approach to machine learning.
Learn Python basics with notebooks.
Use data science libraries like
Implement basic ML models in
TensorFlow 2.0 + Keras
Create deep learning models for improved performance.
Setup your local environment for ML.
Wrap your ML in RESTful APIs using
to create applications.
Standardize and scale your ML applications with
Deploy simple and scalable ML workflows using
Dive into architectural and interpretable advancements in neural networks.
Implement state-of-the-art NLP techniques.
Learn about popular deep learning algorithms used for generation, time-series, etc.
(＊) - Checkout this
for why we switched from PyTorch to TensorFlow 2.0 + Keras.
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A practical approach to machine learning.