Introduction to Machine Learning and Deep Learning
PyTorch Basics: Tensors & Gradients
Linear Regression with PyTorch
Working with Image Data in PyTorch
Image Classification using Convolutional Neural Networks
Residual Networks, Data Augmentation and Regularization Techniques
Generative Adverserial Networks
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 – Compute gradients – Adjust weights and biases using gradient descent – Train for multiple epochs – Linear Regression using PyTorch built-ins – Dataset and DataLoader – 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
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)
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
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
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
Who this course is for:
All Developers, Business Managers, Functional leads, AI enthusiasts
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
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.
Basic operations in Numpy, Scipy, Pandas, and Matplotlib
Vector, Matrix, and Tensor manipulation
Visualizing data
Reading, writing, and manipulating DataFrames
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
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 areDifferentiate 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
How to implement an Artificial Neural Network in Python
How to do Regression
How to use Google Colab
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
Adding the output layer
Compiling the ANN
Part 3: Training the ANN
Training the ANN model on the training set
Predicting the results of the test set
More about Combined-Cycle Power Plants
A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
Who this course is for:
Anyone interested in Machine Learning and Deep Learning
