5. Youtube channels and Books
KrisNaik(deployment)
StatQuest
DeepLearning.TV
CodeEmporium
3blue1brown
Bob Trenwith
Data School
Cognitive Class
The Hundred-page Machine Learning Book
Hands on ML with scikit learn, keras and
tensorflow
Deep learning (Ian Goodfellow)
Refer Grouply.org
7. ML Algorithms
1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forest
5. Clustering (K means, Hierarchical)
8. Machine learning skills
1. Math skills
Probability and statistics
Linear Algebra
Calculus
2. Programming in Python/R
3. Data Engineer skills
1. Ability to work with large amounts of data
2. Data preprocessing
3. Knowledge of SQL and NoSQL
4. ETL (Extract Transform and Load) operations
5. Data Analysis and Visualization skills
20. Neural Network
‘Black box’ that takes inputs and predicts an output.
Trained using known (input,output) , approximates the function and maps new inputs
Learns the function mapping inputs to output by adjusting the internal parameters (weights)
26. RNN
RNNs are used when the inputs have some state information
Examples include time series and word sequences
Can captures the essence of the sequence in its hidden state (context)
https://towardsdatascience.com/illustrated-guide-to-recurrent-neural-networks-79e5eb8049c9
38. Encoder - Decoder summary
2014 -Google replaced their Statistical model with NMT
Due to its flexibility it is the goto framework for NLG with different models taking
roles of encoder and decoder
The decoder can not only be conditioned on a sequence but on arbitrary
representation enabling lot of use cases (like generating caption from image)
40. Translation issues (long term dependencies)
The model cannot remember enough
difficult to retain >30 words.
"Born in France..went to EPFL Switzerland..I speak fluent ..."
45. Machine translation
NN can encode words
RNN can encode sentences
Long sentences need changes to RNN architecture
Two RNNs can act as encoder and decoder (of any representation)
Encoding everything into single context loses information
Selectively pay attention to the inputs we need.
Get rid of RNN and use only attention mechanism. Make it parallelizable
Richer representation of inputs using Self-attention.
Use Encoder-Decoder attention as usual for translation