Update render script and Makefile

terminal/AIbooks

@@ -1,4 +1,4 @@
-                                                                                                 Awesome AI books
+                                                                                       Awesome AI books
 
 Some awesome AI related books and pdfs for downloading and learning.
 
@@ -48,7 +48,8 @@
 
 Training ground
 
-- OpenAI Gym (https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari (https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo etc...)
+- OpenAI Gym (https://gym.openai.com/): A toolkit for developing and comparing reinforcement learning algorithms. (Can play with Atari (https://en.wikipedia.org/wiki/Atari), Box2d, MuJoCo 
+etc...)
 - malmo (https://github.com/Microsoft/malmo): Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. 
 - DeepMind Pysc2 (https://github.com/deepmind/pysc2): StarCraft II Learning Environment.
 - Procgen (https://github.com/openai/procgen): Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments.
@@ -66,8 +67,8 @@
 - gym-sokoban (https://github.com/mpSchrader/gym-sokoban): Sokoban is Japanese for warehouse keeper and a traditional video game
 - Pommerman (https://github.com/MultiAgentLearning/playground) Playground hosts Pommerman, a clone of Bomberman built for AI research.
 - gym-miniworld (https://github.com/maximecb/gym-miniworld#introduction) MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research
-- vizdoomgym (https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for ViZDoom (https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from Raw Visual 
-Information) enviroments.
+- vizdoomgym (https://github.com/shakenes/vizdoomgym) OpenAI Gym wrapper for ViZDoom (https://github.com/mwydmuch/ViZDoom) (A Doom-based AI Research Platform for Reinforcement Learning from 
+Raw Visual Information) enviroments.
 - ddz-ai (https://github.com/freefuiiismyname/ddz-ai) 以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai
 
 
@@ -111,7 +112,8 @@
 - Machine Learning (https://yadi.sk/i/03Jg9WMzgD2YlA) - Tom M. Mitchell
 - Pattern Recognition and Machine Learning (https://yadi.sk/i/8ffTCaMH0bM8uQ) - Christopher Bishop
 - The Elements of Statistical Learning (https://yadi.sk/i/hfatiRyBCwfcWw) - Trevor Hastie
-- Machine Learning for OpenCV (https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler (Source code here (https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
+- Machine Learning for OpenCV (https://yadi.sk/i/_UdlHqwuR-Wdxg) - Michael Beyeler (Source code here 
+(https://github.com/zslucky/awesome-AI-books/tree/master/resources/Machine%20Learning%20for%20OpenCV))
 - 机器学习 (https://yadi.sk/i/vfoPTRRfgtEQKA) - 周志华
 - 机器学习 (原书Machine Learning) (https://yadi.sk/i/jTNv4kzG-lmlYQ) - Tom M. Mitchell
 - 统计学习方法 (https://yadi.sk/i/R08dbDMOJb3KKw) - 李航
@@ -135,11 +137,12 @@
 - COMMERCIAL Conversational AI (https://www.manning.com/books/conversational-ai) - Andrew R. Freed
 
 Philosophy
-- COMMERCIAL Human Compatible: Artificial Intelligence and the Problem of Control (https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart Russell
+- COMMERCIAL Human Compatible: Artificial Intelligence and the Problem of Control (https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) - Stuart 
+Russell
 - COMMERCIAL Life 3.0: Being Human in the Age of Artificial Intelligence (https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598) - Max Tegmark
 - COMMERCIAL Superintelligence: Paths, Dangers, Strategies 
-(https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=pd_sbs_14_t_0/146-0357100-6717505?_encoding=UTF8&pd_rd_i=0198739834&pd_rd_r=676ace91-552c-4865-a8d3-6273db5418bf&pd_rd_
-w=zYEu2&pd_rd_wg=hQdGQ&pf_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ) - Nick Bostrom
+(https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=pd_sbs_14_t_0/146-0357100-6717505?_encoding=UTF8&pd_rd_i=0198739834&pd_rd_r=676ace91-552c-4865-a8d3
+-6273db5418bf&pd_rd_w=zYEu2&pd_rd_wg=hQdGQ&pf_rd_p=5cfcfe89-300f-47d2-b1ad-a4e27203a02a&pf_rd_r=DTH77KT4FSVRMJ47GBVQ&psc=1&refRID=DTH77KT4FSVRMJ47GBVQ) - Nick Bostrom
 
 Quantum with AI
 
@@ -161,14 +164,14 @@
   - Stable Diffusion (https://github.com/CompVis/stable-diffusion) - Paper (https://arxiv.org/abs/2112.10752)  A latent text-to-image diffusion model
   - Stable Diffusion V2 (https://github.com/Stability-AI/stablediffusion) - High-Resolution Image Synthesis with Latent Diffusion Models
   - GFPGAN (https://github.com/TencentARC/GFPGAN) - Paper (https://arxiv.org/abs/2101.04061)  GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
-  - ESRGAN (https://github.com/xinntao/ESRGAN) - Paper (https://arxiv.org/abs/2107.10833)  ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in 
-BasicSR.
+  - ESRGAN (https://github.com/xinntao/ESRGAN) - Paper (https://arxiv.org/abs/2107.10833)  ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The 
+training codes are in BasicSR.
   - CodeFormer (https://github.com/sczhou/CodeFormer) - Paper (https://arxiv.org/abs/2206.11253)  - NeurIPS 2022  Towards Robust Blind Face Restoration with Codebook Lookup Transformer
   - UniPC (https://github.com/wl-zhao/UniPC) - Paper (https://arxiv.org/abs/2302.04867)  UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
 - #### Reinforcement Learning
   - A3C (https://arxiv.org/pdf/1602.01783.pdf) - Google DeepMind Asynchronous Advantage Actor-Critic algorithm
-  - Q-Learning (http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf) SARSA DQN (https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) DDQN (https://arxiv.org/pdf/1509.06461.pdf) - Q-Learning is a 
-value-based Reinforcement Learning algorithm
+  - Q-Learning (http://www.gatsby.ucl.ac.uk/~dayan/papers/cjch.pdf) SARSA DQN (https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) DDQN 
+(https://arxiv.org/pdf/1509.06461.pdf) - Q-Learning is a value-based Reinforcement Learning algorithm
   - DDPG (https://arxiv.org/pdf/1509.02971.pdf) - Deep Deterministic Policy Gradient,
   - Large-Scale Curiosity (https://arxiv.org/pdf/1808.04355.pdf) - Large-Scale Study of Curiosity-Driven Learning
   - PPO (https://arxiv.org/pdf/1707.06347.pdf) - OpenAI Proximal Policy Optimization Algorithms
@@ -187,9 +190,10 @@
   - LightGBM (https://lightgbm.readthedocs.io/en/latest/Features.html#) (Python, R, CLI) - Microsoft lightGBM lib's features document.
   - CatBoost (https://arxiv.org/pdf/1706.09516.pdf) (Python, R, CLI) - Yandex Catboost lib's key algorithm pdf papper.
   - StackNet (https://github.com/kaz-Anova/StackNet) (Java, CLI) - Some model stacking algorithms implemented in this lib.
-  - RGF (https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using Regularized Greedy Forest (multi-core implementation FastRGF (https://github.com/RGF-team/rgf/tree/master/FastRGF))
-  - FM (https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), FastFM (https://arxiv.org/pdf/1505.00641.pdf), FFM (https://arxiv.org/pdf/1701.04099.pdf), XDeepFM (https://arxiv.org/pdf/1803.05170.pdf) - 
-Factorization Machines and some extended Algorithms
+  - RGF (https://arxiv.org/pdf/1109.0887.pdf) - Learning Nonlinear Functions Using Regularized Greedy Forest (multi-core implementation FastRGF 
+(https://github.com/RGF-team/rgf/tree/master/FastRGF))
+  - FM (https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf), FastFM (https://arxiv.org/pdf/1505.00641.pdf), FFM (https://arxiv.org/pdf/1701.04099.pdf), XDeepFM 
+(https://arxiv.org/pdf/1803.05170.pdf) - Factorization Machines and some extended Algorithms
 - #### Deep Learning
   - GNN Papers (https://github.com/thunlp/GNNPapers) - Must-read papers on graph neural networks (GNN)
   - EfficientNet (https://arxiv.org/pdf/1905.11946.pdf) - Rethinking Model Scaling for Convolutional Neural Networks
@@ -200,7 +204,8 @@
   - GPT-3 (https://arxiv.org/pdf/2005.14165.pdf) - Language Models are Few-Shot Learners
 - #### CV
   - Fast R-CNN (https://arxiv.org/pdf/1504.08083.pdf) - Fast Region-based Convolutional Network method (Fast R-CNN) for object detection
-  - Mask R-CNN (https://arxiv.org/pdf/1703.06870.pdf) - Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
+  - Mask R-CNN (https://arxiv.org/pdf/1703.06870.pdf) - Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box
+recognition.
   - GQN (http://science.sciencemag.org/content/360/6394/1204/tab-pdf) - DeepMind Generative Query Network, Neural scene representation and rendering
 - #### Meta Learning
   - MAML (https://arxiv.org/pdf/1703.03400.pdf) - Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
@@ -222,16 +227,16 @@
   - LDA (https://www.isip.piconepress.com/publications/reports/1998/isip/lda/lda_theory.pdf) (Linear) - Linear Discriminant Analysis
   - LLE (https://cs.nyu.edu/~roweis/lle/papers/lleintro.pdf) (Non-linear) - Locally linear embedding
   - Laplacian Eigenmaps (http://web.cse.ohio-state.edu/~belkin.8/papers/LEM_NC_03.pdf) - Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
-  - Sammon Mapping (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding inter-point distances
-in the
+  - Sammon Mapping (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0910/henderson.pdf) (Non-linear) - Sammon mapping is designed to minimise the differences between corresponding 
+inter-point distances in the
 two spaces
 - #### Data Processing
   - Pandas (https://github.com/pandas-dev/pandas) (Python) - Flexible and powerful data analysis / manipulation library for Python.
   - Polars (https://github.com/pola-rs/polars) (Rust, Python) - Lightning-fast DataFrame library for Rust and Python.
 
 Distributed training
-- Horovod (https://github.com/horovod/horovod#usage) - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and 
-easy to use.
+- Horovod (https://github.com/horovod/horovod#usage) - Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep 
+Learning fast and easy to use.
 - Acme (https://github.com/deepmind/acme) - A Research Framework for (Distributed) Reinforcement Learning. 
 - bagua (https://github.com/BaguaSys/bagua) - Bagua is a flexible and performant distributed training algorithm development framework.