Deep Learning with Python and Keras
What you will learn
To describe what Deep Learning is in a simple yet accurate way
To explain how deep learning can be used to build predictive models
To distinguish which practical applications can benefit from deep learning
To install and use Python and Keras to build deep learning models
To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
To build, train and use fully connected, convolutional and recurrent neural networks
To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
To train and run models in the cloud using a GPU
To estimate training costs for large models
To re-use pre-trained models to shortcut training time and cost (transfer learning)
Course Gallery
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Comidoc Review
Our Verdict
<4–5 full lines (≈70-90 words)>Sure, here's the verdict in JSON format: \n{\n \"pros\":[\n 'Excellent explanations of complex Deep Learning concepts',\n 'Strong focus on code understanding and modification',\n 'Comprehensive coverage of neural network types, from CNN to autoencoders',\n 'Practical examples and exercises for theoretical concept reinforcement'\n ],\n \"cons\":[\n 'Some notebooks require updates with the latest libraries',\n 'Monotonous voice of the instructor lacking pauses for information digestion',\n 'Certain sections, particularly RNN parts, could benefit from more detailed explanations'\n ],\n \"finalThoughts\":\"This course provided me with a solid foundation in Deep Learning using Python and Keras. While some elements such as notebook clarity and the instructor's monotonous voice need improvement, the wealth of information on Deep Learning theory and practical examples makes it an invaluable resource for those looking to delve into advanced machine learning and data science concepts. Highly recommended.\"\n}
What We Liked
- Excellent explanations of complex Deep Learning concepts
- Strong focus on code understanding and modification
- Comprehensive coverage of neural network types, from CNN to autoencoders
- Practical examples and exercises for theoretical concept reinforcement
Potential Drawbacks
- Some notebooks require updates with the latest libraries
- Monotonous voice of the instructor lacking pauses for information digestion
- Certain sections, particularly RNN parts, could benefit from more detailed explanations