In this course you will learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python.

In this course you will learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python*

You’ll learn the following:

⌨️ RNNs and LSTMs

⌨️ Sequence Modeling

⌨️ PyTorch

⌨️ Building a Chatbot in PyTorch

⭐️Requirements ⭐️

⌨️ Some Basic High School Mathematics

⌨️ Some Basic Programming Knowledge

⌨️ Some basic Knowledge about Neural Networks

⭐️Contents ⭐️

⌨️ (0:00:08) Recurrent Nerual Networks - RNNs and LSTMs

⌨️ (0:35:54) Sequence-To-Sequence Models

⌨️ (0:44:31) Attention Mechanisms

⌨️ (0:57:17) Introduction to PyTorch

⌨️ (1:10:02) PyTorch Tensors

⌨️ (1:28:03) Chatbot: Processing the Dataset

⌨️ (2:48:12) Chatbot: Data Preperation

⌨️ (3:33:26) Chatbot: Building the Model

⌨️ (4:53:42) Chatbot: Training the Model

🔗PyTorch Chatbot tutorial: https://pytorch.org/tutorials/beginner/chatbot_tutorial.html

Original video source: https://www.youtube.com/watch?v=CNuI8OWsppg

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Implementing Deep Learning Papers - Deep Deterministic Policy Gradients (using Python)

In this intermediate deep learning tutorial, you will learn how to go from reading a paper on deep deterministic policy gradients to implementing the concepts in Tensorflow. This process can be applied to any deep learning paper, not just deep reinforcement learning.

In the second part, you will learn how to code a deep deterministic policy gradient (DDPG) agent using Python and PyTorch, to beat the continuous lunar lander environment (a classic machine learning problem).

DDPG combines the best of Deep Q Learning and Actor Critic Methods into an algorithm that can solve environments with continuous action spaces. We will have an actor network that learns the (deterministic) policy, coupled with a critic network to learn the action-value functions. We will make use of a replay buffer to maximize sample efficiency, as well as target networks to assist in algorithm convergence and stability.

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☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Python for Data Science and Machine Learning Bootcamp

☞ Machine Learning, Data Science and Deep Learning with Python

☞ Deep Learning A-Z™: Hands-On Artificial Neural Networks

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Basic programming concept in any language will help but not require to attend this tutorial

Description

Become a Python Programmer and learn one of employer's most requested skills of 21st century!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)

With over 40 lectures and more than 3 hours of video this comprehensive course leaves no stone unturned! This course includes tests, and homework assignments as well as 3 major projects to create a Python project portfolio!

This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!

We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we've got you covered!

We cover a wide variety of topics, including:

Command Line Basics

Installing Python

Running Python Code

Strings

Lists

Dictionaries

Tuples

Sets

Number Data Types

Print Formatting

Functions

Scope

Built-in Functions

Debugging and Error Handling

Modules

External Modules

Object Oriented Programming

Inheritance

Polymorphism

File I/O

Web scrapping

Database Connection

Email sending

and much more!

Project that we will complete:

Guess the number

Guess the word using speech recognition

Love Calculator

google search in python

Image download from a link

Click and save image using openCV

Ludo game dice simulator

open wikipedia on command prompt

Password generator

QR code reader and generator

You will get lifetime access to over 40 lectures.

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Basic knowledge

Basic programming concept in any language will help but not require to attend this tutorial

What will you learn

Learn to use Python professionally, learning both Python 2 and Python 3!

Create games with Python, like Tic Tac Toe and Blackjack!

Learn advanced Python features, like the collections module and how to work with timestamps!

Learn to use Object Oriented Programming with classes!

Understand complex topics, like decorators.

Understand how to use both the pycharm and create .py files

Get an understanding of how to create GUIs in the pycharm!

Build a complete understanding of Python from the ground up!

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Machine Learning, Data Science and Deep Learning with PythonExplore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:

• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)

• The History of Artificial Neural Networks

• Deep Learning in the Tensorflow Playground

• Deep Learning Details

• Introducing Tensorflow

• Using Tensorflow

• Introducing Keras

• Using Keras to Predict Political Parties

• Convolutional Neural Networks (CNNs)

• Using CNNs for Handwriting Recognition

• Recurrent Neural Networks (RNNs)

• Using a RNN for Sentiment Analysis

• The Ethics of Deep Learning

• Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.