Algorithmic Problems & Neural Networks in Python

Algorithmic Problems & Neural Networks in Python
English | Size: 826.48 MB
Category: Tutorial


Learn the basic algorithmic methodologies from backtracking to dynamic programming: Sudoku, Knapsack problem
What you’ll learn

Understand backtracking
Understand dynamic programming
Solve problems from scratch
Implement feedforward neural networks from scratch [Read more…]

SKILLSHARE Modern Deep Convolutional Neural Networks with PyTorch

SKILLSHARE Modern Deep Convolutional Neural Networks with PyTorch
English | Size: 573.01 MB
Category: Tutorial


Dear friend, welcome to the course “Modern Deep Convolutional Neural Networks with PyTorch”! In this course, you will learn:

What are convolutional neural networks and why do people need them
How to efficiently train them
What is the best way to regularize and speed-up training of neural networks
How we can improve the prediction quality

Warmly welcome! [Read more…]

SKILLSHARE THE BASICS OF NEURAL NETWORK

SKILLSHARE THE BASICS OF NEURAL NETWORK
English | Size: 449.80 MB
Category: Tutorial


The Basics of Neural Networks. Neural neworks are typically organized in layers. … Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. [Read more…]

CS231n Convolutional Neural Networks (Deep Learning)

CS231n: Convolutional Neural Networks (Deep Learning)
English | Size: 4.75 GB
Category: tUTORIAL

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge. [Read more…]

Packt Publishing – Learning Neural Networks with Tensorflow

Packt Publishing – Learning Neural Networks with Tensorflow
English | Size: 725.19 MB
Category: CBTs

The video is packed with step-by-step instructions, working examples, and helpful advice about building your Neural Network with Tensorflow. You’ll learn to build your own network. This practical course is divided into clear byte-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

Neural Networks are used all around us: they index photos into categories, translate text, suggest replies for emails, and beat the best games. Many people are eager to apply this knowledge to their own data, but many fail to achieve the results they expect. [Read more…]

O’Reilly – Neural Networks

O’Reilly – Neural Networks
English | Size: 229.32 MB
Category: Tutorial

Neural networks form the foundation for deep learning, the most advanced and popular machine learning technique in use today. This course provides an introduction to neural networks. It begins with an overview of a neural network’s basic concepts and building blocks – neurons, weights, activations, and layers – before explaining how to train one using gradient descent. The optimization technique is explained with a visual example and different issues such as parameter initialization and model validation are discussed. The course covers the different types of neural network architectures, explains the differences between them, and illustrates practical applications for each. Because training a neural network can be very slow, the course will offer up some tricks for speeding up the process and improving results. The course ends with a review of the history of this fascinating field, from its origin to its fall, and then its subsequent rise in modern days. Requirements include a clear understanding of supervised learning and optimization. [Read more…]

O’Reilly – Understanding Convolutional Neural Networks (CNNs)

O’Reilly – Understanding Convolutional Neural Networks (CNNs)
English | Size: 991.69 MB
Category: Tutorial

Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. This course offers an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualization and image enhancement. The course covers concepts, processes, and technologies such as CNN layers and architectures. It also explains CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs). Learners who come to this course with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualizations.
[Read more…]