## Deep Learning with PyTorch for Beginners – Part 1

Introduction to Machine Learning and Deep Learning

Linear Regression with PyTorch

Working with Image Data in PyTorch

Image Classification using Convolutional Neural Networks

Residual Networks, Data Augmentation and Regularization Techniques

## Requirements

• Basic Linear Algebra (matrix multiplication)
• Basic Python Programming
• Basic Calculus (Derivatives)

## Description

“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.

Topics Covered:

1. Introduction to Machine Learning & Deep Learning
2. Introduction on how to use Jovian platform
3. Introduction to PyTorch: Tensors & Gradients
4. Interoperability with Numpy
5. Linear Regression with PyTorch
– System setup
– Training data
– Linear Regression from scratch
– Loss function
– Train for multiple epochs
– Linear Regression using PyTorch built-ins
– Using nn.Linear
– Loss Function
– Optimizer
– Train the model
– Commit and update the notebook
7. Sharing Jupyter notebooks online with Jovian

## Who this course is for:

• Beginner Python developers curious about Deep Learning and PyTorch

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## Data Science: Intro To Deep Learning With Python In 2021

Understand the intuition behind Artificial Neural Networks

Build artificial neural networks with Tensorflow

Classify images, data using deep learning

Apply Convolutional Neural Networks in practice

## Requirements

• Some prior coding experience with python is required.

## Description

Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.

Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.

There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.

An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.

Module 1

1. Introduction to Deep Learning and TensorFlow

2. Basics of Neural Networks

3. Designing a shallow neural network (Scratch and python) (Project)

4. Deeper neural network using TensorFlow. (Project)

## Who this course is for:

• Beginners In Python
• Beginners In Deep Learning
• Beginners In Machine Learning

## Course content

Neural Network Fundamentals

## Requirements

• Be able to code in Python with NumPy and Pandas

## Description

Wanna understand deep learning and neural networks so well, you could code them from scratch? In this course, we’ll do exactly that.

The course starts by motivating and explaining perceptrons, and then gradually works its way toward deriving and coding a multiclass neural network with stochastic gradient descent that can recognize hand-written digits from the famous MNIST dataset.

Course Goals

This course is all about understanding the fundamentals of neural networks. So, it does not discuss TensorFlow, PyTorch, or any other neural network libraries. However, by the end of this course, you should understand neural networks so well that learning TensorFlow and PyTorch should be a breeze!

Challenges

In this course, I present a number of coding challenges inside the video lectures. The general approach is, we’ll discuss an idea and the theory behind it, and then you’re challenged to implement the idea / algorithm in Python. I’ll discuss my solution to every challenge, and my code is readily available on github.

Prerequisites

In this course, we’ll be using Python, NumPy, Pandas, and good bit of calculus. ..but don’t let the math scare you. I explain everything in great detail with examples and visuals.

If you’re rusty on your NumPy or Pandas, check out my free courses Python NumPy For Your Grandma and Python Pandas For Your Grandpa.

## Who this course is for:

• People interested in learning how neural networks work

## Deep learning: An Image Classification Bootcamp

Basics of Image Processing for deep learning using tensorflow

Image Classification

Tensorflow

Machine Learning

Deep Learning

Neural Networks

## Requirements

• Basics of Python 3 programming

## Description

Want to dive into Deep Learning and can’t find a simple yet comprehensive course?

Don’t worry you have come to the right place.

We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.

This class contains fundamentals of Image Classification with Tensorflow.

This course will teach you everything you need to get started.

## Who this course is for:

• Data Scientists
• If you have some Knowledge about Python and want to explore Deep learning
• Beginner python developer curious about Data Science

## Basics of Deep Learning

Basics of Deep Learning

This is an introductory course on Deep Learning. The students will get to know the evolution of deep neural network and their application in areas like image recognition, natural language processing etc.

## Requirements

• Basic Mathematics

## Description

Have you ever wondered what is Deep Learning and how it is helping today in powering Artificial Intelligence?

This basic course in Deep Learning may unravel some of them. You dont need any technical or coding background to know the basic fundamentals of Neural Network. This course is designed for functional consultants, product managers as well as developers and architects.

Contents of the course:

1. Inspiration for Deep Learning

2. Key Concepts of Deep Learning

3. Improving the model

4. Convolutional network

5. Recurrent network

6. Word representation

## Applied Deep Learning: Build a Chatbot – Theory, Application

Understand the theory behind Sequence Modeling

Understand the theory of how Chatbots work

Undertand the theory of how RNNs and LSTMs work

Get Introduced to PyTorch

Implement a Chatbot in PyTorch

Undertand the theory of different Sequence Modeling Applications

## Requirements

• Some Basic High School Mathematics
• Some Basic Programming Knowledge
• Some basic Knowledge about Neural Networks

## Description

In this course, you’ll learn the following:

• RNNs and LSTMs
• Sequence Modeling
• PyTorch
• Building a Chatbot in PyTorch

We will first cover the theoretical concepts you need to know for building a Chatbot, which include RNNs, LSTMS and Sequence Models with Attention.

Then we will introduce you to PyTorch, a very powerful and advanced deep learning Library. We will show you how to install it and how to work with it and with PyTorch Tensors.

Then we will build our Chatbot in PyTorch!

Please Note an important thing: If you don’t have prior knowledge on Neural Networks and how they work, you won’t be able to cope well with this course. Please note that this is not a Deep Learning course, it’s an Application of Deep Learning, as the course names implies (Applied Deep Learning: Build a Chatbot). The course level is Intermediate, and not Beginner. So please familiarize yourself with Neural Networks and it’s concepts before taking this course.  If you are already familiar, then your ready to start this journey!

## Who this course is for:

• Anybody enthusiastic about Deep Learning Applications

## Machine Intelligence – an Introductory Course

Machine Learning algorithms

Artificial Intelligence algorithms

Information Retrieval algorithms

Deep Learning algorithms

Quantum Computing algorithms

Computer Vision algorithms

Natural Language Processing algorithms

## Requirements

• Understanding of Calculus and Linear Algebra will help better understand most of the concepts discussed here. But you can look for helpful resources alongside studying this course.

## Description

This course focuses on the theoretical aspects of the field of Data Science and Machine Learning. It helps the students to quickly gain an in-depth overview of different algorithmic techniques used in various domains and applications. This course features external links to further enhance the experience and reinforce the concepts acquired. It also provides easy explanations of popular and useful research papers that are driving this field forward.

## Who this course is for:

• Aspiring and Professional Data Scientists and Machine Learning Engineers.
• Students pursuing their PhD and looking for a refresher course.

## Deep Learning Prerequisites: The Numpy Stack in Python V2

Basic operations in Numpy, Scipy, Pandas, and Matplotlib

Vector, Matrix, and Tensor manipulation

Visualizing data

## Requirements

• Linear Algebra, Probability, and Python Programming

## Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).

The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.

As I’ve always said: “If you can’t implement it, then you don’t understand it”.

Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.

This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Suggested Prerequisites:

• linear algebra
• probability
• Python programming

## Who this course is for:

• Anyone who wants to implement Machine Learning algorithms

## Learn Basic of Emerging Trends in Computer

Emerging Trends in Computer & Information technology Field

## Requirements

• Be able to identify the concepts of Computer Sciences.

## Description

The aim of this course is to  help students to attain the industry identified competency through various teaching learning experience: acquire knowledge of emerging trends. Advancements and applications of Computer Engineering and Information Technology are ever changing. Emerging trends aims at creating awareness about major trends that will define technological disruption in the  upcoming years in the field of Computer Engineering and Information Technology. These are some emerging areas expected to generated revenue, increasing demand as IT professionals and open avenues of entrepreneurship. The Objectives of the course are Differentiate between Machine Learning & Deep Learning, State IoT issues & Challenges in deployment, Describe the given model of Digital Forensics Investigation, Describe the given evidence handling, Describe the need to hack your own systems, Describe Database Vulnerabilities. The outcomes of the course are Describe Artificial Intelligence, Machine Learning & Deep Learning: Describe the concept of AI, State the components of AI, Differentiate between Machine Learning & Deep Learning, Interpret IoT Concepts: Describe IoT Systems in which information and knowledge are inferred from data, State IoT issues and challenges in deployment, Compare Model of Digital Forensic Investigation: Describe the given model of Digital Forensics Investigation, State the ethical and unethical issues in Digital Forensics, Describe Evidence Handling Procedures: List the rules of digital evidence, Describe the given evidence handling procedures, Describe Ethical Hacking Process: Describe the need to hack your own system, Detect Network, Operating System & Application vulnerabilities: Network Infrastructure vulnerabilities (Wired/Wireless),Describe Messaging Systems vulnerabilities.

## Who this course is for:

• Beginners of Software developers, Under graduates in Computer Science

## Artificial Neural Network for Regression

How to implement an Artificial Neural Network in Python

How to do Regression

## Requirements

• Deep Learning Basics

## Description

Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.

In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.

The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.

Go hands-on with Hadelin in solving this complex, real-world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will supercharge your Machine Learning toolkit.

Check out what’s in store for you when you enroll:

Part 1: Data Preprocessing

• Importing the dataset
• Splitting the dataset into the training set and test set

Part 2: Building an ANN

• Initializing the ANN
• Adding the input layer and the first hidden layer
• Compiling the ANN

Part 3: Training the ANN

• Training the ANN model on the training set
• Predicting the results of the test set