| 001. Part 1. Getting started.mp4 | 3.4 MB | ||
| 002. Chapter 1. Introducing evolutionary deep learning.mp4 | 30.5 MB | ||
| 003. Chapter 1. The why and where of evolutionary deep learning.mp4 | 3.6 MB | ||
| 004. Chapter 1. The need for deep learning optimization.mp4 | 13.3 MB | ||
| 005. Chapter 1. Automating optimization with automated machine learning.mp4 | 19.9 MB | ||
| 006. Chapter 1. Applications of evolutionary deep learning.mp4 | 16 MB | ||
| 007. Chapter 1. Summary.mp4 | 3.2 MB | ||
| 008. Chapter 2. Introducing evolutionary computation.mp4 | 13.5 MB | ||
| 009. Chapter 2. Simulating life with Python.mp4 | 10.2 MB | ||
| 010. Chapter 2. Life simulation as optimization.mp4 | 11.9 MB | ||
| 011. Chapter 2. Adding evolution to the life simulation.mp4 | 26 MB | ||
| 012. Chapter 2. Genetic algorithms in Python.mp4 | 48.4 MB | ||
| 013. Chapter 2. Summary.mp4 | 7.4 MB | ||
| 014. Chapter 3. Introducing genetic algorithms with DEAP.mp4 | 20.1 MB | ||
| 015. Chapter 3. Solving the Queen’s Gambit.mp4 | 15.1 MB | ||
| 016. Chapter 3. Helping a traveling salesman.mp4 | 23.5 MB | ||
| 017. Chapter 3. Selecting genetic operators for improved evolution.mp4 | 27.5 MB | ||
| 018. Chapter 3. Painting with the EvoLisa.mp4 | 19.5 MB | ||
| 019. Chapter 3. Summary.mp4 | 5.5 MB | ||
| 020. Chapter 4. More evolutionary computation with DEAP.mp4 | 35.6 MB | ||
| 021. Chapter 4. Particle swarm optimization with DEAP.mp4 | 18 MB | ||
| 022. Chapter 4. Coevolving solutions with DEAP.mp4 | 22.5 MB | ||
| 023. Chapter 4. Evolutionary strategies with DEAP.mp4 | 30.3 MB | ||
| 024. Chapter 4. Differential evolution with DEAP.mp4 | 19.4 MB | ||
| 025. Chapter 4. Summary.mp4 | 3.9 MB | ||
| 026. Part 2. Optimizing deep learning.mp4 | 2.4 MB | ||
| 027. Chapter 5. Automating hyperparameter optimization.mp4 | 36.7 MB | ||
| 028. Chapter 5. Automating HPO with random search.mp4 | 22.2 MB | ||
| 029. Chapter 5. Grid search and HPO.mp4 | 21.9 MB | ||
| 030. Chapter 5. Evolutionary computation for HPO.mp4 | 21.4 MB | ||
| 031. Chapter 5. Genetic algorithms and evolutionary strategies for HPO.mp4 | 20.4 MB | ||
| 032. Chapter 5. Differential evolution for HPO.mp4 | 14 MB | ||
| 033. Chapter 5. Summary.mp4 | 4.1 MB | ||
| 034. Chapter 6. Neuroevolution optimization.mp4 | 16.7 MB | ||
| 035. Chapter 6. Genetic algorithms as deep learning optimizers.mp4 | 13.7 MB | ||
| 036. Chapter 6. Other evolutionary methods for neurooptimization.mp4 | 7.4 MB | ||
| 037. Chapter 6. Applying neuroevolution optimization to Keras.mp4 | 13.3 MB | ||
| 038. Chapter 6. Understanding the limits of evolutionary optimization.mp4 | 9.5 MB | ||
| 039. Chapter 6. Summary.mp4 | 4.1 MB | ||
| 040. Chapter 7. Evolutionary convolutional neural networks.mp4 | 28.7 MB | ||
| 041. Chapter 7. Encoding a network architecture in genes.mp4 | 14.2 MB | ||
| 042. Chapter 7. Creating the mating crossover operation.mp4 | 10.2 MB | ||
| 043. Chapter 7. Developing a custom mutation operator.mp4 | 13 MB | ||
| 044. Chapter 7. Evolving convolutional network architecture.mp4 | 17.5 MB | ||
| 045. Chapter 7. Summary.mp4 | 3.7 MB | ||
| 046. Part 3. Advanced applications.mp4 | 4.9 MB | ||
| 047. Chapter 8. Evolving autoencoders.mp4 | 30.5 MB | ||
| 048. Chapter 8. Evolutionary AE optimization.mp4 | 17.6 MB | ||
| 049. Chapter 8. Mating and mutating the autoencoder gene sequence.mp4 | 9.8 MB | ||
| 050. Chapter 8. Evolving an autoencoder.mp4 | 9.7 MB | ||
| 051. Chapter 8. Building variational autoencoders.mp4 | 29.8 MB | ||
| 052. Chapter 8. Summary.mp4 | 5.1 MB | ||
| 053. Chapter 9. Generative deep learning and evolution.mp4 | 29.3 MB | ||
| 054. Chapter 9. The challenges of training a GAN.mp4 | 29.4 MB | ||
| 055. Chapter 9. Fixing GAN problems with Wasserstein loss.mp4 | 13.8 MB | ||
| 056. Chapter 9. Encoding the Wasserstein DCGAN for evolution.mp4 | 14.7 MB | ||
| 057. Chapter 9. Optimizing the DCGAN with genetic algorithms.mp4 | 11.2 MB | ||
| 058. Chapter 9. Summary.mp4 | 3.7 MB | ||
| 059. Chapter 10. NEAT NeuroEvolution of Augmenting Topologies.mp4 | 24.4 MB | ||
| 060. Chapter 10. Visualizing an evolved NEAT network.mp4 | 10.7 MB | ||
| 061. Chapter 10. Exercising the capabilities of NEAT.mp4 | 15.3 MB | ||
| 062. Chapter 10. Exercising NEAT to classify images.mp4 | 17.4 MB | ||
| 063. Chapter 10. Uncovering the role of speciation in evolving topologies.mp4 | 24.1 MB | ||
| 064. Chapter 10. Summary.mp4 | 3.3 MB | ||
| 065. Chapter 11. Evolutionary learning with NEAT.mp4 | 32.4 MB | ||
| 066. Chapter 11. Exploring complex problems from the OpenAI Gym.mp4 | 15.3 MB | ||
| 067. Chapter 11. Solving reinforcement learning problems with NEAT.mp4 | 14.7 MB | ||
| 068. Chapter 11. Solving Gym’s lunar lander problem with NEAT agents.mp4 | 16.5 MB | ||
| 069. Chapter 11. Solving Gym’s lunar lander problem with a deep Q-network.mp4 | 16.3 MB | ||
| 070. Chapter 11. Summary.mp4 | 3.6 MB | ||
| 071. Chapter 12. Evolutionary machine learning and beyond.mp4 | 25.1 MB | ||
| 072. Chapter 12. Revisiting reinforcement learning with Geppy.mp4 | 15.5 MB | ||
| 073. Chapter 12. Introducing instinctual learning.mp4 | 34.6 MB | ||
| 074. Chapter 12. Generalized learning with genetic programming.mp4 | 24.6 MB | ||
| 075. Chapter 12. The future of evolutionary machine learning.mp4 | 22.4 MB | ||
| 076. Chapter 12. Generalization with instinctual deep and deep reinforcement learning.mp4 | 19.8 MB | ||
| 077. Chapter 12. Summary.mp4 | 4 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 79 total files | |||
Evolutionary Deep Learning, Video Edition
https://FreeCourseWeb.com
Released 8/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 9h 39m | Size: 1.26 GB
Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment.
In Evolutionary Deep Learning you will learn how to
Solve complex design and analysis problems with evolutionary computation
Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization
Use unsupervised learning with a deep learning autoencoder to regenerate sample data
Understand the basics of reinforcement learning and the Q-Learning equation
Apply Q-Learning to deep learning to produce deep reinforcement learning
Optimize the loss function and network architecture of unsupervised autoencoders
Make an evolutionary agent that can play an OpenAI Gym game
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