PacktPub | Deep Learning with Real World Projects [Video] [FCO]
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Files
| 0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url | 409 B | ||
| 1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url | 307 B | ||
| 2. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, & more.etc.url | 204 B | ||
| 3. (FTUApps.com) Download Cracked Developers Applications For Free.url | 204 B | ||
| Activation Functions.mp4 | 41.9 MB | ||
| AlexNet.mp4 | 73.8 MB | ||
| Analysis - Part 1.mp4 | 109.8 MB | ||
| Analysis - Part 2.mp4 | 46 MB | ||
| Applications for CNN.mp4 | 54.5 MB | ||
| Architecture.mp4 | 22.5 MB | ||
| Assumptions in Neural Networks.mp4 | 45.8 MB | ||
| Back Propagation Training - Part 1.mp4 | 46.5 MB | ||
| Back Propagation Training - Part 10.mp4 | 23 MB | ||
| Back Propagation Training - Part 2.mp4 | 38.1 MB | ||
| Back Propagation Training - Part 3.mp4 | 14.7 MB | ||
| Back Propagation Training - Part 4.mp4 | 34.7 MB | ||
| Back Propagation Training - Part 5.mp4 | 34.8 MB | ||
| Back Propagation Training - Part 6.mp4 | 23.3 MB | ||
| Back Propagation Training - Part 7.mp4 | 20.6 MB | ||
| Back Propagation Training - Part 8.mp4 | 27 MB | ||
| Back Propagation Training - Part 9.mp4 | 29 MB | ||
| Batch Normalisation - Part 1.mp4 | 36.1 MB | ||
| Batch Normalisation - Part 2.mp4 | 37.5 MB | ||
| Batch Normalisation - Part 3.mp4 | 50.6 MB | ||
| Batch data.mp4 | 8.5 MB | ||
| Bidirectional RNN.mp4 | 43.5 MB | ||
| Case Study - Part 1.mp4 | 194.1 MB | ||
| Case Study - Part 2.mp4 | 95.9 MB | ||
| Case Study - Part 3.mp4 | 39.1 MB | ||
| Code Password.mp4 | 719.8 KB | ||
| Combining Network.mp4 | 53.2 MB | ||
| Convolution - Part 1.mp4 | 43.4 MB | ||
| Convolution - Part 2.mp4 | 79.5 MB | ||
| Deep Learning.mp4 | 29 MB | ||
| Dropouts Part 1.mp4 | 24.2 MB | ||
| Dropouts Part 2.mp4 | 13.5 MB | ||
| Example for Perceptron.mp4 | 45 MB | ||
| Feature Map.mp4 | 127.6 MB | ||
| Finding Global Minima.mp4 | 10.3 MB | ||
| Formulas.mp4 | 18.6 MB | ||
| Gated Recurrent Network (GRU).mp4 | 27 MB | ||
| GoogleNet.mp4 | 48.7 MB | ||
| History of Deep learning.mp4 | 81.4 MB | ||
| Homogeneous Co-ordinate.mp4 | 23.6 MB | ||
| How you can help our Group!.txt | 204 B | ||
| Idea behind CNN - Part 1.mp4 | 45.9 MB | ||
| Idea behind CNN - Part 2.mp4 | 75.9 MB | ||
| Images.mp4 | 161.1 MB | ||
| Input Layer.mp4 | 53.6 MB | ||
| Introducing Keras.mp4 | 122.9 MB | ||
| Introducing Loss Function.mp4 | 44.1 MB | ||
| Introducing TensorFlow.mp4 | 45.4 MB | ||
| Introduction to RNN.mp4 | 13.5 MB | ||
| Introduction to Regularisation.mp4 | 49 MB | ||
| Introduction.mp4 | 19.7 MB | ||
| LSTM - Part 1.mp4 | 7.7 MB | ||
| LSTM - Part 2.mp4 | 6 MB | ||
| LSTM - Part 3.mp4 | 4.2 MB | ||
| LSTM - Part 4.mp4 | 11.6 MB | ||
| LSTM - Part 5.mp4 | 19.7 MB | ||
| LSTM Equation.mp4 | 8.5 MB | ||
| Multi Classifier.mp4 | 37.9 MB | ||
| Multi-Level Perceptrons.mp4 | 81.5 MB | ||
| Neural Network Playground.mp4 | 152.1 MB | ||
| Neural Networks.mp4 | 49.3 MB | ||
| One-to-Many.mp4 | 17.4 MB | ||
| Online Offline Mode.mp4 | 36 MB | ||
| Output Layer.mp4 | 14.1 MB | ||
| Padding.mp4 | 15.1 MB | ||
| Part-Of-Speech Tagger case- study (Part-2).mp4 | 87.7 MB | ||
| Part-Of-Speech Tagger case- study (Part-3).mp4 | 42.6 MB | ||
| Part-Of-Speech Tagger case- study (Part-4).mp4 | 57.1 MB | ||
| Part-Of-Speech Tagger case- study (Part-5).mp4 | 109.4 MB | ||
| Part-Of-Speech Tagger case- study (Part-6).mp4 | 25.6 MB | ||
| Part-Of-Speech Tagger case- study (Part-7).mp4 | 60 MB | ||
| Part-Of-Speech Tagger case- study (Part-8).mp4 | 120 MB | ||
| Part-Of-Speech Tagger case- study (Part-9).mp4 | 30.9 MB | ||
| Part-Of-Speech Tagger case-study (Part-1).mp4 | 56.2 MB | ||
| Perceptron for Classifiers.mp4 | 32 MB | ||
| Perceptron in Depth.mp4 | 30.7 MB | ||
| Perceptron.mp4 | 29.7 MB | ||
| Perceptrons.mp4 | 37.7 MB | ||
| Pooling.mp4 | 70.6 MB | ||
| Practical on CNN - Case Study - Part 1.mp4 | 10.3 MB | ||
| Practical on CNN - Case Study - Part 2.mp4 | 28.3 MB | ||
| Practical on CNN - Case Study - Part 3.mp4 | 36 MB | ||
| Practical on CNN - Case Study - Part 4.mp4 | 13.2 MB | ||
| Practical on CNN - Case Study - Part 5.mp4 | 7.5 MB | ||
| Pseudocode for Batch.mp4 | 28.8 MB | ||
| Pseudocode.mp4 | 12.7 MB | ||
| RNN - Part 1.mp4 | 9.9 MB | ||
| RNN Formula.mp4 | 29.2 MB | ||
| RNN Part 2.mp4 | 6.4 MB | ||
| Representations.mp4 | 158.1 MB | ||
| ResNet - Part 1.mp4 | 32.3 MB | ||
| ResNet - Part 2.mp4 | 28.1 MB | ||
| SGD.mp4 | 39 MB | ||
| Sigmoid Function.mp4 | 26 MB | ||
| Sigmoid function.mp4 | 26.5 MB | ||
| Simplified Notations.mp4 | 29.4 MB | ||
| Stride and Padding.mp4 | 34.2 MB | ||
| Text Generation - Code generator case- study (Part-1).mp4 | 171.3 MB | ||
| Text Generation - Code generator case- study (Part-2).mp4 | 105.2 MB | ||
| Text Generation - Code generator case- study (Part-3).mp4 | 48.7 MB | ||
| Text Generation - Code generator case- study (Part-4).mp4 | 40 MB | ||
| Training Neural Network - Part 1.mp4 | 122.6 MB | ||
| Training Neural Network - Part 2.mp4 | 56.8 MB | ||
| Training Neural Network - Part 3.mp4 | 110.8 MB | ||
| Training RNN.mp4 | 11.6 MB | ||
| Training for Batches.mp4 | 22.8 MB | ||
| Training in Neural Networks.mp4 | 32.7 MB | ||
| Transfer Learning - Part 1.mp4 | 7.4 MB | ||
| Transfer Learning - Part 2.mp4 | 19.9 MB | ||
| Transfer Learning - Part 3.mp4 | 35.3 MB | ||
| Transfer Learning - Part 4.mp4 | 39.9 MB | ||
| Transfer Learning - Part 5.mp4 | 27.1 MB | ||
| Transfer Learning - Part 6.mp4 | 36.3 MB | ||
| Types of RNN - Part 1.mp4 | 7.4 MB | ||
| Types of RNN - Part 2.mp4 | 13.2 MB | ||
| Understanding Dimensions.mp4 | 58.6 MB | ||
| Understanding Human Brain.mp4 | 26.6 MB | ||
| Understanding MNIST.mp4 | 20.4 MB | ||
| Understanding Notations.mp4 | 100 MB | ||
| VGG16 (Visual Geometry Group).mp4 | 44.6 MB | ||
| Vanishing Gradient.mp4 | 21.4 MB | ||
| Vectorised Methods.mp4 | 81.4 MB | ||
| Video.mp4 | 40 MB | ||
| Weight and Bias.mp4 | 82.5 MB | ||
| Working with Flower Images - Case Study - Part 1.mp4 | 49.6 MB | ||
| Working with Flower Images - Case Study - Part 10.mp4 | 56.7 MB | ||
| Working with Flower Images - Case Study - Part 11.mp4 | 61.7 MB | ||
| Working with Flower Images - Case Study - Part 12.mp4 | 159.5 MB | ||
| Working with Flower Images - Case Study - Part 13.mp4 | 31.8 MB | ||
| Working with Flower Images - Case Study - Part 14.mp4 | 75.1 MB | ||
| Working with Flower Images - Case Study - Part 2.mp4 | 118.7 MB | ||
| Working with Flower Images - Case Study - Part 3.mp4 | 50.1 MB | ||
| Working with Flower Images - Case Study - Part 4.mp4 | 48.6 MB | ||
| Working with Flower Images - Case Study - Part 5.mp4 | 39.3 MB | ||
| Working with Flower Images - Case Study - Part 6.mp4 | 72.1 MB | ||
| Working with Flower Images - Case Study - Part 7.mp4 | 26.3 MB | ||
| Working with Flower Images - Case Study - Part 8.mp4 | 91.4 MB | ||
| Working with Flower Images - Case Study - Part 9.mp4 | 81.1 MB | ||
| Working with X-Ray images - Case Study - Part 1.mp4 | 9 MB | ||
| Working with X-Ray images - Case Study - Part 2.mp4 | 8.8 MB | ||
| Working with X-Ray images - Case Study - Part 3.mp4 | 14.1 MB | ||
| Working with X-Ray images - Case Study - Part 4.mp4 | 18.3 MB | ||
| Working with X-Ray images - Case Study - Part 5.mp4 | 26.6 MB | ||
| Working with X-Ray images - Case Study - Part 6.mp4 | 18.3 MB | ||
| ▲ 161 total files | |||
Description
Lynda and other Courses >>> https://www.freecoursesonline.me/
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Geekshub Pvt. Ltd.
July 12, 2019
20 hours 49 minutes
Source: https://www.packtpub.com/eu/data/deep-learning-with-real-world-projects-video
Novice to pro in Deep Learning with Hands-on Real-World Projects
Learn
Learn to create Deep Neural networks and machine learning models for complex real-world problems
Get comfortable with Deep Learning libraries like TensorFlow and Keras
Learn inner workings of Convolutional Networks and Computer Vision
Work with AlexNet, GoogleNet, and ResNet
Recurrent Neural Networks
About
Deep learning is an artificial intelligence function that mimics the inner workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks of interconnected nodes capable of un-supervised learning from data that is unstructured or unlabelled training data. It also enables representation of data in form of abstract features and classifies them into sub-classes which may be too complex for traditional machine learning models.
One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data. As more and more sources of data-generation are coming into picture, the number of file formats is increasing as well. Now, designing one model to merge data from these many sources and extract meaningful insights is not possible with traditional hand-coded programs. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach. While this may sound daunting, Deep Learning algorithms handle such tasks with ease. The scope of implementation in various sectors is just limitless.
Features
Learn to implement machine learning models in Tensor Flow and Keras
Complete introduction to advance level concepts in Recurrent Neural Networks (RNN)
A Practical course with multiple real-life projects in Deep learning
Course Length 20 hours 49 minutes
ISBN 9781838985721
Date Of Publication 12 Jul 2019
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