PacktPub | Deep Learning with Real World Projects [Video] [FCO]

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1 - Introduction
  • Activation Functions.mp4 (151.3 MB)
  • Code Password.mp4 (719.8 KB)
  • History of Deep learning.mp4 (81.4 MB)
  • Introduction.mp4 (65.6 MB)
  • Multi-Level Perceptrons.mp4 (81.5 MB)
  • Neural Network Playground.mp4 (152.1 MB)
  • Perceptrons.mp4 (37.7 MB)
  • Representations.mp4 (158.1 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)
10 - CNN-Industry Live Project - Find..Save Life
  • Introduction.mp4 (10.9 MB)
  • Working with X-Ray images - Case Study - Part 1.mp4 (9.0 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)
11 - Recurrent Neural Networks - Introduction
  • Architecture.mp4 (22.5 MB)
  • Batch data.mp4 (8.5 MB)
  • Introduction to RNN.mp4 (13.5 MB)
  • One-to-Many.mp4 (17.4 MB)
  • RNN - Part 1.mp4 (9.9 MB)
  • RNN Formula.mp4 (29.2 MB)
  • RNN Part 2.mp4 (6.4 MB)
  • Simplified Notations.mp4 (29.4 MB)
  • Training RNN.mp4 (11.6 MB)
  • Types of RNN - Part 1.mp4 (7.4 MB)
  • Types of RNN - Part 2.mp4 (13.2 MB)
  • Vanishing Gradient.mp4 (21.4 MB)
12 - Recurrent Neural Networks - LSTM
  • Bidirectional RNN.mp4 (14.3 MB)
  • Gated Recurrent Network (GRU).mp4 (27.0 MB)
  • Introduction.mp4 (4.4 MB)
  • LSTM - Part 1.mp4 (7.7 MB)
  • LSTM - Part 2.mp4 (6.0 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)
  • Online Offline Mode.mp4 (10.3 MB)
13 - Recurrent Neutral Networks - Part-Of-Speech Tagger
  • 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 (60.0 MB)
  • Part-Of-Speech Tagger case- study (Part-8).mp4 (120.0 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)
14 - Text generation using RNN
  • 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 (40.0 MB)
2 - Artificial Neural Networks-Introduction
  • Activation Functions.mp4 (41.9 MB)
  • Assumptions in Neural Networks.mp4 (45.8 MB)
  • Deep Learning.mp4 (29.0 MB)
  • Example for Perceptron.mp4 (45.0 MB)
  • Homogeneous Co-ordinate.mp4 (23.6 MB)
  • Input Layer.mp4 (53.6 MB)
  • Introduction.mp4 (23.9 MB)
  • Multi Classifier.mp4 (37.9 MB)
  • Neural Networks.mp4 (49.3 MB)
  • Output Layer.mp4 (14.1 MB)
  • Perceptron for Classifiers.mp4 (32.0 MB)
  • Perceptron in Depth.mp4 (30.7 MB)
  • Perceptron.mp4 (29.7 MB)
  • Sigmoid function.mp4 (26.5 MB)
  • Training in Neural Networks.mp4 (32.7 MB)
  • Understanding Human Brain.mp4 (26.6 MB)
  • Understanding MNIST.mp4 (20.4 MB)
  • Understanding Notations.mp4 (100.0 MB)
3 - ANN - Feed Forward Network
  • Bidirectional RNN.mp4 (43.5 MB)
  • Introduction.mp4 (50.5 MB)
  • Online Offline Mode.mp4 (36.0 MB)
  • Pseudocode for Batch.mp4 (28.8 MB)
  • Pseudocode.mp4 (40.6 MB)
  • Understanding Dimensions.mp4 (58.6 MB)
  • Vectorised Methods.mp4 (81.4 MB)
4 - Back Propagation
  • Back Propagation Training - Part 1.mp4 (46.5 MB)
  • Back Propagation Training - Part 10.mp4 (23.0 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 (27.0 MB)
  • Back Propagation Training - Part 9.mp4 (29.0 MB)
  • Finding Global Minima.mp4 (10.3 MB)
  • Introducing Loss Function.mp4 (44.1 MB)
  • Introduction.mp4 (35.4 MB)
  • Pseudocode.mp4 (12.7 MB)
  • SGD.mp4 (39.0 MB)
  • Sigmoid Function.mp4 (26.0 MB)
  • Training for Batches.mp4 (22.8 MB)
5 - Regularisation
  • Batch Normalisation - Part 1.mp4 (36.1 MB)
  • Batch Normalisation - Part 2.mp4 (37.5 MB)
  • Batch Normalisation - Part 3.mp4 (50.6 MB)
  • Dropouts Part 1.mp4 (24.2 MB)
  • Dropouts Part 2.mp4 (13.5 MB)
  • Introd

Description

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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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PacktPub | Deep Learning with Real World Projects [Video] [FCO]

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7 GB
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PacktPub | Deep Learning with Real World Projects [Video] [FCO]

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