Neural Network Optimization

A detailed article that provides a crash course in the terminology and processes used in the field of multi-layer perceptron artificial neural networks. It covers neurons, weights, activations, and how networks of neurons are trained.

Unlock the boundless potential of data by enrolling in our comprehensive course, "Mastering Machine Learning, Data Science, Neural Networks, and Artificial Intelligence with Python and Libraries." This meticulously crafted program is designed to empower individuals with the skills and knowledge needed to navigate the dynamic landscape of modern technology.Course Overview:In this immersive learning journey, participants will delve into the core principles of Machine Learning, Data Science, Neural Networks, and Artificial Intelligence using Python as the primary programming language. The course is structured to cater to both beginners and intermediate learners, ensuring a gradual progression from fundamental concepts to advanced applications.Key Highlights:Foundations of Machine Learning:Gain a solid understanding of machine learning fundamentals, algorithms, and models.Explore supervised and unsupervised learning techniques.Master feature engineering, model evaluation, and hyperparameter tuning.Data Science Essentials:Learn the art of extracting valuable insights from data.Acquire proficiency in data manipulation, cleaning, and exploratory data analysis.Harness the power of statistical analysis for informed decision-making.Neural Networks and Deep Learning:Dive into the realm of neural networks and deep learning architectures.Understand the mechanics of artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).Implement state-of-the-art deep learning models using Python libraries.Artificial Intelligence (AI) Applications:Explore the practical applications of AI in various industries.Wor

This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! The course includes the following Sections:--------------------------------------------------------------------------------------------------------Section 1 - How Neural Networks and Backpropagation Works In this section, you will deeply understand the theories of how neural networks  and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages! Section 2 - Loss Functions In this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work. Section 3 - Optimization In this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMS Prop, Adam, AMS Grad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others. Section 4 - Weight Initialization In this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization. Section 5 - Regularization Techniques In this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout.

Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks.While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.There are hundreds of machine learning resources available on the internet. However, you're at risk of learning unnecessary lessons if you don't filter what you learn. While creating this course, we've helped with filtering to isolate the essential basics you'll need in your deep learning journey.It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.

كورس لتعليم اساسيات التعلم العميق والشبكات العصبية الالتفافية للمبتدئين وحتى المستوى المتقدمسواء كنت طالباً فى علوم الحاسب او طالباً  فى الهندسة أو مبرمجاً وتعشق مجال الذكاء الاصطناعى , فإن هذا الكورس سيساعدك علي فهم أساسيات التعلم الشبكات العصبيه الالتفافية و الوصول إلى مستوى محترف وسوف يركز هذا الكورس على الجوانب النظرية وراء الخوارزميات والنماذج المنتشره هذه الايام للتعلم العميقThis course is focus on the theoretical aspects of the recent convolutional neural network based methods.Section 1: Introduction to Convolutional Neural Network (CNNs)Lecture 1: Introduction to Deep Learning Lecture 2: Image Net Challenge Lecture 3: Drawbacks of Previous Neural Networks Lecture 4: CNNs Motivation & History Section 2: Convolutional Neural Network Properties Lecture 5: Local Connectivity Lecture 6: Parameter Sharing Lecture 7: Pooling & Subsampling Section 3: Convolution Operation Lecture 8: Definition of Convolution Lecture 9: Image Convolution Example Lecture 10: Other Filters Section 4: Convolutional Neural Network Layers Lecture 11: Convolutional Layer Lecture 12: Strided Convolution Lecture 13: Strided Convolution with Padding Lecture 14: Convolution over Volume Lecture 15: Activation Function (ReLU)Lecture 16: Pooling Layer Lecture 17: Convolutional Network Lecture 18: Batch Normalization Layer Section 5: Convolutional Neural Network Architectures Lecture 19: Introduction to CNNs Architectures Lecture 20: Le Net-5Lecture 21: Alex Net & ZF Net Lecture 22: VGG Net Lecture 23: Google Net (Inception Networ

Welcome to Supply Chain Analysis with Machine Learning & Neural Network course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualization on supply chain dataset. This course will be mainly focusing on performing cost optimization, demand forecasting, lead time efficiency, risk management, and order quantity optimization. We will be utilizing two different models, those are LightGBM which is a machine learning model and RNNs which stands for Recurrent Neural Networks. Regarding programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, Matplotlib for visualizing the data, and Scikit-Learn for implementing the machine learning models.Meanwhile, for the data, we are going to download the supply chain dataset from Kaggle. In the introduction session, you will learn basic fundamentals of supply chain analytics, such as getting to know its key objectives, getting to know models that will be used, and challenges that we commonly faced when it comes to analyzing supply chain data for example demand volatility and data integration. Then, you will continue by learning the basic mathematics and logics behind price and order quantity optimization where you will be guided step by step on how to solve a basic case study using economic order quantity equation. This session was designed to prepare your knowledge and understanding about order quantity optimization before implementing this concept to your code in the project. Afterward, you will learn about several different factors that can potentially cause supply chain disruption, such as natural disaster, economic volatility, and supplier issues. Once you’ve learnt all necessary knowledge about supply chain analytics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE, then, you will also learn how to

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and PyTorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you'll unravel the intricacies of CNNs architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNNs is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.Introducing our comprehensive deep CNNs with python course, where you'll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.In today's data-driven world, Convolutional Neural Networks  stand at the forefront of image rec

You're looking for a complete Artificial Neural Network (ANNs) course that teaches you everything you need to create a Neural Network model in Python, right?You've found the right Neural Networks course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python using Keras and TensorFlow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning concepts How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the 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 Deep learning techniqu

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up.In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLPs in MATLAB, in which, in addition to reviewing the theories related to MLPs neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

In this course, you'll be learning the fundamentals of deep neural networks and CNNs in depth.This course offers an extensive exploration of deep neural networks with a focus on Convolutional Neural Networks (CNNs).The course begins by delving into the fundamental concepts to provide a strong foundation for learners.Initial sections of the course include:Understanding what deep learning is and its significance in modern machine learning.Exploring the intricacies of neural networks, the building blocks of deep learning.Discovering where CNNs fit into the larger landscape of machine learning techniques.In-depth examination of the fundamentals of Perceptron Networks.Comprehensive exploration of Multilayer Perceptrons (MLPs).A detailed look into the mathematics behind feed forward networks.Understanding the significance of activation functions in neural networks.A major portion of the course is dedicated to Convolutional Neural Networks (CNNs):Exploring the architecture of CNNs.Investigating their applications, especially in image processing and computer vision.Understanding convolutional layers that extract relevant features from input data.Delving into pooling layers, which reduce spatial dimensions while retaining essential information.Examining fully connected layers for making predictions and decisions.Learning about design choices and hyperparameters influencing CNNs performance.The course also covers training and optimization of CNNs:Understanding loss functions and their role in training.Grasping the concept of backpropagation.Learning techniques to prevent overfitting.Introduction to optimization algorithms for fine-tuning C

In this course, we will implement a neural network from scratch, without dedicated libraries. Although we will use the python programming language, at the end of this course, you will be able to implement a neural network in any programming language. We will see how neural networks work intuitively, and then mathematically. We will also see some important tricks, which allow stabilizing the training of neural networks (log-sum-exp trick), and to prevent the memory used during training from growing exponentially (jacobian-vector product). Without these tricks, most neural networks could not be trained. We will train our neural networks on real image classification and regression problems. To do so, we will implement different cost functions, as well as several activation functions. This course is aimed at developers who would like to implement a neural network from scratch as well as those who want to understand how a neural network works from A to Z. This course is taught using the Python programming language and requires basic programming skills. If you do not have the required background, I recommend that you brush up on your programming skills by taking a crash course in programming. It is also recommended that you have some knowledge of Algebra and Analysis to get the most out of this course. Concepts covered : Neural networks Implementing neural networks from scratch Gradient descent and Jacobian matrix The creation of Modules that can be nested in order to create a complex neural architecture The log-sum-exp trick Jacobian vector product Activation functions (ReLU, Softmax, Log Softmax, ...) Cost functions (MSE Loss, NLL Loss, ...) This course will be frequently updated, with the addition of bonuses. Don't wait any longer before launching yourself i