curiousily/Deep-Learning-For-Hackers

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

1.1k 437 +0/wk

GitHub

anomaly-detection artificial-intelligence autoencoders bert deep-learning image-augmentation intent-recognition jupyter-notebooks keras lstms machine-learning neural-networks

Trend 0

Star & Fork Trend (35 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 1.1k

forks 437

Growth Velocity

curiousily/Deep-Learning-For-Hackers has +0 stars this period . 7-day velocity: -0.1%.

Deep analysis is being generated for this repository.

Signal-backed technical analysis will be available soon.

Metric	Deep-Learning-For-Hackers	UniRepLKNet	tab-transformer-pytorch	Inverse-Reinforcement-Learning
Stars	1.1k	1.1k	1.1k	1.1k
Forks	437	63	131	238
Weekly Growth	+0	+0	+0	+0
Language	Jupyter Notebook	Python	Python	Python
Sources	1	1	1	1
License	MIT	Apache-2.0	MIT	MIT

Capability Radar vs UniRepLKNet

Deep-Learning-For-Hackers

UniRepLKNet

Maintenance Activity 0

Last code push 2177 days ago.

Community Engagement 22

Fork-to-star ratio: 40.8%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 95

Licensed under MIT. Permissive — safe for commercial use.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.