1, Deep Learning Prerequisites: Linear Regression in Python
This couse learn how learn linear regression from scratch and build your own working program in Python for data analysis.
- Derive and solve a linear regression model, and apply it appropriately to data science problems.
- Program your own version of a linear regression model in Python
Price: 120$
Rating: 4.6
Visit course: https://www.udemy.com/data-science-linear-regression-in-python/
2. Data Science: Deep Learning in Python
A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.
- Code a neural network from scratch in Python and numpy
- Code a neural network using Google’s TensorFlow
- Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
- Describe different types of neural networks and the different types of problems they are used for
- Derive the backpropagation rule from first principles
- Create a neural network with an output that has K > 2 classes using softmax
- Install TensorFlow
Level: Intermediate
Price: 120$
Rating: 4.6
Visit course: https://www.udemy.com/data-science-deep-learning-in-python/
3. Deep Learning Prerequisites: Logistic Regression in Python
Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python
- Program logistic regression from scratch in Python
- Describe how logistic regression is useful in data science
- Derive the error and update rule for logistic regression
- Understand how logistic regression works as an analogy for the biological neuron
- Use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
Level: All Level
Price: 120$
Rating: 4.6
Visit course: https://www.udemy.com/data-science-logistic-regression-in-python/
4. Deep Learning: Recurrent Neural Networks in Python
Target: GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences.
- To understand the simple recurrent unit (Elman unit)
- To understand the GRU (gated recurrent unit)
- To understand the LSTM (long short-term memory unit)
- Write various recurrent networks in Theano
- Understand backpropagation through time
- Understand how to mitigate the vanishing gradient problem
- Solve the XOR and parity problems using a recurrent neural network
- Use recurrent neural networks for language modeling
- Use RNNs for generating text, like poetry
- Visualize word embeddings and look for patterns in word vector representations
Level: All Level
Price: 120$
Rating: 4.6
Visit course: https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/
5. Unsupervised Deep Learning in Python
Purpose: Autoencoders + Restricted Boltzmann Machines for Deep Neural Networks in Theano, + t-SNE and PCA.
- Understand the theory behind principal components analysis (PCA)
- Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
- Derive the PCA algorithm by hand
- Write the code for PCA
- Understand the theory behind t-SNE
- Use t-SNE in code
- Understand the limitations of PCA and t-SNE
- Understand the theory behind autoencoders
- Write an autoencoder in Theano
- Understand how stacked autoencoders are used in deep learning
- Write a stacked denoising autoencoder in Theano
- Understand the theory behind restricted Boltzmann machines (RBMs)
- Understand why RBMs are hard to train
- Understand the contrastive divergence algorithm to train RBMs
- Write your own RBM and deep belief network (DBN) in Theano
- Visualize and interpret the features learned by autoencoders and RBMs
Level: All Level
Price: 120$
Rating: 4.6
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