Be comfortable applying for a developer job in a financial institution

Requirements

  • You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
  • All software and data used in the course is free

Description

Do you want to learn how to use Python in a working environment?

Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems?

If so, then this is the right course for you!




We are proud to present Python for Finance: Investment Fundamentals and Data Analytics – one of the most interesting and complete courses we have created so far. It took our team slightly over four months to create this course, but now, it is ready and waiting for you.

An exciting journey from A-Z.

If you are a complete beginner and you know nothing about coding, don’t worry! We start from the very basics. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks.

Finance Fundamentals.

And it gets even better! The Finance block of this course will teach you in-demand real-world skills employers are looking for. To be a high-paid programmer, you will have to specialize in a particular area of interest. In this course, we will focus on Finance, covering many tools and techniques used by finance professionals daily:

  • Rate of return of stocks
  • Risk of stocks
  • Rate of return of stock portfolios
  • Risk of stock portfolios
  • Correlation between stocks
  • Covariance
  • Diversifiable and non-diversifiable risk
  • Regression analysis
  • Alpha and Beta coefficients
  • Measuring a regression’s explanatory power with R^2
  • Markowitz Efficient frontier calculation
  • The capital asset pricing model
  • Sharpe ratio
  • Multivariate regression analysis
  • Monte Carlo simulations
  • Using Monte Carlo in a Corporate Finance context
  • Derivatives and type of derivatives
  • Applying the Black Scholes formula
  • Using Monte Carlo for options pricing
  • Using Monte Carlo for stock pricing




All these topics are first explained in theory and then applied in practice using Python.

Is there a better way to reinforce what you have learned in the first part of the course?

This course is great, even if you are an experienced programmer, as we will teach you a great deal about the finance theory and mechanics you will need if you start working in a finance context.

Teaching is our passion.

Plain and clear English, relevant examples and time-efficient videos. Don’t forget to check some of our sample videos to see how easy they are to understand.

If you have any questions, contact us! We enjoy communicating with our students and take pride in responding within 1 business day. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding.

What makes this course different from the rest of the Programming and Finance courses out there?

  • This course will teach you how to code in Python and apply these skills in the world of Finance. It is both a Programming and a Finance course.
  • High-quality production – HD video and animations (this isn’t a collection of boring lectures!)
  • Knowledgeable instructors.
  • Extensive Case Studies that will help you reinforce everything you’ve learned.
  • Course Challenge: Solve our exercises and make this course an interactive experience.
  • Excellent support: If you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day.
  • Dynamic: We don’t want to waste your time! The instructors set a very good pace throughout the whole course.



Who this course is for:

  • Aspiring data scientists
  • Programming beginners
  • People interested in finance and investments
  • Programmers who want to specialize in finance
  • Everyone who wants to learn how to code and apply their skills in practice
  • Finance graduates and professionals who need to better apply their knowledge in Python
  • MBA in a Box: Business Lessons from a CEO Course Catalog
  • Last updated 4/2020

Python for Finance: Investment Fundamentals & Data Analytics Course Catalog




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Support Vector Machines in Python – SVM in Python Course Catalog

Support Vector Machines in Python – SVM in Python Course Catalog

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning

What you’ll learn

Support Vector Machines in Python – SVM in Python Course Catalog

  • Get a solid understanding of Support Vector Machines (SVM)
  • Understand the business scenarios where Support Vector Machines (SVM) is applicable
  • Tune a machine learning model’s hyperparameters and evaluate its performance.
  • Use Support Vector Machines (SVM) to make predictions
  • Implementation of SVM models in Python

Requirements

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description

You’re looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right?


You’ve found the right Support Vector Machines techniques course!

How this course will help you?

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real-world problems of business, this course will give you a solid base for that by teaching you some of the advanced techniques of machine learning, which is Support Vector Machines.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through a Decision tree.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to be able to help your business.



What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these:

This is very good, I love the fact the all explanation given can be understood by a layman – Joshua

Thank you, Author, for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files take Quizzes and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.


Go ahead and click the enroll button, and I’ll see you in lesson 1!

Cheers

Start-Tech Academy

Who this course is for:

Support Vector Machines in Python – SVM in Python Course Catalog




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Data Analysis with Pandas and Python Course Catalog – Learn Python

Data Analysis with Pandas and Python Course Catalog – Learn Python

Analyze data quickly and easily with Python’s powerful pandas library! All datasets included — beginners welcome!

What you’ll learn

Data Analysis with Pandas and Python Course Catalog – Learn Python

  • Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more!
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets

Requirements

  • Basic/intermediate experience with Microsoft Excel or other spreadsheet software (common functions, vlookups, Pivot Tables, etc)
  • Basic experience with the Python programming language
  • Strong knowledge of data types (strings, integers, floating points, booleans), etc

Description

Welcome to the most comprehensive Pandas course available! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world!


Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

  • installing
  • sorting
  • filtering
  • grouping
  • aggregating
  • de-duplicating
  • pivoting
  • munging
  • deleting
  • merging
  • visualizing

and more!

Why learn pandas?

If you’ve spent time in spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.



Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets — analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

I call it “Excel on steroids”!

Over more than 19 hours, I’ll take you step-by-step through Pandas, from installation to visualization! We’ll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!

Whether you’re a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!

Who this course is for:

Data Analysis with Pandas and Python Course Catalog – Learn Python




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Complete Guide to TensorFlow for Deep Learning with Python Course Catalog

Complete Guide to TensorFlow for Deep Learning with Python Course Catalog

Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting edge techniques!

What you’ll learn

Complete Guide to TensorFlow for Deep Learning with Python Course Catalog

  • 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
  • Learn how to 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!

Requirements

  • Some knowledge of programming (preferably Python)
  • Some basic knowledge of math (mean, standard deviation, etc..)

Description

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 covers a variety of topics, including

  • Neural Network Basics
  • TensorFlow Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • Reinforcement Learning
  • OpenAI Gym
  • and much more!

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.

Who this course is for:

Complete Guide to TensorFlow for Deep Learning with Python Course Catalog




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Artificial Intelligence: Reinforcement Learning in Python Course Site

Artificial Intelligence: Reinforcement Learning in Python Course Site

Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications

What you’ll learn

Artificial Intelligence: Reinforcement Learning in Python Course Site

  • Apply gradient-based supervised machine learning methods to reinforcement learning
  • Understand reinforcement learning on a technical level
  • Understand the relationship between reinforcement learning and psychology
  • Implement 17 different reinforcement learning algorithms

Requirements

  • Calculus (derivatives)
  • Probability / Markov Models
  • Numpy, Matplotlib
  • Beneficial ave experience with at least a few supervised machine learning methods
  • Gradient descent
  • Good object-oriented programming skills

Description

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

Reinforcement learning has recently become popular for doing all of that and more.





Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’s AlphaGo beat the world champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat. To date, I have over SIXTEEN (16!) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.

It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human.



It’s the closest thing we have so far to a true general artificial intelligence.

  • The multi-armed bandit problem and the explore-exploit dilemma
  • Ways to calculate means and moving averages and their relationship to stochastic gradient descent
  • Markov Decision Processes (MDPs)
  • Dynamic Programming
  • Monte Carlo
  • Temporal Difference (TD) Learning (Q-Learning and SARSA)
  • Approximation Methods (i.e. how to plug in a deep neural network or another differentiable model into your RL algorithm)
  • Project: Apply Q-Learning to build a stock trading bot

If you’re ready to take on a brand new challenge and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.



Suggested Prerequisites:

  • Calculus
  • Probability
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations
  • Linear regression
  • Gradient descent

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don’t, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Artificial Intelligence: Reinforcement Learning in Python Course Site





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Complete Guide to Protocol Buffers 3 [Java, Golang, Python] Course Catalog

Complete Guide to Protocol Buffers 3 [Java, Golang, Python] Course Catalog

Google Protobuf with examples and exercises. Code in Java Go Python. Say Goodbye to JSON & XML. Pre-req to gRPC

What you’ll learn

Complete Guide to Protocol Buffers 3 [Java, Golang, Python] Course Catalog

  • Write simple and complex .proto files
  • Practice Exercises to Confirm the Learnings
  • Leverage Imports and Packages appropriately
  • Generate Code using `protoc` in any language
  • Code in Java with Protocol Buffers
  • Understand how Data Evolution works for Protobuf
  • Learn about advanced Protocol Buffers concepts

Requirements

  • Some programming background (Java, Python or Go for example)

Description

Protocol Buffers (protobuf) is a fundamental data serialization format that every Data Engineer should know about.


In this course, we are going to explore in-depth, with hands-on lectures, all the aspects of Protocol Buffers 3. 

In just a few hours, you will know everything you need to know to create simple and complex .proto files, and write code in your Favourite Programming language such as Java, Python and Go. Protocol Buffers generates all the boilerplate code for you!

Stop using XML and JSON and start using a Data Format that will allow you to create the most efficient APIs. 

———————————

> Write simple and complex .proto files
> Practice Exercises to Confirm the learnings
> Leverage Imports and Packages appropriately
> Generate Code using `protoc`
> Code in Java with Protocol Buffers
> Learn about advanced Protocol Buffers concepts

Note: This course assumes you have some knowledge about Programming and JSON / XML



Section outline:

  • Protocol Buffers Basics I: Learn how to create your first messages using Scalar Types. Practice with 5 exercises
  • Protocol Buffers Basics II: Learn how to create complex messages, and organize your code in different files and packages. Practice with 4 exercises
  • Setting up Protoc Compiler: Setup the protoc compiler and learn how to generate code in any language
  • Java Programming with Protocol Buffers: Write your Protocol Buffers Data in Java
  • Golang Programming with Protocol Buffers: Write your Protocol Buffers Data in Golang
  • Data Evolution with Protobuf: Evolve your protocol buffers file in a safe way in order to add or remove fields without breaking previous code
  • Protocol Buffers Advanced: Advanced Types in Protocol Buffers as well as Options, Integer Types, and an introduction to RPC Services with gRPC

Learning and getting hands-on on Protocol Buffers helps you to enhance your career opportunities and helps to boost your income. An investment in your career is an investment in yourself.  Don’t procrastinate.



There is no time like the present to take charge of your career. Take your career to the next level by learning Protocol Buffers today!

Who this course is for:

  • Developers who want to understand how to write .proto files and write code to create Protocol Buffer data
  • Architects who want to understand how Protocol Buffers works and be useful for their solution architecture
  • Learn TestNG using IntelliJ IDEA Course
  • Last updated 3/2020

Complete Guide to Protocol Buffers 3 [Java, Golang, Python] Course Catalog




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Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site

Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site

Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras!

What you’ll learn

Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site

  • 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

Requirements

  • Know how to code in Python
  • Some math basics such as derivatives

Description

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 framework in a way that is easy to understand.





We’ll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0’s official API) to quickly and easily build models. In this course, we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • NumPy Crash Course
  • Pandas Data Analysis Crash Course
  • Data Visualization Crash Course
  • Neural Network Basics
  • TensorFlow Basics
  • Keras Syntax Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • GANs – Generative Adversarial Networks
  • Deploying TensorFlow into Production
  • and much more!





1. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.

TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance

Who this course is for:

Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site





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Content From: https://www.udemy.com/course/complete-tensorflow-2-and-keras-deep-learning-bootcamp/
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Advanced AI: Deep Reinforcement Learning in Python Course Site

Advanced AI: Deep Reinforcement Learning in Python Course Site

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

What you’ll learn

Advanced AI: Deep Reinforcement Learning in Python Course Site

  • 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

Requirements

  • Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
  • College-level math is helpful
  • Experience building machine learning models in Python and Numpy
  • Know how to build ANNs and CNNs using Theano or Tensorflow

Description

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





Reinforcement learning has been around since the 70s but none of this has been possible until now.

The world is changing at a very fast pace.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.

Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.

This is such a fascinating perspective, it can even make supervised/unsupervised machine learning and “data science” seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world?

While deep reinforcement learning and AI has a lot of potentials, it also carries with it a huge risk.

Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk.

One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we’ll be making heavy use of in this course.

It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments.



In this course, we’ll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:

  • CartPole
  • Mountain Car
  • Atari games

To train effective learning agents, we’ll need new techniques.

Thanks for reading, and I’ll see you in class!

Suggested Prerequisites:

  • College-level math is helpful (calculus, probability)
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations
  • Linear regression
  • Gradient descent
  • Know how to build ANNs and CNNs in Theano or TensorFlow
  • Markov Decision Processes (MDPs)
  • Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs




TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don’t, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Advanced AI: Deep Reinforcement Learning in Python Course Site





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Content From: https://www.udemy.com/course/deep-reinforcement-learning-in-python/