Oreilly – Deep Learning with Python, Second Edition, Video Edition 2021-4

Oreilly – Deep Learning with Python, Second Edition, Video Edition 2021-4 Downloadly IRSpace

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Oreilly – Deep Learning with Python, Second Edition, Video Edition 2021-4

Deep Learning with Python Second Edition Video Edition course. This video tutorial presents the book “Deep Learning with Python, Second Edition” in video and audio format. In this course, the speaker reads the book while the content, images, codes, diagrams and text are displayed on the screen. This course is like an audio book that you can also watch as a video.

This course will help you become familiar with the significant advances in deep learning. Written by the creator of the Keras library, this course teaches you practical deep learning techniques with Python that are easily applicable in the real world. In this course, complex concepts are explained in simple language and you will be taught through practical Python codes. Also, valuable insights into different model architectures and teaching tips are provided.

What you will learn

Deep learning from the basics
Image classification and segmentation
Time series forecasting
Text classification and machine translation
Text production, neural style transfer and image production

This course is suitable for people who

They are proficient in Python programming language.
No previous experience in Keras, TensorFlow or machine learning.

Course details

Publisher: Oreilly
Author: Francois Chollet
Training level: beginner to advanced
Training duration: 15 hours and 2 minutes
Number of courses: 96

Course headings

Chapter 1 What is deep learning?
Chapter 1 Learning rules and representations from data
Chapter 1 Understanding how deep learning works, in three figures
Chapter 1 Before deep learning: A brief history of machine learning
Chapter 1 Back to neural networks
Chapter 1 Why deep learning? Why now?
Chapter 1 Algorithms
Chapter 2 The mathematical building blocks of neural networks
Chapter 2 Data representations for neural networks
Chapter 2 Real-world examples of data tensors
Chapter 2 The gears of neural networks: Tensor operations
Chapter 2 Tensor reshaping
Chapter 2 The engine of neural networks: Gradient-based optimization
Chapter 2 Derivative of a tensor operation: The gradient
Chapter 2 Chaining derivatives: The Backpropagation algorithm
Chapter 2 Looking back at our first example
Chapter 3 Introduction to Keras and TensorFlow
Chapter 3 Setting up a deep learning workspace
Chapter 3 First steps with TensorFlow
Chapter 3 Anatomy of a neural network: Understanding core Keras APIs
Chapter 3 The “compile” step: Configuring the learning process
Chapter 4 Getting started with neural networks: Classification and regression
Chapter 4 Building your model
Chapter 4 Classifying newswires: A multiclass classification example
Chapter 4 Predicting house prices: A regression example
Chapter 5 Fundamentals of machine learning
Chapter 5 The nature of generalization in deep learning
Chapter 5 Evaluating machine learning models
Chapter 5 Improving model fit
Chapter 5 Improving generalization
Chapter 5 Regularizing your model
Chapter 6 The universal workflow of machine learning
Chapter 6 Collect a dataset
Chapter 6 Develop a model
Chapter 6 Beat a baseline
Chapter 6 Deploy the model
Chapter 6 Monitor your model in the wild
Chapter 7 Working with Keras: A deep dive
Chapter 7 Subclassing the Model class
Chapter 7 Using built-in training and evaluation loops
Chapter 7 Writing your own training and evaluation loops
Chapter 7 Make it fast with tf.function
Chapter 8 Introduction to deep learning for computer vision
Chapter 8 The convolution operation
Chapter 8 Training a convnet from scratch on a small dataset
Chapter 8 Data preprocessing
Chapter 8 Leveraging a pretrained model
Chapter 8 Feature extraction with a pretrained model
Chapter 9 Advanced deep learning for computer vision
Chapter 9 Modern convnet architecture patterns
Chapter 9 Residual connections
Chapter 9 Depthwise separable convolutions
Chapter 9 Interpreting what convnets learn
Chapter 9 Visualizing convnet filters
Chapter 9 Visualizing heatmaps of class activation
Chapter 10 Deep learning for timeseries
Chapter 10 Preparing the data
Chapter 10 Let’s try a basic machine learning model
Chapter 10 Understanding recurrent neural networks
Chapter 10 A recurrent layer in Keras
Chapter 10 Advanced use of recurrent neural networks
Chapter 10 Using bidirectional RNNs
Chapter 11 Deep learning for text
Chapter 11 Preparing text data
Chapter 11 Vocabulary indexing
Chapter 11 Two approaches for representing groups of words: Sets and sequences
Chapter 11 Processing words as a sequence: The sequence model approach, Part 1
Chapter 11 Processing words as a sequence: The sequence model approach, Part 2
Chapter 11 The Transformer architecture
Chapter 11 The Transformer encoder
Chapter 11 Beyond text classification: Sequence-to-sequence learning
Chapter 11 Sequence-to-sequence learning with Transformer
Chapter 12 Generative deep learning
Chapter 12 How do you generate sequence data?
Chapter 12 A text-generation callback with variable-temperature sampling
Chapter 12 DeepDream
Chapter 12 Neural style transfer
Chapter 12 Generating images with variational autoencoders
Chapter 12 Implementing a VAE with Keras
Chapter 12 A bag of tricks
Chapter 13 Best practices for the real world
Chapter 13 Hyperparameter optimization
Chapter 13 Scaling-up model training
Chapter 13 Multi-GPU training
Chapter 13 TPU training
Chapter 14 Conclusions
Chapter 14 Key enabling technologies
Chapter 14 Key network architectures
Chapter 14 The limitations of deep learning
Chapter 14 Local generalization vs. extreme generalization
Chapter 14 The purpose of intelligence
Chapter 14 Setting the course toward greater generality in AI
Chapter 14 Implementing intelligence: The missing ingredients
Chapter 14 The missing half of the picture
Chapter 14 Blending together deep learning and program synthesis
Chapter 14 Lifelong learning and modular subroutine reuse

Images of Deep Learning with Python Second Edition Video Edition course

Deep Learning with Python Second Edition Video Edition

Sample video of the course

Installation guide

After Extract, view with your favorite Player.

Subtitle: None

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 546 MB

File(s) password: www.downloadly.irFile size

1.5 GB

Oreilly – Deep Learning with Python, Second Edition, Video Edition 2021-4